Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Traffic Management in Software-Defined Data Center Networks

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.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. In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks by providing novel approaches that enhance the control plane as well as leveraging novel concepts of data plane programmability. At the control plane, we provide solutions such as high-level based policy framework, automation of layer 2 services, and fast restoration of network connectivity for virtual machine migration, that make network configuration and traffic management simpler for data-center operators. At the data plane, we propose approaches to improve load balancing in data centers and an in-network support mechanism to avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.  Article 8 part of doctoral thesis as manuscript, now published.</p

FlowDyn: Towards a Dynamic Flowlet Gap Detection using Programmable Data Planes

Data center networks offer multiple disjoint paths between Top-of-Rack (ToR) switches to connect server racks providing large bisection bandwidth. An effective load-balancing mechanism is required in order to fully utilize the available capacity of the multiple paths. While packet-based loadbalancing can achieve high utilization, it suffers from reordering. Flow-based load-balancing such as equal-cost multipath routing (ECMP) spreads traffic uniformly across multiple paths leading to frequent hash collisions and suboptimal performance. Finally, flowlet based load-balancing such as CONGA or HULA splits flows into smaller units, which are sent on different paths. Most flowlet based load-balancing schemes depend on a proper static setting of the flowlet gap, which decides when new flowlets are detected. While a too small gap may lead to reordering, a too large gap results in missed load-balancing opportunities. In this paper,weproposeFlowDyn,whichdynamicallyadaptstheflowlet gap to increase the efficiency of the load-balancing schemes while avoiding the reordering problem. Using programmable data planes, FlowDyn uses active probes together with telemetry informationtotrackpathlatencybetweendifferentToRswitches. FlowDyn calculates dynamically a suitable flowlet gap that can be used for flowlet based load-balancing mechanism. We evaluate FlowDyn extensively in simulation, showing that it achieves 3.19 times smaller flow completion time at 10% load and 1.16x at 90% load.HITS, 470

Study of TCP Available Bandwidth Using NS2 and Its Forecasting Based on Genetic Algorithm

On the one hand, the available bandwidth in a bandwidth-limited medium as the wireless medium is a highly demanded topic of study. On the other hand, the Transport Control Protocol (TCP) is one of the most used transport protocols on the Internet. The available bandwidth study and TCP constitute the most typical scenario in the Wireless Local Area Networks (WLAN). This Thesis locates the study in the 2.4GHz frequency band where Primary Users can be present modifying the behaviour of the WLAN medium. This band is unlicensed and, as a consequence of this, the congestion is considerable. Nowadays, several studies of this band are related to Cognitive Users operating in this band. However, this thesis studies the impact of Primary Users in the TCP available bandwidth in a classic IEE 802.11g WLAN network. The second part of the dissertation takes a step forward, a tool to forecast the TCP available bandwidth for WLAN based on a genetic algorithm has been developed. This tool is able to estimate the future available bandwidth finding the best function that will fit better to the future behaviour of the network. A genetic algorithm programmed specifically for this purpose finds this function. A significant number of tests have been carried out in the study. The TCP available bandwidth study shows a relation between MAC busy time and available bandwidth in some cases. In addition, the study shows that the TCP available bandwidth increases if the idle periods are longer. Reliable results in the forecasting have been achieved with a limitation in some specific scenarios

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer Reviewe

Automating Ethernet VPN deployment in SDN-based Data Centers

Layer 2 Virtual Private Network (L2VPN) is widely deployed in both service provider networks and enterprises. However, legacy L2VPN solutions have scalability limitations in the context of Data Center (DC) interconnection and networking which require new approaches that address the requirements of service providers for virtual private cloud services. Recently, Ethernet VPN (EVPN) has been proposed to address many of those concerns and vendors started to deploy EVPN based solutions in DC edge routers. However, manual configuration leads to a time-consuming, error-prone configuration and high operational costs. Automating the EVPN deployment from cloud platforms such as OpenStack enhances both the deployment and flexibility of EVPN Instances (EVIs). This paper proposes a Software Defined Network (SDN) based framework that automates the EVPN deployment and management inside SDN-based DCs using OpenStack and OpenDaylight (ODL). We implemented and extended several modules inside ODL controller to manage and interact with EVIs and an interface to OpenStack that allows the deployment and configuration of EVIs. We conclude with scalability analysis of our solution.HIT