Accuracy and Dynamics of Hash-Based Load Balancing Algorithms for Multipath Internet Routing

This paper studies load balancing for multipath Internet routing. We focus on hash-based load balancing algorithms that work on the flow level to avoid packet reordering which is detrimental for the throughput of transport layer protocols like TCP. We propose a classification of hash-based load balancing algorithms, review existing ones and suggest new ones. Dynamic algorithms can actively react to load imbalances which causes route changes for some flows and thereby again packet reordering. Therefore, we investigate the load balancing accuracy and flow reassignment rate of load balancing algorithms. Our exhaustive simulation experiments show that these performance measures depend significantly on the traffic properties and on the algorithms themselves. As a consequence, our results should be taken into account for the application of load balancing in practice

Scalable and Adaptive Load Balancing on IBM PowerNP

Web and other Internet-based server farms are a critical company resource. A solution to the increased complexity of server farms and to the need to improve the server performance in terms of scalability, fault tolerance and management is to implement a load balancing technique. It consists of a front-end machine which intelligently redirects the traffic to several Real Servers. We discuss the feasibility of implementing adaptive load balancing with minimal flow disruption on the IBM PowerNP Network Processor. We focus our attention on the steady-state part of the algorithm and propose a PowerNP-tailored mapping algorithm derived from Robust Hash Mapping. We propose and show a fast algorithm solution (despite the simple arithmetical logic of the PowerNP), as well as a scalable approach (aiming at minimizing the packet processing time) and, finally, we present some initial performance results

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall