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Michal Segalov

Michal Segalov

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    Network Utilization: The Flow View
    Ariel Shaqed (Scolnicov)
    IEEE INFOCOM 2013, IEEE, Turin, Italy
    Preview abstract Building and operating a large backbone network can take months or even years, and it requires a substantial investment. Therefore, there is an economical drive to increase the utilization of network resources (links, switches, etc.) in order to improve the cost efficiency of the network. At the same time, the utilization of network components has a direct impact on the performance of the network and its resilience to failure, and thus operational considerations are a critical aspect of the decision regarding the desired network load and utilization. However, the actual utilization of the network resources is not easy to predict or control. It depends on many parameters like the traffic demand and the routing scheme (or Traffic Engineering if deployed), and it varies over time and space. As a result it is very difficult to actually define real network utilization and to understand the reasons for this utilization. In this paper we introduce a novel way to look at the network utilization. Unlike traditional approaches that consider the average link utilization, we take the flow perspective and consider the network utilization in terms of the growth potential of the flows in the network. After defining this new Flow Utilization, and discussing how it differs from common definitions of network utilization, we study ways to efficiently compute it over large networks. We then show, using real backbone data, that Flow Utilization is very useful in identifying network state and evaluating performance of TE algorithms. View details
    Preview abstract Many practically deployed flow algorithms produce the output as a set of values associated with the network links. However, to actually deploy a flow in a network we often need to represent it as a set of paths between the source and destination nodes. In this paper we consider the problem of decomposing a flow into a small number of paths. We show that there is some fixed constant β >; 1 such that it is NP-hard to find a decomposition in which the number of paths is larger than the optimal by a factor of at most β. Furthermore, this holds even if arcs are associated only with three different flow values. We also show that straightforward greedy algorithms for the problem can produce much larger decompositions than the optimal one, on certain well tailored inputs. On the positive side we present a new approximation algorithm that decomposes all but an c-fraction of the flow into at most O(1/ϵ2) times the smallest possible number of paths. We compare the decompositions produced by these algorithms on real production networks and on synthetically generated data. Our results indicate that the dependency of the decomposition size on the fraction of flow covered is exponential. Hence, covering the last few percent of the flow may be costly, so if the application allows, it may be a good idea to decompose most but not all the flow. The experiments also reveal the fact that while for realistic data the greedy approach works very well, our novel algorithm which has a provable worst case guarantee, typically produces only slightly larger decompositions. View details
    Preview abstract This paper presents the "Mind the Gap" initiative that aims to encourage female high school pupils to study computer science (CS) in high school. This is achieved by increasing their awareness to what CS is, and exposing them to the essence of a hi-tech environment and to same gender role models. The initiative was undertaken by female software engineers at Google's Israel R&D Center in collaboration with the Israeli National Center for Computer Science Teachers. We describe the initiative and its impact on the female pupils' interest in CS. One of our conclusions is that even a short visit to a hi-tech company, in this case – Google, has the potential to change pupils' perception of what CS is and to increase their interest in CS and their desire to study it. Our initiative can be easily adapted by other companies and can be scaled to impact a rather large population. View details
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