Traffic distribution over equal-cost-multi-paths

To effectively manage the traffic distribution inside a network, traffic splitting is needed for load sharing over a set of equal-cost-multi-paths (ECMPs). In this paper, a new traffic splitting algorithm, called Table-based Hashing with Reassignments (THR), is proposed. Based on the load sharing statistics collected, THR selectively reassigns some active flows from the over-utilized paths to under-utilized paths. The reassignment process takes place in such a way that the packet out-of-order problem is minimized. As compared with the existing traffic splitting algorithms, THR provides close-to-optimal load balancing performance, less than 2% of packets arrived out-of-order, and a very small end-to-end packet delay performance. Although additional traffic monitoring function is needed by THR, we show that the extra complexity incurred is marginal.published_or_final_versio

Optimizing load balancing routing mechanisms with evolutionary computation

Link State routing protocols, such as Open Shortest Path First (OSPF), are widely applied to intra-domain routing in todays IP networks. They provide a good scalability without lost of simplicity. A router running OSPF distributes traf- fic uniformly over Equal-cost Multi-path (ECMP), enabling a better distribution of packets among the existent links. More recently, other load balancing strategies, that consider non even splitting of traffic, have been put forward. Such is the case of the Distributed Exponentially-weighted Flow SpliTting (DEFT), that enables traf- fic to be directed through non equal-cost multi-paths, while preserving the OSPF simplicity. As the optimal link weight computation is known to be NP-hard, intel- ligence heuristics are particularly suited to address this optimization problem. In this context, this work compares the solutions provided by Evolutionary Al- gorithms (EA) for the weight setting problem, considering both ECMP and DEFT load balancing alternatives. In addition to a single objective network congestion optimization problem, both load balancing schemes are also applied to a multi- objective optimization approach able to attain routing configurations resilient to traffic demand variations.COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e TecnologiaThis work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT -Fundação para a Ciência e Tecnologia within the ProjectScope: UID/CEC/00319/2013

Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale

Continuum Equilibria and Global Optimization for Routing in Dense Static Ad Hoc Networks

We consider massively dense ad hoc networks and study their continuum limits as the node density increases and as the graph providing the available routes becomes a continuous area with location and congestion dependent costs. We study both the global optimal solution as well as the non-cooperative routing problem among a large population of users where each user seeks a path from its origin to its destination so as to minimize its individual cost. Finally, we seek for a (continuum version of the) Wardrop equilibrium. We first show how to derive meaningful cost models as a function of the scaling properties of the capacity of the network and of the density of nodes. We present various solution methodologies for the problem: (1) the viscosity solution of the Hamilton-Jacobi-Bellman equation, for the global optimization problem, (2) a method based on Green's Theorem for the least cost problem of an individual, and (3) a solution of the Wardrop equilibrium problem using a transformation into an equivalent global optimization problem