Features and Amenities
Features and Amenities:
Wifi ready study area
Gym and Function Room
Features and Amenities:
2 Lap Pools
Ground Floor Commercial Areas
Features and Amenities:
3 Swimming Pools
Gym and Fitness Center
Outdoor Basketball Court
Contact us today for a no obligation quotation:
Copyright © 2018 SMDC :: SM Residences, All Rights Reserved.
A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Test Code : A2010-578
Test Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor Name : IBM
: 120 Real Questions
A2010-578 Questions and answers required to pass the certification examination at the start attempt.
I passed the A2010-578 exam. It was the first time I used killexams.com for my preparation, so I didnt know what to expect. So, I got a pleasant surprise as killexams.com has stunned me and totally passed my expectations. The exam simulator/practice tests work great, and the questions are valid. By valid I mean that they are REAL exam questions, and I got many of them on my actual exam. Very reliable, and I was left with great impressions. I would not hesitate to recommend killexams.com to my colleagues.
Awesome Source! I got Actual test questions of A2010-578 exam.
I dont sense by myself within the direction of exams anymore because i have a exceptional test associate in the form of this killexams. Not only that however I moreover have instructors who are geared up to manual me at any time of the day. This same steering became given to me throughout my test and it didnt remember whether or not it changed into day or night time, all my questions have been responded. I am very grateful to the academics here for being so excellent and best and supporting me in clearing my very difficult exam with A2010-578 have a test material and A2010-578 test and yes even A2010-578 exam simulator is top class.
am i able to find dumps Q & A modern A2010-578 examination?
As a assured authority, I knew I want to take assistance from Dumps at the off hazard that I need to clear the acute exam like A2010-578. Furthermore i was correct. The killexams.com Dumps have an interesting technique to make the difficult topics smooth. They manage them in short, simple and true manner. Clear-cut and take into account them. I did so and could answer all of the questions in half of time. High-quality, killexams.com dumpss a authentic partner in want.
A2010-578 take a look at prep a ways clean with those dumps.
well, I did it and that i cannot consider it. I should in no way have passed the A2010-578 with out your assist. My score turned into so high i was surprised at my overall performance. Its just due to you. thanks very a lot!!!
Dont forget about to strive those real exam questions questions for A2010-578 examination.
I took this exam remaining month and passed it thanks to my instruction with the killexams.com kit. this is a outstanding exam dump, greater reliable than I could anticipate. All questions are legitimate, and it is usually masses of practise information. higher and extra dependable than I expected - I passed with over 97%, thats the satisfactory A2010-578 exam score. I dont know why so few IT people understand approximately killexams.com, or maybe its simply my conservative environment anyways, I may be spreading the word among my buddies for the reason that this is outstanding and can be beneficial to many.
forget about everything! simply forcus on those A2010-578 questions.
that is a gift from killexams.com for all of the candidates to get cutting-edge observe material for A2010-578 exam. all themembers of killexams.com are doing a tremendous process and ensuring fulfillment of applicants in A2010-578 checks. I handed the A2010-578 exam just due to the fact I used killexams.com material.
look at books for A2010-578 expertise but make certain your fulfillment with those .
The fine IT exam prep i have ever come upon. definitely my A2010-578 exam is in some days, however I feel so equipped and reassured, particularly now that i have read all of the tips and tricks here. The exam simulator seems to be very helpful, its clean to consider questions and answers, plus, in case you keep going via them time after time, you startseeing a larger picture and understand the principles higher. to date, i have had outstanding revel in with Killexams!
Unbelieveable! but authentic source modern-day A2010-578 real test questions.
I take the advantage of the Dumps provided by the killexams.com and the content rich with information and offers the effective things, which I searched exactly for my preparation. It boosted my spirit and provides needed confidence to take my A2010-578 exam. The material you provided is so close to the real exam questions. As a non native English speaker I got 120 minutes to finish the exam, but I just took 95 minutes. Great material. Thank you.
Nice to hear that Latest dumps of A2010-578 exam are available.
Just passed the A2010-578 exam manner to Killexams. The questions are all correct and actual. This schooling % may be very robust and reliable, actually passed my expectancies. I have already shared my perspectives with colleagues who passed the A2010-578 exam,. So if you are looking for dependable brain dumps for any exam, this is a incredible choice. At least A2010-578 exam is without a doubt dependable
Dont forget to try these dumps questions for A2010-578 exam.
I wound up the exam with a satisfying 84% marks in stipulated time. thank you very plenty killexams. via and by, it become tough to do top to bottom test intending with a full-time work. At that factor, I became to the of killexams. Its concise answers helped me to see a few complex topics. I selected to take a seat for the exam A2010-578 to reap in addition advancement in my career.
Harrisburg, NC -- (SBWIRE) -- 01/23/2019 -- world connected HEALTHCARE MARKET size, fame AND FORECAST 2019-2025
The record provides a helpful source of insightful facts for enterprise strategists and aggressive evaluation of connected Healthcare Market. It provides the linked Healthcare industry overview with increase evaluation and futuristic cost, salary and many other features. The research analysts give an complicated description of the price chain and its distributor evaluation. This linked Healthcare look at gives complete information which reinforces the figuring out, scope and utility of this document.
in keeping with the report, global related healthcare market turned into valued at about USD 1,860.26 million in 2017 and is anticipated to generate income of round USD 10,798.45 million with the aid of end of 2025, growing at a CAGR of round 27.forty seven% between 2018 and 2025.
The file presents the market aggressive landscape and a corresponding designated evaluation of the primary dealer/key avid gamers out there. accurate organizations within the world linked Healthcare Market: Accenture, IBM, SAP, GE Healthcare, Oracle, Microsoft, Airstrip expertise, Medtronic, Allscripts, Boston Scientific, Athenahealth, Cerner, Philips, Agamatrix, Qualcomm, AliveCor and others.
click the hyperlink to get a free sample copy of the report:https://www.marketinsightsreports.com/experiences/01181057103/international-related-healthcare-market-dimension-status-and-forecast-2019-2025/inquiry?supply=releasewire&Mode=34
world linked HEALTHCARE MARKET split via PRODUCT class AND functions:
This report segments the world linked Healthcare market on the foundation of types are:TelemedicineHome MonitoringAssisted LivingClinical Monitoring
On the basis of software, the international related Healthcare market is segmented into:analysis and TreatmentMonitoring ApplicationsEducation and AwarenessWellness and PreventionHealthcare ManagementOthers
REGIONAL analysis FOR connected HEALTHCARE MARKET:
For complete knowing of market dynamics, the world related Healthcare market is analyzed across key geographies namely: united states, China, Europe, Japan, South-east Asia, India and others. each and every of those areas is analyzed on groundwork of market findings throughout main countries in these regions for a macro-stage realizing of the market.
have an effect on OF THE connected HEALTHCARE MARKET file:-complete assessment of all opportunities and chance in the linked Healthcare market.- connected Healthcare market fresh improvements and major routine.-precise study of business strategies for increase of the linked Healthcare market-leading players.-Conclusive analyze about the increase plot of related Healthcare market for coming near near years.-In-depth knowing of related Healthcare market-specific drivers, constraints and essential micro markets.-favourable impact interior essential technological and market latest trends awesome the connected Healthcare market.
The record has 150 tables and figures browse the report description and TOC:https://www.marketinsightsreports.com/stories/01181057103/world-linked-healthcare-market-measurement-status-and-forecast-2019-2025?source=releasewire&Mode=34
WHAT ARE THE MARKET components which are defined within the file?
-Key Strategic tendencies: The analyze also contains the key strategic tendencies of the market, comprising R&D, new product launch, M&A, agreements, collaborations, partnerships, joint ventures, and regional growth of the main opponents operating available in the market on a global and regional scale.
-Key Market points: The record evaluated key market elements, including revenue, fee, skill, capacity utilization fee, gross, creation, construction rate, consumption, import/export, provide/demand, can charge, market share, CAGR, and gross margin. furthermore, the look at presents a comprehensive analyze of the important thing market dynamics and their latest developments, along with pertinent market segments and sub-segments.
-Analytical equipment: The world connected Healthcare Market record comprises the precisely studied and assessed facts of the key trade avid gamers and their scope out there via ability of a number of analytical equipment. The analytical equipment reminiscent of Porter's five forces analysis, SWOT analysis, feasibility study, and funding return analysis had been used to investigate the increase of the important thing gamers operating out there.
Customization of the file: This file can also be custom-made as per your needs for additional data as much as 3 groups or international locations or 40 analyst hours.Please connect with their income group ([email protected]).
ABOUT US:MarketInsightsReports provides syndicated market analysis on industry verticals including Healthcare, suggestions and communique know-how (ICT), technology and Media, chemical compounds, materials, power, Heavy industry, etc. MarketInsightsReports provides global and regional market intelligence coverage, a 360-diploma market view which includes statistical forecasts, aggressive landscape, exact segmentation, key developments, and strategic techniques.
CONTACT US: Irfan Tamboli (Head of revenue) - Market Insights ReportsPhone: + 1704 266 3234 | +91-750-707-8687[email protected] | [email protected]
For more tips on this press release talk over with: http://www.sbwire.com/press-releases/linked-healthcare-market-2019-highlights-and-fundamentals-accenture-ibm-sap-ge-healthcare-oracle-microsoft-airstrip-expertise-medtronic-1129108.htm
Irfan TamboliSales HeadMarket Insights ReportsTelephone: 1-704-266-3234Email: click to email Irfan TamboliWeb: https://www.marketinsightsreports.com/
Feb 07, 2019 (Heraldkeeper by way of COMTEX) -- manhattan, February 07, 2019: The international software administration services Market is expected to exceed more than US$ 32.5 Billion via 2024 at a CAGR of 21% within the given forecast duration.The scope of the report includes an in depth analyze of international and regional markets on software administration functions Market with the reasons given for variations within the growth of the business in certain areas.The report covers designated aggressive outlook including the market share and company profiles of the important thing members working in the global market. Key players profiled in the file consist of akin to Cognizant (US), Atos (France), Accenture (Republic of eire), Capgemini (France), Fujitsu (Japan),HCL (India), DXC (US), IBM (US), Tech Mahindra (India) and Wipro (India). company profile includes assign comparable to enterprise summary, financial summary, company strategy and planning, SWOT evaluation and current tendencies.you could Browse Full file: https://www.marketresearchengine.com/software-management-capabilities-market
A more associated commercial center has made overseeing enterprise greater at a loss for words. huge measures of guidance now attainable to the company are each a controversy to convey and an opportunity to searching for. software administration functions potential which assigns the administrations of massive business software administration contributed by means of diverse associations to corporations that should outsource their assignment software administration approaches. The associations that soak up the application administration carrying out have their IT abilities and have the mastery of comparative application administration for distinctive companies working in a similar area of business.
