Network Configuration and Flow Scheduling for Big Data Applications

International audienceThis chapter focuses on network configuration and flow scheduling for Big Data applications. It highlights how the performance of Big Data applications is tightly coupled with the performance of the network in supporting large data transfers. Deploying high-performance networks in data centers is thus vital but configuration and performance management as well as the usage of the network are of paramount importance. This chapter starts by discussing the problem of virtual machine placement and its solutions considering the underlying network topology. It then provides an analysis of alternative topologies highlighting their advantages from the perspective of Big Data applications needs. In this context, different routing and flow scheduling algorithms are discussed in terms of their potential for using the network most efficiently. In particular, Software-Defined Networking relying on centralized control and the ability to leverage global knowledge about the network state is propounded as a promising approach for efficient support of Big Data applications

Learning fine-grained search space pruning and heuristics for combinatorial optimization

Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time, effort and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. In this paper, we propose a novel framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive local features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99% of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. Overall, this results in several fold speedups of state-of-the-art algorithms. Furthermore, the classification model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic we call ALTHEA for the maximum clique detection problem, outperforming the state-of-the-art for dense graphs.Access provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit

A Network-Aware Virtual Machine Allocation in Cloud Datacenter

Part 2: Session 2: Cloud Resource ManagementInternational audienceIn a cloud computing environment, virtual machine allocation is an important task for providing infrastructure services. Generally, the datacenters, on which a cloud computing platform runs, are distributed over a wide area network. Therefore, communication cost should be taken into consideration when allocating VMs across servers of multiple datacenters. A network-aware VM allocation algorithm for cloud is developed. It tries to minimize the communication cost and latency between servers, with the number of VMs, VM configurations and communication bandwidths are satisfied to users. Specifically, a two-dimensional knapsack algorithm is applied to solve this problem. The algorithm is evaluated and compared with other ones through experiments, which shows satisfying results