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

RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.Comment: To appear in IEEE INFOCOM 201

Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach

Network Functions Virtualization (NFV) is a promising network architecture where network functions are virtualized and decoupled from proprietary hardware. In modern datacenters, user network traffic requires a set of Virtual Network Functions (VNFs) as a service chain to process traffic demands. Traffic fluctuations in Large-scale DataCenters (LDCs) could result in overload and underload phenomena in service chains. In this paper, we propose a distributed approach based on Alternating Direction Method of Multipliers (ADMM) to jointly load balance the traffic and horizontally scale up and down VNFs in LDCs with minimum deployment and forwarding costs. Initially we formulate the targeted optimization problem as a Mixed Integer Linear Programming (MILP) model, which is NP-complete. Secondly, we relax it into two Linear Programming (LP) models to cope with over and underloaded service chains. In the case of small or medium size datacenters, LP models could be run in a central fashion with a low time complexity. However, in LDCs, increasing the number of LP variables results in additional time consumption in the central algorithm. To mitigate this, our study proposes a distributed approach based on ADMM. The effectiveness of the proposed mechanism is validated in different scenarios.Comment: IEEE International Conference on Communication Technology (ICCT), Chengdu, China, 201