This paper studies the design of self-adjusting networks whose topology dynamically adapts to the workload, in an online and demand-aware manner. This problem is motivated by emerging optical technologies which allow to reconfigure the datacenter topology at runtime. Our main contribution is ReNet, a self-adjusting network which maintains a balance between the benefits and costs of reconfigurations. In particular, we show that ReNets are statically optimal for arbitrary sparse communication demands, i.e., perform at least as good as any fixed demand-aware network designed with a perfect knowledge of the future demand. Furthermore, ReNets provide compact and local routing, by leveraging ideas from self-adjusting datastructures.
We also note that our approach of reconfiguring network topologies to reduce communication costs, is orthogonal to optimization approaches changing the traffic matrix itself (e.g., @cite_35 ) or migrating communication endpoints on a fixed topology @cite_37 .
@cite_35 @cite_37
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The steadily increasing number of nodes in high-performance computing systems and the technology and power constraints lead to sparse network topologies. Efficient mapping of application communication patterns to the network topology gains importance as systems grow to petascale and beyond. Such mapping is supported in parallel programming frameworks such as MPI, but is often not well implemented. We show that the topology mapping problem is NP-complete and analyze and compare different practical topology mapping heuristics. We demonstrate an efficient and fast new heuristic which is based on graph similarity and show its utility with application communication patterns on real topologies. Our mapping strategies support heterogeneous networks and show significant reduction of congestion on torus, fat-tree, and the PERCS network topologies, for irregular communication patterns. We also demonstrate that the benefit of topology mapping grows with the network size and show how our algorithms can be used in a practical setting to optimize communication performance. Our efficient topology mapping strategies are shown to reduce network congestion by up to 80 , reduce average dilation by up to 50 , and improve benchmarked communication performance by 18 . Traffic in communication networks fluctuates heavily over time. Thus, to avoid capacity bottlenecks, operators highly overestimate the traffic volume during network planning. In this article we consider telecommunication network design under traffic uncertainty, adapting the robust optimization approach of Bertsimas and Sim [Oper Res 52 (2004), 35‐53]. We present two different mathematical formulations for this problem, provide valid inequalities, study the computational implications, and evaluate the realized robustness. To enhance the performance of the mixed-integer programming solver, we derive robust cutset inequalities generalizing their deterministic counterparts. Instead of a single cutset inequality for every network cut, we derive multiple valid inequalities by exploiting the extra variables available in the robust formulations. We show that these inequalities define facets under certain conditions and that they completely describe a projection of the robust cutset polyhedron if the cutset consists of a single edge. For realistic networks and live traffic measurements, we compare the formulations and report on the speed-up achieved by the valid inequalities. We study the “price of robustness” and evaluate the approach by analyzing the real network load. The results show that the robust optimization approach has the potential to support network planners better than present methods. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 000(00), 000‐000 2013 We study a class of noncooperative general topology networks shared by N users. Each user has a given flow which it has to ship from a source to a destination. We consider a class of polynomial link cost functions, adopted originally in the context of road traffic modeling, and show that these costs have appealing properties that lead to predictable and efficient network flows. In particular, we show that the Nash equilibrium is unique, and is moreover efficient, i.e., it coincides with the solution of a corresponding global optimization problem with a single user. These properties make the cost structure attractive for traffic regulation and link pricing in telecommunication networks. We finally discuss the computation of the equilibrium in the special case of the affine cost structure for a topology of parallel links. The routing infrastructure of the Internet has become resistant to fundamental changes and the use of overlay networks has been proposed to provide additional flexibility and control. One of the most prominent configurable components of an overlay network is its topology, which can be dynamically reconfigured to accommodate communication requirements that vary over time. In this paper, we study the problem of determining dynamic topology reconfiguration for service overlay networks with dynamic communication requirement, and the ideal goal is to find the optimal reconfiguration policies that can minimize the potential overall cost of using an overlay. We start by observing the properties of the optimal reconfiguration policies through studies on small systems and find structures in the optimal reconfiguration policies. Based on these observations, we propose heuristic methods for constructing different flavors of reconfiguration policies, i.e., never-change policy, always-change policy and cluster-based policies, to mimic and approximate the optimal ones. Our experiments show that our policy construction methods are applicable to large systems and generate policies with good performance. Our work does not only provide solutions to practical overlay topology design problems, but also provides theoretical evidence for the advantage of overlay network due to its configurability.