The restraining components of world application administration features Market are as follows:
Imbalance or excessive prices within the application security budget will offset the IT utility budgetApplication management is a time vicious processOrganizations are mainly worried of software statistics securityLot of complications in operational and Architectural implementationThe main riding elements of global application administration capabilities Market are as follows:
predominant part of the software management approach is cloud computingProliferation of cellular Apps Demand amazing cellular App management features and Emergence of Byod.Unexplored possibilities can be paved by means of open sourced technologyPresence of gigantic number of common functions which offer massive profit opportunitiesTime-To-Market is accelerated because of increasing want for enterprise AgilityThe global utility management services Market has been segmented as below:
The global software administration functions Market is Segmented on the traces of corporation size analysis, provider analysis, Vertical analysis and Regional evaluation. by firm measurement analysis this market is segmented on the foundation of Small and Medium-Sized enterprises and big organizations. through service analysis this market is segmented on the basis of application safety, application Integration, software Portfolio evaluation, net utility safety, cellular software protection, application Modernization, Cloud utility Migration, utility Replat forming, UI Modernization, utility Managed features and software upkeep and help.
via Vertical analysis this market is segmented on the groundwork of executive, Retail and eCommerce, Banking, financial functions, and coverage (BFSI), Telecom and IT, Manufacturing, Healthcare and Lifesciences, power and Utilities and Others (go back and forth and Hospitality, education, and Transport and Logistics, Media and leisure). by Regional analysis this market is segmented on the groundwork of North the us, Europe, Asia-Pacific and relaxation of the world.
This record gives:
1) a top level view of the global marketplace for application management features Market and related applied sciences.2) Analyses of international market trends, with facts from 2015, estimates for 2016 and 2017, and projections of compound annual boom rates (CAGRs) via 2024.3) Identifications of recent market opportunities and centered promotional plans for utility administration services Market.4) discussion of research and construction, and the demand for brand spanking new items and new applications.5) comprehensive enterprise profiles of main avid gamers in the business.
Request sample file from here:https://www.marketresearchengine.com/utility-administration-capabilities-market
desk of Contents:
1 Introduction2 Market analysis tactics
3 Market abstract
4 pleasant Market Insights
5 software administration features Market Overview
6 Regulatory Market Synopsis7 software management features Market, with the aid of Service8 application management capabilities Market, by means of company Size9 utility administration features Market, through Vertical
10 application administration functions Market, by way of Geographic Region11 aggressive Landscape12 enterprise Profiles(enterprise Overview, Product Portfolio, fiscal Overview, Key Devolopements)*12.1 Accenture12.2 ATOS12.three Capgemini12.four Cognizant12.5 Fujitsu12.6 DXC12.7 HCL12.8 IBM12.9 Tech Mahindra12.10 Wipro
different connected Market analysis studies:
Case management Market is meant to reach US$ 7.0 Billion by way of 2023
supplier possibility management Market is expected to Get US$ 6 Billion by using 2023
enterprise identify: Market analysis Engine
Contact adult: John Bay
country: united states
web site: https://www.marketresearchengine.com/
While it is hard errand to pick solid certification questions/answers assets regarding review, reputation and validity since individuals get sham because of picking incorrectly benefit. Killexams.com ensure to serve its customers best to its assets as for exam dumps update and validity. The greater part of other's sham report objection customers come to us for the brain dumps and pass their exams cheerfully and effortlessly. They never bargain on their review, reputation and quality because killexams review, killexams reputation and killexams customer certainty is imperative to us. Extraordinarily they deal with killexams.com review, killexams.com reputation, killexams.com sham report grievance, killexams.com trust, killexams.com validity, killexams.com report and killexams.com scam. On the off chance that you see any false report posted by their rivals with the name killexams sham report grievance web, killexams.com sham report, killexams.com scam, killexams.com protestation or something like this, simply remember there are constantly terrible individuals harming reputation of good administrations because of their advantages. There are a great many fulfilled clients that pass their exams utilizing killexams.com brain dumps, killexams PDF questions, killexams questions, killexams exam simulator. Visit Killexams.com, their example questions and test brain dumps, their exam simulator and you will realize that killexams.com is the best brain dumps site.
M2180-651 free pdf | P4070-005 practice test | C9020-667 test prep | 000-751 Practice Test | MB2-185 real questions | 250-513 Practice test | A2010-651 questions and answers | 190-622 real questions | HAT-050 braindumps | 000-639 cram | Property-and-Casualty mock exam | 646-580 free pdf | CQE dumps | E20-542 brain dumps | 77-604 braindumps | 000-082 dumps questions | LOT-405 braindumps | 920-551 bootcamp | 70-773 VCE | 4A0-105 practice exam |
Never miss these A2010-578 questions you go for test.
killexams.com furnish latest and refreshed Practice Test with Actual Exam Questions and Answers for new syllabus of IBM A2010-578 Exam. Practice their Real Questions and Answers to Improve your insight and pass your exam with High Marks. They guarantee your achievement in the Test Center, covering each one of the references of exam and build your Knowledge of the A2010-578 exam. Pass past any uncertainty with their braindumps.
Are you looking for Pass4sure IBM A2010-578 Dumps containing real exams questions and answers for the Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma Exam prep? They provide most updated and quality source of A2010-578 Dumps that is http://killexams.com/pass4sure/exam-detail/A2010-578. They have compiled a database of A2010-578 Dumps questions from actual exams in order to let you prepare and pass A2010-578 exam on the first attempt.
killexams.com Huge Discount Coupons and Promo Codes are as under;
WC2017 : 60% Discount Coupon for all exams on website
PROF17 : 10% Discount Coupon for Orders greater than $69
DEAL17 : 15% Discount Coupon for Orders greater than $99
DECSPECIAL : 10% Special Discount Coupon for All Orders
Quality and Value for the A2010-578 Exam: killexams.com Practice Exams for IBM A2010-578 are made to the most quickened standards of particular exactness, making utilization of simply certified professionals and dispensed makers for development.
100% Guarantee to Pass Your A2010-578 Exam: If you don't pass the IBM A2010-578 exam using their killexams.com exam simulator and PDF, they will give you a FULL REFUND of your purchasing charge.
