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A2010-578 - Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma - BrainDump Information

Vendor Name : IBM
Exam Code : A2010-578
Exam Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Questions and Answers : 120 Q & A
Updated On : November 16, 2018
PDF Download Mirror : A2010-578 Braindumps
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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
Q&A : 120 Real Questions

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IBM IBM Assess: Fundamentals of

three Days Left To cash in On overseas enterprise Machines supplier (NYSE:IBM) Dividend, | killexams.com Real Questions and Pass4sure dumps

crucial news for shareholders and competencies investors in foreign company Machines company (NYSE:IBM): The dividend price of US$1.57 per share may be allotted to shareholders on 10 December 2018, and the inventory will begin trading ex-dividend at an prior date, 08 November 2018. is that this future earnings a persuasive sufficient catalyst for traders to believe about international company Machines as an funding today? under, I’m going to look at the newest facts and analyze the stock and its dividend property in further element.

take a look at our newest analysis for foreign enterprise Machines

5 checks you should use to investigate a dividend stock

When assessing a stock as a potential addition to my dividend Portfolio, I look at these five areas:

  • Is their annual yield among the many excellent 25% of dividend payers?
  • Does it constantly pay out dividends without missing a price of tremendously reducing payout?
  • Has it multiplied its dividend per share volume during the last?
  • Does revenue amply cover its dividend funds?
  • Will the business be capable of preserve paying dividend in response to the long run salary boom?
  • NYSE:IBM ancient Dividend Yield November 4th 18

    more How smartly does foreign business Machines healthy our standards?

    The company currently will pay out 99% of its profits as a dividend, based on its trailing twelve-month facts, which potential that the dividend isn't smartly-coated by means of its revenue. besides the fact that children, going ahead, analysts predict IBM’s payout to fall right into a greater sustainable latitude of 46% of its profits, which leads to a dividend yield of 5.5%. moreover this, EPS may still boost to $12.03, which means that the reduce payout ratio doesn't necessarily implicate a lower dividend price.

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    If dividend is a key standards to your investment consideration, then you definitely should be certain the dividend inventory you’re eyeing out is legit in its funds. IBM has accelerated its DPS from $2 to $6.28 during the past 10 years. It has also been paying out dividend perpetually throughout this time, as you’d are expecting for a company expanding its dividend tiers. here's an magnificent feat, which makes IBM a true dividend rockstar.

    when it comes to its friends, foreign enterprise Machines has a yield of 5.4%, which is high for IT shares.

    subsequent Steps:

    With these dividend metrics in mind, I in fact rank overseas business Machines as a robust salary stock, and is value further research for anybody who considers dividends an important part of their portfolio approach. on account that here's basically a dividend evaluation, i urge abilities traders to are attempting and get a good figuring out of the underlying business and its fundamentals earlier than settling on an funding. beneath, I’ve compiled three pertinent elements you should further analysis:

  • Future Outlook: What are neatly-informed industry analysts predicting for IBM’s future growth? Take a look at our free research report of analyst consensus for IBM’s outlook.
  • Valuation: what's IBM worth these days? notwithstanding the inventory is a cash cow, it’s now not price an infinite fee. The intrinsic price infographic in our free research report helps visualize even if IBM is presently mispriced by way of the market.
  • other Dividend Rockstars: Are there stronger dividend payers with superior fundamentals out there? take a look at our free record of those excellent stocks here.
  • To support readers see previous the brief term volatility of the fiscal market, we aim to bring you an extended-time period focused research analysis in simple terms pushed with the aid of fundamental records. notice that our analysis does not element within the latest cost-delicate company bulletins.

    The author is an impartial contributor and on the time of booklet had no position in the stocks mentioned. For mistakes that warrant correction please contact the editor at editorial-crew@simplywallst.com.


    Is IBM in reality Resetting the Cloud landscape With Its pink Hat Acquisition? | killexams.com Real Questions and Pass4sure dumps

    Assuming it passes muster -- and there's no reason to are expecting it might not -- IBM's (NYSE:IBM) introduced purchase of red Hat (NYSE:RHT) for $33 billion will make it the third-greatest M&A deal in the U.S. tech house ever. So even for a legacy enormous, here's critical enterprise. And at $a hundred ninety a share, or not it's paying a sixty three% top class from where the goal closed Friday. So for buyers, the obtrusive query is: what's it about this specific deal that makes it so vital for IBM?

    To clarify what's up, MarketFoolery host Mac Greer has analysts Emily Flippen and Jason Moser join him for this podcast to assessment the aggressive situation in the cloud, speak about the techniques the crimson Hat acquisition will increase IBM's choices, and agree with how this linkup may play out.

    A full transcript follows the video.

    This video turned into recorded on Oct. 29, 2018.

    Mac Greer: IBM making a big deal. On Sunday, IBM agreeing to buy purple Hat for $33 billion or $190 in money per share. it really is a sixty three% top rate to where red Hat closed on Friday. That appears like a great deal, correct?

    Emily Flippen: it's.

    Jason Moser: It sounds like plenty.

    Greer: not bad. IBM CEO Ginni Rometty says the deal is all about, "resetting the cloud panorama." Emily, I need to get into that here in a minute. the clicking release asserting the deal describes red Hat as "the realm's main provider of open source cloud application." when you seem at the deal, shares of purple Hat up around forty seven% today. IBM, down a bit. an outstanding deal for IBM?

    Flippen: I suppose or not it's a crucial deal for IBM. or not it's an enchanting deal for red Hat. For a bit fun fact, here's in reality the third biggest deal in the U.S. background for tech.

    Greer: it really is variety of enjoyable.

    Flippen: it be a enjoyable fact. it is to claim that, you'll note that red Hat shares are not exactly at that $190 expense. that's as a result of the undeniable fact that this could want regulatory and shareholder approval earlier than it moves forward. i'd predict that to happen.

    it's an exhilarating deal for IBM. IBM has been posting in reality stagnant increase over the last few years. They crucial to do whatever. They had been losing out horribly to competitors like Amazon and Microsoft during this house. This acquisition for them is an attempt to control each a part of the cloud ecosystem for an enterprise. if you are an commercial enterprise, and you've got a lot of actual assets, in terms of common information storage, and also you're trying to circulation toward a cloud atmosphere, what they do is, they have got an inter-between section the place you're operating in a hybrid cloud environment. This deal is going to enable IBM to get in on the hybrid cloud environment area and support organizations transition to that cloud atmosphere. they may be using crimson Hat to try this.

    Moser: I think she hit the nail on the pinnacle there. It became a vital deal for IBM. we've been speakme about IBM form of just sitting there doing nothing over the past decade, extra or less, as each other tech company we discus seems to be flying correct previous it. customarily, with massive tech corporations like this, which are a bit bit greater in response to legacy success, you have to go in there and make a huge deal like this. that's why it's the sort of premium, I consider. IBM truly wishes this. They don't want red Hat to head searching round and see if a person desires to up the ante a bit bit. So, they're paying a extremely heavy top rate nowadays.

    It greater than likely goes through. in all probability there's some scrutiny there. but once I seemed at the two corporations, I consider this in fact tells you everything you should recognize. in the event you look at the agencies, you note the disparity in the research and construction line merchandise on their salary statements. in case you examine R&D as a percent of earnings, for pink Hat, or not it's about 20%. they're plowing loads of the cash they make back into the company as a result of they need to keep significant and maintain advancing. IBM's around 7%, and they're definitely doing that just to tread water. and that is the reason what they've been doing for a long time now.

    to look them make a large deal like this is rarely excellent. I think purple Hat's a very good company. the basics are sound. whether IBM in reality does anything else truly respectable with it continues to be to be seen.

    Greer: in the event you study IBM the stock, it hasn't even been treading water. You seem over the last 5 years. Emily, we were speaking about this before the exhibit. Shares are down. you have got misplaced cash on IBM over the last 5 years. it truly is relatively tough to do during this market.

    Moser: Yeah, it had the recognition for the longest time of, in case you couldn't actually figure out what sort of promote it was and how you'll want to be investing, you might always just purchase shares of IBM and no person would dangle that in opposition t you as a result of its standing within the tech house. but certainly, tech has modified so plenty in only the fresh years, and IBM hasn't actually been spearheading that alternate.

    Greer: I wish to speak greater about this theory of resetting the cloud panorama. IBM's CEO says that big agencies have moved round 20% of their work to the cloud. Let's simply take that as our starting point. That leaves around 80% that all of these distinct groups are competing for. IBM and crimson Hat can go after that 80%. but we've obtained just a few rivals. Let's simply evaluation one of the crucial names in that area. Amazon, Microsoft, and Alphabet.

    Flippen: What's wonderful about crimson Hat is they are concentrated on open-sourced systems. When an individual goes out uses crimson Hat as a provider, they're no longer actually buying any category of fabric utility. all the code is already available. if they desired that, they could reasonably literally go onto GitHub and copy and paste it. What they may be doing is, they're purchasing purple Hat support -- the expertise, the skills it truly is crucial to put in force that. it's going to be in fact enjoyable to peer how they compete. you're going to word that loads of their rivals definitely use crimson Hat.

    What IBM is making an attempt to do is announcing, "good day, we're not going to be the massive cloud issuer it really is Amazon internet services. however, we can at the least be the equipment which Amazon net functions is operating on." pink Hat's most widely subscribed product is pink Hat enterprise Linux. it be the operating device that all this expertise is working on. What they may be attempting to do is get in on the base level of that and take a look at to be a participant within the house without always at once competing.

    We mentioned previous the tradition around IBM, the way it's been a sluggish growth company, not doing plenty during this house. I consider this is the place the controversy for this deal comes from. lots of people see purple Hat as an innovator, fully open-source. Any enterprise that can make $2 billion promoting whatever it is free is a great company in my booklet.

    Moser: [laughs] that is an outstanding element!

    Flippen: So, the opportunity there for a cultural disillusion, as this enterprise it really is been such an innovator, that's been open-supply, moves to this conglomerate, this colossal, sluggish tech enterprise, it may be a hard pill to swallow for some personnel. but IBM definitely needs the boom.

    Greer: The old culture conflict. we've got heard that story earlier than. I believe returned to AOL and Time Warner. we will see how the deal shakes out.

    John Mackey, CEO of complete meals Market, an Amazon subsidiary, is a member of The Motley idiot's board of administrators. Suzanne Frey, an government at Alphabet, is a member of The Motley fool's board of administrators. Teresa Kersten, an worker of LinkedIn, a Microsoft subsidiary, is a member of The Motley idiot's board of directors. Emily Flippen has no position in any of the stocks outlined. Jason Moser has no position in any of the stocks outlined. Mac Greer owns shares of Alphabet (C shares) and Amazon. The Motley idiot owns shares of and recommends Alphabet (A shares), Alphabet (C shares), and Amazon. The Motley idiot has a disclosure policy.


    IBM stock investors Don’t Yet Get It — purple Hat Is a game-Changer | killexams.com Real Questions and Pass4sure dumps

    The large news in the M&a global this week was that blue-chip technology tremendous IBM (NYSE:IBM) is acquiring hybrid cloud enterprise pink Hat (NYSE:RHT) for $34 billion, a steep 60% premium to the open supply utility maker’s market cost. Wall street largely praised the deal, with IBM inventory analysts calling the deal “as transformative as it receives”.

    but, traders weren’t so certain. The deal was announced on Sunday, Oct. 28. That outdated Friday, massive Blue’s inventory closed at $one hundred twenty five. today, it sits at $a hundred and fifteen.

    Why the investor bearishness? prices are going up, and IBM is presumably taking over a lot of debt to fund this deal. therefore, IBM’s balance sheet is being loaded up with debt on the exact time you don’t wish to load up your steadiness sheet with liabilities. investors are involved about revenue and cash flows being pressured, and are sooner or later worried that the extra debt burden will stop buybacks and/or drive a dividend cut.

    That won’t occur. as a substitute, the analysts have it appropriate on this one. The red Hat deal is a online game-changer for IBM and, because of this, IBM stock. The tech titan obligatory to take motion to boost its cloud enterprise, which has been the simplest respectable thing about this company for the past a couple of years. lately, notwithstanding, the cloud enterprise has misplaced some luster as growth costs slipped and IBM fell at the back of competitors Amazon (NASDAQ:AMZN), Microsoft (NASDAQ:MSFT), and Alphabet (NASDAQ:GOOGL).

    The pink Hat acquisition changes all that. it'll boost IBM’s cloud presence, and sooner or later enrich long-term salary and income boom potentialities. these advancements should still drive IBM inventory higher, principally from today’s depressed base.

    As such, I feel now's the time to get bullish on IBM stock.

    The red Hat Deal Is a favorable

    IBM has been affected by sluggish profits boom during the last a couple of years. while management has executed everything in its vigor to reduce charges and enhance margins, slow sales growth has ultimately held back this enterprise’s income vigor and saved IBM stock depressed.

    The huge issue is that this vogue wasn’t reversing on its own. Bulls have been hoping for out-sized cloud growth to shake IBM inventory out of its droop. That didn’t occur. as an alternative, cloud increase costs have been cooling for a couple of quarters now. web outcomes? IBM’s earnings growth is still just a little down to flat, while income boom remains muted.

    but, the red Hat acquisition will reverse this fashion. red Hat is a major hybrid cloud player which dominates the all-important open supply software part of the cloud. This company is a double-digit earnings grower with eighty five%-plus gross margins and 20%-plus operating margins. IBM, by contrast, is a flat salary increase enterprise with 50% gross margins and just about 20% pre-tax margins.

    in short, IBM is buying a much bigger salary grower with a healthier margin profile. it is a recipe for achievement.

    in addition, purple Hat counts greater than ninety% of Fortune 500 agencies as customers, and has a hundred,000-plus commercial enterprise valued clientele in total. The acquisition of pink Hat offers IBM a unique and valuable opportunity to go-sell and up-promote cloud capabilities to crimson Hat’s huge client base. Inevitably, this can deliver a lift to IBM’s cloud company.

    typical, then, this deal is a tremendous wonderful. yes, it's going to cost IBM a boatload of money. but, it's sooner or later worth it as a result of purple Hat is the shot in the arm that IBM needs to reinvigorate increase in its key cloud enterprise. Over the next several years, the IBM-red Hat pairing will steadily gain share in opposition t the cloud’s large 3 — Amazon, Microsoft, and Google. These shares beneficial properties will sooner or later propel more advantageous economic efficiency for IBM and send IBM stock bigger.

