Data Center Traffic Research Articles

BurstBalancer: Do Less, Better Balance for Large-Scale Data Center Traffic

BurstBalancer: Do Less, Better Balance for Large-Scale Data Center Traffic

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing (MCQG). The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service. Different traffic scheduling strategies are used according to the characteristics of different service types in data centers. Reroute scheduling for elephant flows that tend to cause local congestion. The path evaluation function is formed by the maximum link utilization on the path, the number of elephant flows and the time delay, and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows. It is used to reduce the possibility of local network congestion occurrence. Equal cost multi-path (ECMP) protocols with faster response time are used to schedule mouse flows with shorter duration. Used to guarantee the quality of service of the network. To achieve isolated transmission of various types of data streams. The experimental results show that the proposed strategy has higher throughput, better network load balancing, and better robustness compared to ECMP under different traffic models. In addition, because it can fully utilize the resources in the network, MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows (namely Hedera). Compared with ECMP and Hedera, MCQG improves average throughput by 11.73% and 4.29%, and normalized total throughput by 6.74% and 2.64%, respectively; MCQG improves link utilization by 23.25% and 15.07%; in addition, the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

Adjusting Switching Granularity of Load Balancing for Heterogeneous Datacenter Traffic

The state-of-the-art datacenter load balancing designs commonly optimize bisection bandwidth with homogeneous switching granularity. Their performances surprisingly degrade under mixed traffic containing both short and long flows. Specifically, the short flows suffer from long-tailed delay, while the throughputs of long flows also degrade dramatically due to low link utilization and packet reordering. To solve these problems, we design a traffic-aware load balancing (TLB) scheme to adaptively adjust the switching granularity of long flows according to the load strength of short ones. Under the heavy load of short flows, the long flows use large switching granularity to help short ones obtain more opportunities in choosing short queues to complete quickly. On the contrary, the long flows reroute flexibly with small switching granularity to achieve high throughput. Furthermore, under extremely bursty scenario, we utilize the packet slicing scheme for long flows to release bandwidth for short ones. The experimental results of NS2 simulation and testbed implementation show that TLB significantly reduces the average flow completion time of short flows by 16%-67% over the state-of-the-art load balancers and achieves the high throughput for long flows. Moreover, for extreme bursty case, at the acceptable throughput degradation of long flows, TLB with packet slicing reduces the deadline missing ratio of bursty short flows by up to 80%.

Online Data Center Traffic Classification Based on Inter-Flow Correlations

Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.

Dynamic Load Balancing of Cloud Data Center Traffic Based on Software-Defined Networking

Cloud computing has grown rapidly in the last decade, where many tenants are using various cloud applications. Cloud computing users expect and demand to run their applications with the highest performance, reliability, and best quality of service. So, load balancing techniques are essential components and widely used in the cloud data center network to manage users’ requests and distribute applications traffic among available data center resources. Software-defined networking is a highly flexible network architecture that automates network configuration using a centralized controller to overcome the traditional network limitations and manual configuration for every network device. This paper proposes a dynamic load balancing technique using software-defined networking for the cloud data center that can handle different application types in real-time based on traffic type and the required quality of service. The proposed method aims to bridge the gap between application requirements and the resources in the cloud data center by choosing the best path for data transmission and selecting the best performance server to process the users' requests according to traffic type for efficient resources’ utilization, minimizing response time, and maximizing throughput. The simulation results show that the proposed method enhances the performance of the running applications’ and utilizes the data center resources efficiently compared with the current load balancing techniques.

Directing Data Center Traffic

A data center design based on an integrated chip-scale approach will be required to deal with the increasing volumes of Internet traffic.

On the Performance of a Large-Scale Optical Packet Switch Under Realistic Data Center Traffic

High-speed, submicrosecond-latency, large-port-count (thousands) optical packet switches (OPSs) for intercluster communication networks can become a key element in the deployment of cloud-oriented large-scale data centers. In this work we numerically investigate the performance of a large-port-count wavelength-division multiplexing (WDM) OPS based on a Spanke-type architecture with highly distributed control. We analyze it under a data center traffic model to determine its suitability for this type of environment. Results indicate that the proposed architecture can be scaled to 4096 ports while providing packet loss below 10−6 and latency under 1 μs, with a total switching capacity over 55  Tbits/s. Additionally, we propose and analyze two WDM OPS architectures. The first one detects and processes small and large-sized Ethernet packets with two parallel switches. The second architecture includes multiple receivers to decrease packet losses and latency while using very limited electronic buffers. Results indicate that both techniques can lead to substantial improvements. In terms of packet loss and latency, they allow up to 40% higher input load with respect to the original WDM OPS architecture.

Report on WREN 2009 -- workshop

WREN 2009, the Workshop on Research on Enterprise Networking, was held on August 21, 2009, in conjunction with SIGCOMM 2009 in Barcelona. WREN focussed on research challenges and results specific to enterprise and data-center networks. Details about the workshop, including the organizers and the papers presented, are at http://conferences.sigcomm.org/ sigcomm/2009/workshops/wren/index.php. Approximately 48 people registered to attend WREN. The workshop was structured to encourage a lot of questions and discussion. To record what was said, four volunteer scribes (Nathan Farrington, Nikhil Handigol, Christoph Mayer, and KokKiong Yap) took notes. This report is a merged and edited version of their notes. Please realize that the result, while presented in the form of quotations, is at best a paraphrasing of what was actually said, and in some cases may be mistaken. Also, some quotes might be mis-attributed, and some discussion has been lost, due to the interactive nature of the workshop. The second instance of WREN will be combined with the Internet Network Management Workshop (INM), in conjunction with NSDI 2010; see http://www.usenix.org/event/ inmwren10/cfp/ for deadlines and additional information. Also note that two papers from WREN were re-published in the January 2010 issue of Computer Communication Review: “Understanding Data Center Traffic Characteristics,” by Theophilus A Benson, Ashok Anand, Aditya Akella, and Ming Zhang, and “Remote Network Labs: An On-Demand Network Cloud for Configuration Testing,” by Huan Liu and Dan Orban. 2. SESSION 1: SECURITY