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

A Method Based on Data Fragmentation to Increase the Performance of ICTCP During Incast Congestion in Networks

No Abstrac

F-DCTCP: Fair Congestion Control for SDN-Based Data Center Networks

Network congestion control and management has long been a major issue in providing low-latency, high throughput and high link-utilisation. In particular, Data Center Network (DCN) environments, often dominated by partition-aggregate workloads, suffer performance collapse due to TCP Incast caused by inadequate congestion control parameters. This paper proposes a step in solving this problem. In particular, we describe a novel framework to overcome and control congestion in DCNs based on Software Defined Networking (SDN). We propose a native SDN-based congestion control mechanism, termed Fair Data Center TCP (F-DCTCP). As we shall see, the experimental results show that F-DCTCP outperforms all previous proposals by providing the best combined overall performance in terms of throughout, fairness and flow completion times, making it highly attractive for next generation networks

Incast mitigation in a data center storage cluster through a dynamic fair-share buffer policy

Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission—resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern—Incast—in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device

Congestion control, energy efficiency and virtual machine placement for data centers

Data centers, facilities with communications network equipment and servers for data processing and/or storage, are prevalent and essential to provide a myriad of services and applications for various private, non-profit, and government systems, and they also form the foundation of cloud computing, which is transforming the technological landscape of the Internet. With rapid deployment of modern high-speed low-latency large-scale data centers, many issues have emerged in data centers, such as data center architecture design, congestion control, energy efficiency, virtual machine placement, and load balancing. The objective of this thesis is multi-fold. First, an enhanced Quantized Congestion Notification (QCN) congestion notification algorithm, called fair QCN (FQCN), is proposed to improve rate allocation fairness of multiple flows sharing one bottleneck link in data center networks. Detailed analysis on FQCN and simulation results is provided to validate the fair share rate allocation while maintaining the queue length stability. Furthermore, the effects of congestion notification algorithms, including QCN, AF-QCN and FQCN, are investigated with respect to TCP throughput collapse. The results show that FQCN can significantly enhance TCP throughput performance, and achieve better TCP throughput than QCN and AF-QCN in a TCP Incast setting. Second, a unified congestion detection, notification and control system for data center networks is designed to efficiently resolve network congestion in a uniform solution and to ensure convergence to statistical fairness with “no state” switches simultaneously. The architecture of the proposed system is described in detail and the FQCN algorithm is implemented in the proposed framework. The simulation results of the FQCN algorithm implemented in the proposed framework validate the robustness and efficiency of the proposed congestion control system. Third, a two-level power optimization model, namely, Hierarchical EneRgy Optimization (HERO), is established to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximizing link utilization. The power-saving performance of the proposed HERO model is evaluated by simulations with different traffic patterns. The simulation results have shown that HERO can reduce the power consumption of data center networks effectively with reduced complexity. Last, several heterogeneity aware dominant resource assistant heuristic algorithms, namely, dominant residual resource aware first-fit decreasing (DRR-FFD), individual DRR-FFD (iDRR-FFD) and dominant residual resource based bin fill (DRR-BinFill), are proposed for virtual machine (VM) consolidation. The proposed heuristic algorithms exploit the heterogeneity of the VMs’ requirements for different resources by capturing the differences among VMs’ demands, and the heterogeneity of the physical machines’ resource capacities by capturing the differences among physical machines’ resources. The performance of the proposed heuristic algorithms is evaluated with different classes of synthetic workloads under different VM requirement heterogeneity conditions, and the simulation results demonstrate that the proposed heuristics achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics