DAQ: Deadline-Aware Queue scheme for scheduling service flows in data centers

Ding, Cong; Rojas‐Cessa, Roberto

doi:10.1109/icc.2014.6883779

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…If the elephant flow is transmitted through a high-priority queue, mice flows will be blocked, resulting in longer FCT for the mice flows. In [43], a deadline-aware queue scheme is proposed. It establishes three priority queues and schedules flows by sensing the deadline.…”

Section: Priority Schedulingmentioning

confidence: 99%

HPSTOS: High-Performance and Scalable Traffic Optimization Strategy for Mixed Flows in Data Center Networks

Liu

Wang

2022

IEEE Trans. Cloud Comput.

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In data center networks, traffic needs to be distributed among different paths using traffic optimization strategies for mixed flows. Most of the existing strategies consider either distributed or centralized mechanisms to optimize the latency of mice flows or the throughput of elephant flows. However, low network performance and scalability issues are intrinsic limitations of both strategies. In addition, the current elephant flow detection methods are inefficient. In this paper, we propose a high-performance and scalable traffic optimization strategy (HPSTOS) based on a hybrid approach that leverages the advantages of both centralized and distributed mechanisms. HPSTOS improves the efficiency of elephant flow detection through sampling and flow-table identification. HPSTOS guarantees preferential transmission of mice flows using priority scheduling and adjusts their transmission rate by coding-based congestion control on the end-host, reducing their latency. Additionally, HPSTOS schedules elephant flows by cost-aware dynamic flow scheduling on a centralized controller to improve their throughput. The controller handles only elephant flows, which constitutes the minority of the flows, allowing effective scalability. Evaluations show that HPSTOS outperforms existing schemes by realizing efficient elephant flow detection and improving network performance and scalability.

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Section: Priority Schedulingmentioning

confidence: 99%

HPSTOS: High-Performance and Scalable Traffic Optimization Strategy for Mixed Flows in Data Center Networks

Liu

Wang

2022

IEEE Trans. Cloud Comput.

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“…Table VI provides a summary of this section. Some recent window-based approaches include DCTCP [29], D2TCP [28], L2DCT [120], MCP [121], DAQ [122], and PASE [123]. DCTCP uses explicit congestion signals from switches that is piggybacked on ACKs to change the window size according to the extent of congestion.…”

Section: A Transmission Controlmentioning

confidence: 99%

“…A simple solution is to use weighted queuing instead of strict prioritization. For instance, DAQ [122] uses a weighted round-robin between long and short flows to make sure that throughput-oriented flows keep making progress. Aging can also be used to address starvation while minimally affecting critical flows.…”

Section: Enforcement Strictmentioning

confidence: 99%

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Noormohammadpour¹,

Raghavendra²

2017

Preprint

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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.

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“…Long flows, or background flows, have a size of several megabytes and they carry the data for the operation and maintenance of the data center. The time required to transmit a flow is referred to as the Flow Completion Time (FCT) [8,10]. This parameter is a major metric for measuring the performance of the transport of short flows in data center networks (DCNs).…”

Section: Introductionmentioning

confidence: 99%

Packet-Based Load Balancing in Data Center Networks

Kaymak

Rojas‐Cessa

2016

JCSM

Self Cite

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In this paper, we evaluate the performance of packet-based load balancing in data center networks (DCNs). Throughput and flow completion time are some of the main the metrics considered to evaluate the performance of the transport of flows over the presence of long flows in a DCN. Load balancing in a DCN may improve those metrics but it may also generate out-of-order packet forwarding. Therefore, we investigate the impact of outof-order packet delivery on the throughput and flow completion time of long and short flows, respectively, in a DCN. We focus on per-packet load balancing. Our simulations show the presence of out-of-order packet delivery in a DCN using this load balancing approach. Simulation results also reveal that packetbased load balancing may yield smaller average flow completion time for short flows and larger average throughput for long flows than the single-path transport model used by Transmission Control Protocol (TCP), which prevents the presence of out-of-order packet delivery. Queueing diversity in the multipath structure of DCNs promotes susceptibility of out-of-order delivery. As the delay difference between alternative paths decreases, the occurrence of out-of-order packet delivery in packet-based load balancing also decreases. Therefore, under the studied scenarios, the benefits of the packet-based load balancing seem to outweigh the out-of-order problem.

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DAQ: Deadline-Aware Queue scheme for scheduling service flows in data centers

Cited by 11 publications

References 17 publications

HPSTOS: High-Performance and Scalable Traffic Optimization Strategy for Mixed Flows in Data Center Networks

HPSTOS: High-Performance and Scalable Traffic Optimization Strategy for Mixed Flows in Data Center Networks

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Packet-Based Load Balancing in Data Center Networks

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