    IBM inventory Is Undervalued

    After this most contemporary sell-off, IBM stock is vastly undervalued.

    The shares now change at just 8X forward salary. That’s as low-priced as IBM inventory has been for the reason that the top notch Recession. The cost-to-revenue diverse is at 1.3X, additionally the bottom stage due to the fact that the economic disaster. same with the expense-to-book and value-to-money circulate multiples. meanwhile, the dividend yield is up at 5%, which is as high because it’s been in more than two many years.

    throughout the board, IBM stock is grime low-cost.

    and dirt cheap handiest makes experience if IBM stock is prepping for large earnings and profits erosion, or a complete balance sheet wipe-out from too lots debt. None of that is going to turn up. revenue increase will enhance over the subsequent a few years thanks to the purple Hat integration. And, as a result of purple Hat has bigger margins, income increase will increase, too.

    in the meantime, IBM is anticipated to generate $12 billion in free money movement this 12 months, and here is a depressed yr. for this reason, it could take IBM only three years of depressed operations to service a $34 billion debt-funded acquisition with free cash move. That capability the balance sheet isn’t all that confused by using this acquisition.

    base line on IBM inventory

    typical, IBM stock is simply too low-cost here to ignore. This inventory should still soar again from present degrees, and sooner or later exchange manner larger because the market begins to reasonably comprise reinvigorated cloud growth prospects into the valuation.

    And while IBM stock is dirt low cost and has plunged into oversold territory, the fundamentals are in fact enhancing due to the crimson Hat acquisition. therefore, recent weak spot should still reverse direction, and IBM inventory may still soar better from right here.

    As of this writing, Luke Lango turned into IBM, AMZN, MSFT and GOOGL.




    Killexams.com A2010-578 Dumps and Real Questions

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    A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

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    Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

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    Resilience and efficiency in transportation networks | killexams.com real questions and Pass4sure dumps

    Abstract

    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, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We 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, we study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. We 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, we 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, we 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). We relied on these data to assess commuter patterns, which we 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. We review these approaches in the Supplementary Materials and note that the main benefit of our 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. We did not create a more accurate transportation model than the existing ones, but we were able to obtain measurable characteristics of transportation systems (average delays) using our model.

    Geospatial boundaries and population density

    To define geospatial boundaries for the transportation infrastructure networks, we used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). We 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, we 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, we 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, we split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni asEmbedded Image

    Embedded Image

    (1)

    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, we assessed the fraction of individuals commuting from region o to destination region d, fod, asEmbedded Image

    Embedded Image

    (2)

    Then, the commuter flow from origin region o to destination region d isEmbedded Image

    Embedded Image

    (3)

    Although individual driving habits may vary (54), we 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, we estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in place of the distance factor P(xod), we used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which we 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, we defined the commuter load on each road segment asEmbedded Image

    Embedded Image

    (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, we 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 isEmbedded Image

    Embedded Image

    (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), we chose to use the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary MaterialsEmbedded Image

    Embedded Image

    (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, we 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, we 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. We 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, our model proxies for resilience and is calibrated against the data that proxy for efficiency. At the same time, we 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 we had for efficiency, we quantified resilience through additional delays.

    RESULTS Efficiency

    Together, our 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. We 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. We 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, we 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, we select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. We 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. We 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 our model considers only roadway transit, and New York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that our 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.

  • Acknowledgments: We would like to thank S. Buldyrev (Yeshiva University) and J. Palma-Oliveira (University of Lisbon) for their insightful comments. Funding: This study was supported by the U.S. Army Engineer Research and Development Center and by the Defense Threat Reduction Agency, Basic Research Program (P. Tandy, program manager). A.A.G. was additionally supported by the Virginia Transportation Research Council and Virginia Department of Transportation. T.S. was supported by the NSF under grant no. 1441352. Author contributions: A.A.G., M.K., and I.L. conceived the model and designed the simulations. A.A.G. developed software and performed data retrieval and simulations. A.A.G. and M.K. analyzed results. I.L. provided senior guidance. A.A.G., M.K., J.M.K., T.S., and I.L. wrote the paper and contributed to the interpretation of the results. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.

    CDP in depth | killexams.com real questions and Pass4sure dumps

    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.



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