Traffic Management in Software-Defined Data Center Networks

Abstract: Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data-center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices. Despite its holistic view of the network, data centers are commonly interconnected by traditional networks that use standard routing protocols. It is therefore essential to achieve interoperability with legacy systems, end-to-end traffic management, and to avoid the cumbersome, time-consuming, and error-prone configuration process of data-center edge network devices.In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks. To achieve this objective, we provide novel approaches that enhance the control plane as well as leverage novel concepts of data plane programmability.At the control plane, we first propose different mechanisms that enable the fast restoration of network connectivity after a virtual machine migration. Second, we suggest a network management automation framework that extends layer 2 connectivity to the tenants' services hosted across geo-distributed data centers. Moreover, we provide high-level policy-based mechanisms that make network configuration and traffic management simpler for data-center operators. At the data plane, we develop MP-HULA that load-balances multipath connections across least-congested paths. MP-HULA leverages advanced data plane mechanisms to rank multiple paths according to congestion metrics and uses that information for fine-grained load-balancing decisions considering transport layer information. To improve flowlet-based load-balancing decisions, we propose FlowDyn, which efficiently estimates round-trip time using programmable telemetry data. Finally, we present pCoflow, an in-network support mechanism that uses advanced programmable scheduling primitives to effectively avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.