Download-able, Interactive A2010-578 Testing Software: Their IBM A2010-578 Preparation Material offers you which you should take IBM A2010-578 exam. Unpretentious components are appeared into and made through IBM Certification Experts normally using industry delight in to supply particular, and true blue.
- Comprehensive questions and answers about A2010-578 exam - A2010-578 exam questions joined by displays - Verified Answers by Experts and very nearly 100% right - A2010-578 exam questions updated on general premise - A2010-578 exam planning is in various decision questions (MCQs). - Tested by different circumstances previously distributing - Try free A2010-578 exam demo before you choose to get it in killexams.com
killexams.com Huge Discount Coupons and Promo Codes are as below;
WC2017: 60% Discount Coupon for all tests on web site
PROF17: 10% Discount Coupon for Orders more than $69
DEAL17: 15% Discount Coupon for Orders more than $99
DECSPECIAL: 10% Special Discount Coupon for All Orders
A2010-578 | A2010-578 | A2010-578 | A2010-578 | A2010-578 | A2010-578
Killexams 000-815 test questions | Killexams 70-121 braindumps | Killexams ST0-174 cheat sheets | Killexams HP2-T21 Practice test | Killexams 70-505-CSharp real questions | Killexams C4040-109 practice questions | Killexams M2140-649 real questions | Killexams 000-006 bootcamp | Killexams I10-001 braindumps | Killexams CSSLP brain dumps | Killexams BH0-008 cram | Killexams GB0-190 Practice Test | Killexams 202-400 mock exam | Killexams E22-106 free pdf | Killexams SK0-003 exam prep | Killexams HP0-381 questions and answers | Killexams MOPF study guide | Killexams HP0-J51 VCE | Killexams 1Z0-040 questions and answers | Killexams 310-016 free pdf |
Killexams HP0-D12 practice questions | Killexams 9A0-046 braindumps | Killexams 000-992 study guide | Killexams NS0-155 Practice Test | Killexams 000-M34 practice test | Killexams CHHE cram | Killexams 050-684 practice questions | Killexams C90-01A study guide | Killexams IBMSPSSMBPDM sample test | Killexams PEGACMBB mock exam | Killexams VCS-277 questions and answers | Killexams 1Z0-595 Practice test | Killexams ST0-067 dump | Killexams SC0-502 dumps | Killexams 1Y0-230 exam prep | Killexams 351-001 braindumps | Killexams C2010-653 free pdf download | Killexams VCP550 exam prep | Killexams 310-813 questions and answers | Killexams 090-602 test prep |
Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, they represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. They built road networks for 40 of the urban areas defined by the U.S. Census Bureau. They developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.INTRODUCTION
Existing roadway design standards emphasize the efficient movement of vehicles through a transportation network (1–4). Efficiency in this context may include identification of the shortest or fastest route (1, 5–7), or the route that minimizes congestion (8). It is the primary criterion on which road networks are modeled and design alternatives are considered (6, 7, 9, 10). The Texas A&M Transportation Institute defines and reports traffic delay in urban areas as the annual delay per auto commuter (11). Other studies define efficiency as delay for the individual driver in terms of time spent moving or stopped (7), or mean travel time between all origin-destination pairs in the network (9). However, as the experience of any motorist in large American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, bad weather, construction, and special events (for example, a marathon race), can cause costly delays and frustrating inefficiencies that result in fuel waste, infrastructure deterioration, and increased pollution (12, 13). Evaluating road networks based only on efficiency under normal operating conditions results in little to no information about how the system performs under suboptimal or disrupted conditions.
Infrastructure systems that exhibit adaptive response to stress are typically characterized as resilient (14–21). Given the essential role of transportation in emergency response, provision of essential services, and economic well-being, the resilience of roadway networks has received increasing policy attention. Nonetheless, scholars have yet to converge on a shared understanding of resilience suitable to guide design, operation, and reconstruction of roadway networks. Although resilience in infrastructure systems is characterized as a multidimensional concept (22, 23), in many engineering and civil infrastructure implementations, resilience is defined as the ability of a system to prepare for, absorb, recover from, and adapt to disturbances (16). Specific to transportation, resilience has been defined as “the ability of the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (24). Others describe transportation resilience as simply the ability of a system to minimize operational loss (25) or use the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).
Current efforts in transportation resilience research have focused on framework development and quantification methods. These efforts include the specification of resilience indicators, such as total traffic delay (24), economic loss (29), post-disaster maximum flow (30), and autonomous system components (31). Practical concerns with this type of resilience evaluation are that it relies on uncertain performance data and often omits indicators that are unquantifiable (19). Other resilience approaches apply traffic network modeling to identify locations for critical buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12). One drawback of existing network resilience methods is that they are data-intensive, often requiring limited information about resources for unusual road system repair (26, 28) or network behavior following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a range of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must be characterized and evaluated by the capacity to adapt to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to be implemented for transportation networks.
Here, they study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. They develop an urban roadway efficiency model, calibrate it on the basis of the observed data (11) of annual delay per peak-period auto commuter, and apply the model to calculate efficiency in 40 cities. Then, they model traffic response to random roadway disruptions and recalculate expected delays to determine the sensitivity of each city to loss of roadway linkages. The results may reveal important considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.METHODS
The Methods section appears here to help clarify the subsequent sections. To develop the urban roadway efficiency model, they defined the urban area boundaries, constructed the road networks, and evaluated the population density within cities using the Census Bureau data sets (35, 36) and OpenStreetMap (OSM) data sets (37). They relied on these data to assess commuter patterns, which they used to measure efficiency and resilience of road networks.
Alternative approaches to transportation have been offered and include those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the use of historical data to predict traffic. They review these approaches in the Supplementary Materials and note that the main benefit of their model is that it relies solely on readily available public data, rather than on particular data sets that may or may not be practical to obtain for any particular region. The model’s algorithmic simplicity allows us to consider spatial topologies of cities in high resolution including tens of thousands of nodes and links. They did not create a more accurate transportation model than the existing ones, but they were able to obtain measurable characteristics of transportation systems (average delays) using their model.Geospatial boundaries and population density
To define geospatial boundaries for the transportation infrastructure networks, they used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). They approximated the exact urban area polygon with a simplified manually drawn one (Fig. 1A) and included all roadways within 40 km (25 miles) of it in the network. For each of the links, they calculated its length on the basis of the polyline defining the link and assigned a number of lanes m and the FFSs (see the Supplementary Materials).Fig. 1 Definition of urban areas and assignment of nodes’ population.
(A) Boston, MA-NH-RI urban area as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, they approximate each of the urban areas shapefiles with a coarse manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.
We next estimated population in vicinity of each intersection i using the Census Tract data (36). To this end, they split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni as
Above, Nt is the population of Census Tract t, and Pi and Pt are the polygons of the cell and the tract, respectively (Fig. 1B and table S2).Transportation model
We built on the gravity model to generate commuting patterns. The gravity model (47) is a classical model for trip distribution assignment and is extensively adopted in most metropolitan planning and statewide travel demand models in the United States (48–51). Other trip distribution models include, for example, destination choice models (52, 53). However, these models are not as widely used in large scale, because the detailed data required by these models are frequently unavailable (48).
We assumed that (i) the flow of commuters from origin region o to destination region d is proportional to the population at the destination Nd and that (ii) the flow of commuters depends on the distance xod between the origin and destination and is given by a distance factor, P(xod). Using these assumptions, they assessed the fraction of individuals commuting from region o to destination region d, fod, as
Then, the commuter flow from origin region o to destination region d is
Although individual driving habits may vary (54), they assumed that all drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to calculate commute paths for every origin-destination pair using inferred FFSs. To calculate commuter flows between all pairs of intersections, they estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in place of the distance factor P(xod), they used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which they approximated with the exponential function (Fig. 2A and table S3).Fig. 2 Model details.
(A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of speed on density for V = 100 km/hour.
Next, they defined the commuter load on each road segment as
(4)where θod(ij) is a binary variable equal to 0 when the link ij is not on the shortest path connecting nodes o and d, and 1 otherwise. Note that in Eq. 4, they only considered origins that were not farther than 30 km from the urban area boundary polygon. The nodes farther than 30 km from the boundary were only used as destinations to evaluate the fraction of commuters not going toward the urban area (Eq. 2).
Because most commuters travel during peak periods, commuter loads Lij can be regarded as traffic-based centrality measures estimating the number of individuals using corresponding road segments. Then, the cumulative time lost by all commuters is
(5)where Vij and vij are, respectively, the FFS and the actual traffic speed along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient same for all urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the boundary polygon. A similar equation was obtained for the moving delay in the study of Jiang and Adeli (45), where the authors looked at the delay induced from road repairs.
The actual traffic speed vij depends on many factors including the speed limit, the number of drivers on the road, and road conditions. Although there exist a number of approaches to estimate actual traffic speed (57, 58), they chose to use the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary Materials
(6)where vmin is the minimum speed in the traffic, vveh is the correction for the finite size of the car, and α is the proportionality coefficient (Fig. 2B). Efficiency and resilience metrics
We measured efficiency as the average annual delay per peak-period auto commuter. In practice, lower delay means higher efficiency. There are multiple ways to map from delays to efficiency, such as taking the inverse values of delays, taking negative values of delays, etc. To avoid ambiguity and facilitate the interpretation of results, they used the delays themselves to quantify the transportation efficiency of urban areas.
We operationalized resilience through the change in traffic delays relative to stress, which is modeled as loss or impairment of roadway linkages. Looking at resilience from the network science perspective, they focused on topological features of cities, rather than on recovery resources available. Sterbenz et al. (60) evaluated a network’s resilience as a range of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational state toward improvement. They are studying how availability of alternate routes helps remediate the consequences of the initial disruption to the network. In the traffic context, the immediate impact of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing speed limits on affected roads will not vary much from network to network, although the number and type of these disruptions will. Likewise, the speed of restoring full functionality (through action in the physical domain) is not so much dependent on the road network as it is on the nature of the disruption (snow versus earthquake versus flood) and the resources that the city allocates to such repair. The level of functionality that these repairs achieve ought to be the full predisruption functionality, that is, eventually all roads can be fully cleared or restored. However, the immediate loss of function for a given traffic flow can very quickly be partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the new steady state at that level of functionality, full functionality is gradually restored. Thus, their model proxies for resilience and is calibrated against the data that proxy for efficiency. At the same time, they note that to fully capture resilience characteristics of a transportation system, it is required to analyze recovery resources available and the effectiveness of coordination between the relevant authorities. Lower additional delay corresponds to higher resilience, but using the same reasoning that they had for efficiency, they quantified resilience through additional delays.RESULTS Efficiency
Together, their traffic model has three parameters (proportionality coefficient α, minimum speed vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can estimate the total delay incurred by all commuters in any given suburban area or, equivalently, the average delay per commuter. They take vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the real data on the annual average delay per peak-period auto commuter provided by the Urban Mobility Scorecard (11).
We divide the 40 urban areas into two equally sized groups for model calibration and validation, respectively. They have found that for the 20 urban areas used for calibration, the R-squared coefficient took values in the range (−0.01 to 0.83) (Fig. 3 and Supplementary Materials). This allows us to set model parameters α and β (see Methods) as follows: α = 4.30 × 104 hour−1 and β = 10.59. These values correspond to the Pearson coefficient of 0.91 (P = 2.17 × 10−8).Fig. 3 Modeled and observed delays in 40 urban areas.
Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).
To validate the model, they estimate travel delays in 20 different urban areas. As seen from Fig. 3, the estimated travel delays are significantly correlated (R = 0.63, P = 3.00 × 10−3) with actual delay times (11), validating the transportation model. Figure 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, average traffic patterns at 8 a.m. provided by Google Maps are in Fig. 4 (A and D). Modeled conditions are given for comparison in Fig. 4 (B and E). Finally, Fig. 4 (C and F) shows the new, modeled traffic patterns that result from redistribution of travel in response to a disruption of 5% of the links.Fig. 4 Traffic distributions.
Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.Resilience
Our approach to model stress is inspired by percolation theory. For every independent simulation of stress, they select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. They collect statistics for 20 realizations of the percolation. On failed segments, free-flow speeds (FFSs) are reduced to 1 km/hour (representing near-total loss), and loads L and traffic delays are then recalculated using the updated FFSs. Low-stress scenarios (r < 0.1) might be caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or severe flooding that makes many roadways nearly impassable. Finally, widespread stress might be caused by snow, ice, or dust storms that affect nearly the entire roadway system. Figure 5 displays the analysis of delay times in six representative urban areas for the full spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for all urban areas. Some routes within a single urban area experience longer delays than others. The inset of Fig. 5 shows the delay distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic delay times grow rapidly as r increases and reach saturation (all routes moving at 1 km/hour) as r approaches 1. They determine the most resilient urban transportation network to be Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to be Washington, DC.Fig. 5 Dependency of the additional delay on the severity of the links disruption for six representative urban areas.
Error bars show mean values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a small number of discrete roadways, and this produces an anomalous impact at 5 to 15% disruption.
Figure 6 shows both the efficiency (in blue) and resilience response (additional delays due to 5% link disruption, in orange) for the 40 urban areas modeled. Some cities with high efficiency under normal operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a sharp increase in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL all fall into this category of urban areas in which traffic operates well under ordinary circumstances but rapidly become snarled under mild stress. On the other hand, Los Angeles is notorious for traffic delays under all conditions—yet minor stress levels result in little degradation of efficiency. By contrast, normal traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in large increases in additional delays. These examples indicate that resilience (that is, additional delay response to stress) is independent of normal operating efficiency.Fig. 6 Comparison of resilience and efficiency metrics.
Annual impact of 5% disruption (additional delay) has a low correlation with normal annual delay per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.DISCUSSION
The disturbances affecting the road infrastructure are often complex, and their impact on the structure and function of roadway systems may be unknown (28, 31). These disturbances might be natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might also be anthropogenic and intentional, such as a street fair or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to be made that may have important implications for highway transportation policy. The first is that resilience and efficiency represent different aspects related to the nature of transportation systems; they are not correlated and should be considered jointly as complementary characteristics of roadway networks.
Second, there are characteristic differences in the resilience of different urban areas, and these differences are persistent at mild, medium, or widespread levels of stress (Fig. 5). Except for San Francisco, CA, which is the most fragile of all cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban area resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the same cities that exhibit low levels of resilience (relative to peers) under widespread roadway impairment. This suggests that the characteristics that impart resilience (such as availability or alternate routes through redundancy of links) are protective against both the intermittent outages caused by occasional car crashes and those caused by snow and ice storms. For cities without resilience, a widespread hazard such as snow may lead to a cascade of conditions (for example, crashes) that rapidly deteriorate into gridlock. This was exactly the case for Washington, DC 20 January 2016 under only 2.5 × 10−2 m or 2.5 cm of snow (61), and for Atlanta, GA 2 years earlier, which experienced 5.1 × 10−2 m or 5.1 cm of snow in the middle of the day that resulted in traffic jams that took days to disentangle (62). Whereas popular explanations of these traffic catastrophes focus on the failure of roadway managers to prepare plows and emergency response equipment, Fig. 5 suggests that cities with similar climates (Memphis, TN and Richmond, VA) are less likely to be affected, regardless of the availability of plow or sand trucks.
The third inference follows from Fig. 6, which suggests that urban areas that make capital investments to reduce traffic delays under normal operating conditions may nevertheless be vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, equipment malfunctions, or even deliberate attack, investment strategies that prioritize reduction of normal operating delays may have the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but possible conditions.
Finally, the exceptional position of New York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and have an important role to play in relieving traffic congestion. According to the Texas A&M Institute (63, 64), public transit reduces delays per peak-period auto commuter in the New York urban area by 63 hours, in Chicago by 23 hours, and by less than 20 hours in other urban areas. Because their model considers only roadway transit, and New York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that their model can provide informative results for the New York urban area.
Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to guide new investments in achieving resilience goals. It is widely understood that roadway infrastructure is expensive, both in acquiring land for rights-of-way and in construction of improvements, and thus, decisions regarding alignment, crossing, and access made over a period of decades may have long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under normal and random stress conditions. Investments motivated exclusively by expected efficiencies under normal operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, new analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of new investments to provide enhanced resilience. The adaptive network-based model described herein is one such approach.SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1
Alternative approaches to model transportation
Mapping from OSM Foundation shapefiles to network nodes and links
Population assignment algorithm
Distance factor of the likelihood of travel between nodes
Estimation of the traffic speed from the density of vehicles
Model calibration procedure
Sensitivity of the model to ramp speeds
Additional delay as a function of the severity of link disruption
table S1. Mapping original OSM types to network link types and assignment of the number of lanes.
table S2. The algorithm of the node population assignment.
table S3. Distance factor P(xod) of the likelihood of travel between nodes.
table S4. Model sensitivity to ramp speed coefficient.
fig. S1. Effects of the removal of nodes of degree 2.
fig. S2. Density-flow relationship in the Daganzo traffic model.
fig. S3. Model calibration.
fig. S4. Modeled delays for ramp speed coefficients of 1/3 and 1/2.
fig. S5. Dependency of the additional delay on the severity of the link disruption for all 40 urban areas.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.REFERENCES AND NOTES
K. Beverly, Efficient Use of Highway Capacity (FHWA-HOP-10-023, Texas Transportation Institute, 2010), p. 100.
T. Yamashita, K. Izumi, K. Kurumatani, Car navigation with route information sharing for improvement of traffic efficiency, in Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE, 2004), pp. 465–470.
K. Turnbull, Technical Activities Division, Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, Transportation Resilience: Adaptation to Climate Change (Transportation Research Board, 2016).
C. S. Holling, Engineering Resilience versus Ecological Resilience, in Engineering within Ecological Constraints, P. C. Schulze, Ed. (The National Academies Press, 1996), pp. 31–44.
S. E. Flynn, S. P. Burke, Critical Transportation Infrastructure and Societal Resilience (Center for National Policy, 2012).
T. P. Seager, S. Spierre Clark, D. A. Eisenberg, J. E. Thomas, M. M. Hinrichs, R. Kofron, C. N. Jensen, L. R. McBurnett, M. Snell, D. L. Alderson, Redesigning resilient infrastructure research, in Resilience and Risk, I. Linkov, J. M. Palma-Oliveira, Eds. (Springer, 2017).
D. Freckleton, K. Heaslip, W. Louisell, J. Collura, Evaluation of transportation network resiliency with consideration for disaster magnitude, paper presented at the 91st Annual Meeting of the Transportation Research Board, Washington, DC, 2012).
S. B. Pant, Transportation network resiliency: A study of self-annealing, thesis, Utah State University (2012).
D. King, A. Shalaby, Performance metrics and analysis of transit network resilience in Toronto, paper presented at the 95th Annual Meeting of the Transportation Research Board, Washington, DC, 10 to 14 January 2016.
D. Li, Resilience of spatial networks, in Complex Systems and Networks, J. Lü, X. Yu, G. Chen, W. Yu, Eds. (Springer Berlin Heidelberg, 2016), pp. 79–106.
P. M. Murray-Tuite, A Comparison of Transportation Network Resilience under Simulated System Optimum and User Equilibrium Conditions, in Proceedings of the Winter Simulation Conference WSC 06, 3 to 6 December 2006, pp. 1398–1405.
A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, J. Eriksson, VTrack: Accurate, energy-aware road traffic delay estimation using mobile phones, in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (ACM Press, 2009), p. 85.
E. Cho, S. A. Myers, J. Leskovec, Friendship and mobility: User movement in location-based social networks, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM Press, 2011), p. 1082.
D. Gross, J. F. Shortie, J. M. Thompson, C. M. Harris, Fundamentals of Queueing Theory (Wiley Series in Probability and Statistics, Wiley, Hoboken, NJ, ed. 4, 2008).
M. Sabyasachee, Y. Wang, X. Zhu, R. Moeckel, S. Mahapatra, Comparison between gravity and destination choice models for trip distribution in Maryland, paper presented at the TRB 92nd Annual Meeting of Compendium of Papers, 13 to 17 January 2013.
J. de Dios Ortúzar, L. G. Willumsen, Modelling Transport (John Wiley & Sons, ed. 4, 2011).
National Research Council (U.S.), Metropolitan Travel Forecasting: Current Practice and Future Direction (Transportation Research Board, 2007).
R. Van Haaren, Assessment of Electric Cars’ Range Requirements and Usage Patterns based on Driving Behavior recorded in the National Household Travel Survey of 2009 (Solar Journey, 2012), p. 25.
B. D. Greenshields, J. R. Bibbins, W. S. Channing, H. H. Miller, R. W. Crum, A study of traffic capacity, in Proceedings of the 14th Annual Meeting of the Highway Research Board, 6 to 7 December 1934, vol. 1.
Database consistencyFC SAN-attached CDP appliances maintain three levels of recoverable database images: crash consistent, transaction consistent and CDP consistency groups.
A crash-consistent image is the default-recoverable database image that all block-based CDP products provide. Should a database corruption occur, storage administrators may recover the database to any past point-in-time. However, full database recoveries still depend on the database admin to replay the database transaction logs to complete the recovery to a state where the restored image is usable by the database.
Transaction-consistent database recoveries minimize the database admin's involvement in recoveries. To create points in the CDP journal that are identifiable as recoverable transaction-consistent database images, CDP products offer application-specific host agents for databases. The CDP host agent monitors the database for periods when it enters a transaction-consistent state and then inserts a bookmark into the CDP journal. When performing recoveries, the CDP software identifies and displays these bookmarks so admins can restore images that are immediately accessible.
To create a consistency group, an admin selects and aggregates the virtual CDP LUNs that mirror the LUNs on which the production database resides. Each consistency group has its own journal that tracks data changes to any of the LUNs belonging to that consistency group. A critical factor is placing the CDP consistency group journal on back-end disk that matches or exceeds the performance of production LUNs.
Critical to Siemens' implementation was the creation and configuration of three CDP consistency groups to keep the database consistent across more than 200 RecoverPoint CDP LUNs. Siemens' Knoerer matched his 200 production database LUNs with virtual RecoverPoint LUNs residing on EMC Clariion CX700 disk.
However, Knoerer placed his consistency group journal on virtual CDP LUNs that were mapped back to the DMX-3 because he needed the higher performance of the DMX-3 storage to keep pace with the large number of changes in the Oracle database.
Future challengesHow to integrate with VMware is under discussion for most CDP vendors. While CDP host agents are supported on guest OSes, CDP host agents that operate at the hypervisor level on VMware and capture all traffic from all guest OSes are a work in progress.
The next frontier that CDP software needs to address, perhaps beginning in FC SAN-attached environments, is how to keep apps that run across multiple servers consistent. Eric Burgener, senior analyst and consultant at Hopkinton, MA-based Taneja Group, says there's a debate going on in the CDP community about the best way to create a consistent image across multiple servers.
FC SAN-attached CDP products will involve lengthier testing and configuration periods on a per-server basis. Admins will need to allocate new FC ports on FC SAN directors for the CDP appliance and new storage capacity to mirror the source server's production volumes; they'll also need to deploy new storage that matches the performance of the production database to keep the CDP journal.
In the end, reduced recovery time and backup software integration will be the deciding factors in product selection. FC SAN-attached CDP products offer almost immediate recoveries and allow applications to fail over and operate on virtual volumes presented by the CDP appliance with minimal or no application performance degradation. If simplified backup and recovery is your primary objective, backup products with integrated CDP promise to dramatically lower recovery time objectives and recovery point objectives.
3COM [8 Certification Exam(s) ]
AccessData [1 Certification Exam(s) ]
ACFE [1 Certification Exam(s) ]
ACI [3 Certification Exam(s) ]
Acme-Packet [1 Certification Exam(s) ]
ACSM [4 Certification Exam(s) ]
ACT [1 Certification Exam(s) ]
Admission-Tests [13 Certification Exam(s) ]
ADOBE [93 Certification Exam(s) ]
AFP [1 Certification Exam(s) ]
AICPA [2 Certification Exam(s) ]
AIIM [1 Certification Exam(s) ]
Alcatel-Lucent [13 Certification Exam(s) ]
Alfresco [1 Certification Exam(s) ]
Altiris [3 Certification Exam(s) ]
Amazon [2 Certification Exam(s) ]
American-College [2 Certification Exam(s) ]
Android [4 Certification Exam(s) ]
APA [1 Certification Exam(s) ]
APC [2 Certification Exam(s) ]
APICS [2 Certification Exam(s) ]
Apple [69 Certification Exam(s) ]
AppSense [1 Certification Exam(s) ]
APTUSC [1 Certification Exam(s) ]
Arizona-Education [1 Certification Exam(s) ]
ARM [1 Certification Exam(s) ]
Aruba [6 Certification Exam(s) ]
ASIS [2 Certification Exam(s) ]
ASQ [3 Certification Exam(s) ]
ASTQB [8 Certification Exam(s) ]
Autodesk [2 Certification Exam(s) ]
Avaya [96 Certification Exam(s) ]
AXELOS [1 Certification Exam(s) ]
Axis [1 Certification Exam(s) ]
Banking [1 Certification Exam(s) ]
BEA [5 Certification Exam(s) ]
BICSI [2 Certification Exam(s) ]
BlackBerry [17 Certification Exam(s) ]
BlueCoat [2 Certification Exam(s) ]
Brocade [4 Certification Exam(s) ]
Business-Objects [11 Certification Exam(s) ]
Business-Tests [4 Certification Exam(s) ]
CA-Technologies [21 Certification Exam(s) ]
Certification-Board [10 Certification Exam(s) ]
Certiport [3 Certification Exam(s) ]
CheckPoint [41 Certification Exam(s) ]
CIDQ [1 Certification Exam(s) ]
CIPS [4 Certification Exam(s) ]
Cisco [318 Certification Exam(s) ]
Citrix [48 Certification Exam(s) ]
CIW [18 Certification Exam(s) ]
Cloudera [10 Certification Exam(s) ]
Cognos [19 Certification Exam(s) ]
College-Board [2 Certification Exam(s) ]
CompTIA [76 Certification Exam(s) ]
ComputerAssociates [6 Certification Exam(s) ]
Consultant [2 Certification Exam(s) ]
Counselor [4 Certification Exam(s) ]
CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [9 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [13 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [750 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1532 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [64 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [69 Certification Exam(s) ]
Microsoft [374 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [279 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]
Vimeo : https://vimeo.com/240171468
Issu : https://issuu.com/trutrainers/docs/a2010-578
Dropmark : http://killexams.dropmark.com/367904/11412835
Wordpress : http://wp.me/p7SJ6L-eE
weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000VDNZ
Scribd : https://www.scribd.com/document/356764454/Pass4sure-A2010-578-Assess-Fundamentals-of-Applying-Tivoli-Service-Availability-Performance-Ma-exam-braindumps-with-real-questions-and-practice-soft
Dropmark-Text : http://killexams.dropmark.com/367904/12023865
Youtube : https://youtu.be/4Z3o2BW2x28
Blogspot : http://killexams-braindumps.blogspot.com/2017/10/look-at-these-a2010-578-real-question.html
RSS Feed : http://feeds.feedburner.com/JustStudyTheseIbmA2010-578QuestionsAndPassTheRealTest
publitas.com : https://view.publitas.com/trutrainers-inc/where-can-i-get-help-to-pass-a2010-573-exam
Google+ : https://plus.google.com/112153555852933435691/posts/N67MCfd19Ma?hl=en
Calameo : http://en.calameo.com/books/004923526b6f8f3044c0a
Box.net : https://app.box.com/s/iginewcbmes1crxhu6bed56d8l819yii
zoho.com : https://docs.zoho.com/file/5bym214ca77d8bb30459280764ae29017cbbd
coursehero.com : "Excle"