Delivering Consistent Network Performance in Multi-tenant Data Centers

Data centers are growing rapidly in size and have recently begun acquiring a new role as cloud hosting platforms, allowing outside developers to deploy their own applications on large scales. As a result, today\u27s data centers are multi-tenant environments that host an increasingly diverse set of applications, many of which have very demanding networking requirements. This has prompted research into new data center architectures that offer increased capacity by using topologies that introduce multiple paths between servers. To achieve consistent network performance in these networks, traffic must be effectively load balanced among the available paths. In addition, some form of system-wide traffic regulation is necessary to provide performance guarantees to tenants. To address these issues, this thesis introduces several software-based mechanisms that were inspired by techniques used to regulate traffic in the interconnects of scalable Internet routers. In particular, we borrow two key concepts that serve as the basis for our approach. First, we investigate packet-level routing techniques that are similar to those used to balance load effectively in routers. This work is novel in the data center context because most existing approaches route traffic at the level of flows to prevent their packets from arriving out-of-order. We show that routing at the packet-level allows for far more efficient use of the network\u27s resources and we provide a novel resequencing scheme to deal with out-of-order arrivals. Secondly, we introduce distributed scheduling as a means to engineer traffic in data centers. In routers, distributed scheduling controls the rates between ports on different line cards enabling traffic to move efficiently through the interconnect. We apply the same basic idea to schedule rates between servers in the data center. We show that scheduling can prevent congestion from occurring and can be used as a flexible mechanism to support network performance guarantees for tenants. In contrast to previous work, which relied on centralized controllers to schedule traffic, our approach is fully distributed and we provide a novel distributed algorithm to control rates. In addition, we introduce an optimization problem called backlog scheduling to study scheduling strategies that facilitate more efficient application execution

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

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

Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen