Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

In-band control, queuing, and failure recovery functionalities for openflow

In OpenFlow, a network as a whole can be controlled from one or more external entities (controllers) using in-band or out-of-band control networks. In this article, we propose in-band control, queuing, and failure recovery functionalities for OpenFlow. In addition, we report experimental studies and practical challenges for implementing these functionalities in existing software packages containing different versions of OpenFlow. The experimental results show that the in-band control functionality is suitable for all types of topologies. The results with the queuing functionality show that control traffic can be served with the highest priority in in-band networks and hence, data traffic cannot affect the communication between the controller and networking devices. The results with the failure recovery functionality show that traffic can be recovered from failures within 50 ms

New Hampshire University Research and Industry Plan: A Roadmap for Collaboration and Innovation

This University Research and Industry plan for New Hampshire is focused on accelerating innovation-led development in the state by partnering academia’s strengths with the state’s substantial base of existing and emerging advanced industries. These advanced industries are defined by their deep investment and connections to research and development and the high-quality jobs they generate across production, new product development and administrative positions involving skills in science, technology, engineering and math (STEM)

Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios