Dynamic Control of Flow Completion Time for Power Efficiency of Data Center Networks

In the era of cloud computing, data center network (DCN) can consume a significant amount of power (e.g., 10 to 20 percent) in large-scale data centers. To reduce the power consumption of DCN, traffic consolidation has been recently proposed as an effective approach to reduce the number of DCN devices in use. However, existing consolidation approaches do not sufficiently consider the flow completion time (FCT) requirement. On one hand, missing the FCT deadlines can cause serious violation of service-level agreement, especially for delay-sensitive networking services, such as web search and E-commerce. On the other hand, keeping all the devices on to make FCTs much shorter than the desired requirements is unnecessary because 1) users may not be able to perceive the difference, and 2) such a greedy strategy can lead to unnecessarily high DCN power consumption and thus more electricity costs. In this paper, we propose FCTcon, a dynamic FCT control strategy for DCN power optimization. FCTcon is designed rigorously based on control theory to dynamically control the FCT of delay-sensitive traffic flows exactly to the requirements, such that the desired FCT performance is guaranteed while the maximum amount of DCN power savings can be achieved. Results from both hardware experiments and simulation evaluation demonstrate that, compared to the state-of-the-art DCN power optimization schemes, FCTcon can improve the DCN FCT performance, while achieving nearly the same or even more power savings. Consequently, FCTcon can result in more than 22.0 to 62.2 percent extra net profits for a data center with 50K servers. In addition, we further propose two extended designs of FCTcon to handle the coflow abstraction recently proposed for DCN, which successfully reduce the coflow deadline miss ratio by 12 to 15 percent.

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions.

One of the major issues in Data Center Networks (DCNs) is related to elephant flows. These flows typically are primarily responsible for network congestion, causing impacts on applications that are intolerant to delays. Although there are several works in the literature that analyze this type of traffic, none of them consider the impact of elephant flow size on the throughput and on the Flow Completion Time (FCT). This article presents the EFM (Elephant Flow Manager) as a complete solution for monitoring and re-routing elephant flows in DCNs, by observing the size of these flows in the network. The tests performed show that the size of the elephant flows influences the throughput and the FCT of the applications and, therefore, deserves to be observed. The evaluation of the solution was performed comparing the joint use of EFM with ECMP (EqualCost Multi-path). In all tests, our re-routing solution proved to be better when compared to pure routing solely with ECMP. Then, this paper presents three main contributions: (1) it compares our solution with the ECMP, (2) highlights the importance of the size of the elephant flows in the re-routing of these in DCNs and (3) preliminary tests indicate that due to the decrease in FCT, it is possible to reduce the energy consumption in the data center.

Cloud applications have reshaped the model of services and infrastructure of the Internet. Search engines, social networks, content delivery and retail and e-commerce sites belong to this group of applications. An important element in the architecture of data centers where these applications run is the communication infrastructure, commonly known as data center networks (DCNs). A critical challenge DCNs have to address is the processing of the traffic of cloud applications, which due to its properties is essentially different to the traffic of other Internet applications. In order to improve the responsiveness and throughput of applications, DCNs should be able to prioritize short flows (a few kB) over long flows (several MB). However, given the time and space variations the traffic presents, the information about flow sizes is not available in advance in order to plan the flow scheduling. There has been a wealth of solutions developed in this space, and prior work includes flow scheduling mechanisms optimizing for a specific workload but fall short when workloads are not known in advance, or comprise a collection of applications changing dynamically. In this paper, we present an adaptable mechanism called Adaptable Workload-Agnostic Flow Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic way the scheduling configuration of DCN forwarding devices. This agnostic adjustment contributes to reduce the Flow Completion Time (FCT) of those short flows, representing around 85% of the traffic handled by cloud applications. AWAFS operates by observing the traffic and detecting statistical properties that provide a hint to adapt the scheduling parameters. Our evaluation results based on simulation show that AWAFS can reduce the average FCT of short flows between 16.9% and 45.2% when compared to the best existing agnostic non-adaptable solution, without inducing starvation on long flows. Indeed, it can provide improvements as high as 39% for long flows. Additionally, AWAFS can improve the FCT for short flows in scenarios with high heterogeneity in the traffic present in the network, with a reduction up to 5% for the average FCT and 15% for the tail FCT.

Reducing the power consumption and maintaining the Flow Completion Time (FCT) for the Quality of Service (QoS) of applications in Data Center Networks (DCNs) are two major concerns for data center operators. However, existing works either fail in guaranteeing the QoS due to the neglect of the FCT constraints or achieve a less satisfying power efficiency. In this paper, we propose SmartFCT, which employs Software-Defined Networking (SDN) coupled with the Deep Reinforcement Learning (DRL) to improve the power efficiency of DCNs and guarantee the FCT. The DRL agent can generate a dynamic policy to consolidate traffic flows into fewer active switches in the DCN for power efficiency, and the policy also leaves different margins in different active links and switches to avoid FCT violation of unexpected short bursts of flows. Simulation results show that with similar FCT guarantee, SmartFCT can save 8% more of the power consumption compared to the state-of-the-art solutions.

Network congestion causes severe transmission performance loss. Congestion control is the key to achieve low latency, high bandwidth and high stability. Due to the high regularity of topologies, datacenter networks contain abundant resources of equal-cost paths inherently and have huge potential for high-bandwidth transmission. Taking in-network telemetry (INT) information as the congestion signal, congestion control algorithms can monitor the network based on precise link load data. Hence the INT-based algorithm is the focus of recent high-precision congestion control research. However, existing INT-based congestion control algorithms handle the single-path transmission between the sender and the receiver, which cannot make full use of the abundant equal-cost paths resources in the network. In this paper, we make the first attempt to propose a multi-path INT-based congestion control algorithm, called MPICC. Compared with the most advanced INT-based congestion control algorithm HPCC, our method realizes multi-path coordinated transmission of data packets in datacenter networks. We conduct extensive experiments under various workloads to evaluate the performance of MPICC. The experimental results show that under the condition of ensuring high throughput of end hosts, MPICC is able to use multi-path to transmit data packets, which greatly reduces the flow completion time (FCT) of the flow in the network. Specifically, under three common datacenter workloads, Cache Follower, Web Search and Web Server, MPICC reduces the average flow completion time of the network by 20.1%, 14.4%, and 39.5%, respectively, and shortens the 97th-percentile flow completion time by 39.9%, 18.9% and 57.9%, respectively.KeywordsCongestion controlIn-network telemetry (INT)Multi-pathDatacenter networksProgrammable switch

Today’s data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.

In data center networks (DCNs), the flow size is a crucial factor to schedule the traffic, as small flows are usually latency-sensitive (e.g., web search from users) while large flows are not (e.g., backup between two sites). Some existing flow scheduling schemes try to schedule flows based on the prior knowledge of flows, such as flow size and deadline. However, it may be unpractical to obtain the prior information in some scenarios. In this paper, we propose START to minimize the flow completion time (FCT) and guarantee the priority of small flows in DCNs. We control the priorities of packets by leveraging the multiple priority queues based on the amount of data a flow has sent. Switches schedule flows according to their priorities. In addition, to schedule the flows more intelligently, we adjust the threshold of each priority dynamically according to the network loads. A prototype of START has been implemented with ns2 simulations. START is compatible with legacy TCP/IP stacks and deployable with existing commodity switches. The results show that START significantly outperforms existing flow scheduling schemes DCTCP and L2DCT up to 40% and 45% for small flows in changing network status. In some scenarios, START can even achieve better performance than pFabric.

With the rapid advancement of big data processing and artificial intelligence (AI), data center networks (DCNs) must deliver more efficient resource management and data transmission mechanisms. Unfortunately, due to the significant differences in bandwidth requirements, transmission patterns, and temporal characteristics across various traffic types in DCNs (such as short flows, long flows, and bursty flows), traditional buffer allocation strategies fail to adapt flexibly to these disparities. In this paper, we propose Enhanced Buffer Management (EBM), a novel buffer-sharing scheme designed for scenarios that require higher performance from DCNs. Unlike prior approaches, EBM employs a multi-level flow identification and adaptive threshold adjustment mechanism to enhance the flexibility and efficiency of buffer management under varying traffic conditions. Specifically, EBM first performs coarse-grained and fine-grained classification of traffic based on packet size, inter-arrival interval, and other flow characteristics. It then applies an improved threshold computation function to allocate buffer space differentially across traffic classes while maintaining allocation smoothness. Our evaluation results demonstrate that EBM significantly improves performance under realistic workloads. For instance, it reduces the 99th percentile Flow Completion Time (FCT) slowdown by 32.7% for short flows in the web-search workload and by 45.1% for incast flows in the hadoop workload, all without sacrificing overall throughput.

Improving the operational efficiency of data center has always been an important direction for the development of ICT. In this paper, we apply two‐sided matching decision‐making process in game theory to traffic scheduling problem in data center network. From the perspective of matching between flow and path, the traffic scheduling is properly arranged. We first propose and model the path‐flow matching problem, considering the preference ordering, then formulate the problem as a multiobjective optimization problem with the target to ensure the stability and satisfaction from the matching scheme, and design a preference‐based path‐flow ordering method Extended PIAS, and finally propose a lightweight scheduling algorithm LinkGame based on multiobjective evolutionary algorithm. Compared with the previous scheduling methods (ECMP, Hedera, and Fincher), experiment results demonstrate that LinkGame can simultaneously consider the stability and satisfaction of the matching results, with improved bandwidth utilization and flow completion time.

Nowadays, TCP has been used as the de facto transport layer protocol on the Internet. However, TCP incast happens inevitably in data centers, where exist massive concurrent flows, leading to packets loss, unacceptable long transmission time, and throughput collapse. Furthermore, DCTCP is widely used to solve these problems. Unfortunately, DCTCP cannot work well under a high concurrency environment. To this end, in this paper, we introduce a two-stage congestion control algorithm (TS-TCP) for high concurrency flows. The first step is to reduce the congestion window of long-lived flows aggressively to release bandwidth for short-lived flows. The second step is to decline the sending rate by adjusting the sending interval. Through the above steps, the proposed algorithm can mitigate the TCP incast effectively. The results, by using extensive ns3 simulation, show that TS-TCP can significantly outperform the DCTCP and TCP NewReno in terms of throughput and flow completion time. Moreover, TS-TCP can support hundreds of concurrent flows while maintaining about 85\% link utilization.

The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

Numerous scheduling approaches have been proposed to improve user experiences in a data center network (DCN) by reducing flow completion time (FCT). Mimicking the shortest job first (SJF) has been proved to be the prominent way to improve FCT. To do so, some approaches require flow size or completion time information in advance, which is not possible in scenarios like HTTP chunk transfer or database query response. Some information-agnostic schemes require involving end-hosts for counting the number of bytes sent. We present Longer Stay Less Priority (LSLP), an information-agnostic flow scheduling scheme, like Multi-Level Feedback Queue (MLFQ) scheduler in operating systems, that aims to mimic SJF using P4 switches in a DCN. LSLP considers all the flows as short flows initially and assigns them to the highest priority queue, and flows get demoted to the lower priority queues over time. LSLP estimates the active time of a flow by leveraging the state-of-the-art P4 switch’s programmable nature. LSLP estimates the active time of a group of new flows that arrive during a time interval and assigns their packets to the highest priority. At the beginning of the next time interval, arriving packets of old flows are placed one priority lower except for those already in the lowest priority queue. Therefore, short flows can be completed in the few higher priority queues while long flows are demoted to lower priority queues. We have evaluated LSLP via a series of tests and shown that its performance is comparable to the existing scheduling schemes.

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

Data centers (DCs), owing to the exponential growth of Internet services, have emerged as an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically houses a large number of computing and storage nodes, interconnected by a specially designed network, namely, DC network (DCN). The DCN serves as a communication backbone and plays a pivotal role in optimizing DC operations. However, compared to the traditional network, the unique requirements in the DCN, for example, large scale, vast application diversity, high power density, and high reliability, pose significant challenges to its infrastructure and operations. We have observed from the premium publication venues (e.g., journals and system conferences) that increasing research efforts are being devoted to optimize the design and operations of the DCN. In this paper, we aim to present a systematic taxonomy and survey of recent research efforts on the DCN. Specifically, we propose to classify these research efforts into two areas: 1) DCN infrastructure and 2) DCN operations. For the former aspect, we review and compare the list of transmission technologies and network topologies used or proposed in the DCN infrastructure. For the latter aspect, we summarize the existing traffic control techniques in the DCN operations, and survey optimization methods to achieve diverse operational objectives, including high network utilization, fair bandwidth sharing, low service latency, low energy consumption, high resiliency, and etc., for efficient DC operations. We finally conclude this survey by envisioning a few open research opportunities in DCN infrastructure and operations.