A Study of the Impact of Network Traffic Pacing from Network and End-User Perspectives

Cai, Yan; Hanay, Y. Sinan; Wolf, Tilman

doi:10.1109/icccn.2011.6006018

Cited by 4 publications

(11 citation statements)

References 23 publications

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“…Note that in this article we provide an optimal algorithm for the allocation problem but with limited scalability. (4) We evaluate application mixes with over 100 parallel applications of five common use cases in a proof-of-concept set-up. The results show how pacing can improve delay and packet loss at bottlenecks and can significantly increase inter-application fairness in terms of utility.…”

Section: Contributionsmentioning

confidence: 99%

“…The reduced packet loss, as a consequence of the decreased burstiness, results in a nearly fully link utilization when using the proposed solution. The QLBP algorithm is also applied in Reference [4] to study the impact of pacing on different network traffic conditions. The authors conclude that pacing is especially beneficial in networks with small buffers, where packet loss, as a result from bursty traffic, can significantly reduce network performance.…”

Section: Benefits and Drawbacks Of Pacingmentioning

confidence: 99%

“…The authors only investigated response time values up to 0.5 s. We linearly extrapolate the results up to 1.2 s where the utility reaches 1. We define M as M (SSH) (t ) := MOS (SSH) (t ) − 1) • 4 3.3 + 1 to project the MOS values to a utility range of [1,5].…”

Section: Remote Terminal Workmentioning

confidence: 99%

“…Overview over the challenges and the proposed solution toward scalable application-aware resource allocation in enterprise networks. Based on the set of applications (1), resources (2) and application utility functions (3), the allocation problem solver maximizes the minimum and total utility over all active applications (4). As a result, delay-constrained flow routing (5) and application pacing rates (6) are implemented by a network controller (7) in the network (8) and on the end-hosts (9).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing

Sieber

Schwarzmann

Blenk

et al. 2020

ACM Trans. Model. Perform. Eval. Comput. Syst.

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Providing scalable user-and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem. The main challenges are as follows: (i) How do we define user-and application-aware shares of resources? (ii) How do we determine an allocation of shares of network resources to applications? (iii) How do we allocate the shares per application in heterogeneous networks at scale? In this article, we propose solutions to the three challenges and introduce a system design for enterprise deployment. Defining the necessary resource shares per application is hard, as the intended use case, the user's environment, e.g., big or small display, and the user's preferences influence the resource demand. We tackle the challenge by associating application flows with utility functions from subjective user experience models, selected Key Performance Indicators, and measurements. The specific utility functions then enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale. A sensible distribution of the resources is determined by formulating a multi-objective mixed integer linear program to solve the throughput-and delay-aware embedding of each utility function in the network for a max-min fairness criteria. The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes and interacts badly with congestion control algorithms. We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts. The system design is evaluated against best effort networks in a proof-of-concept setup for scenarios with increasing number of parallel applications competing for the throughput of a constrained link. The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP. The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.

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Section: Contributionsmentioning

confidence: 99%

Section: Benefits and Drawbacks Of Pacingmentioning

confidence: 99%

Section: Remote Terminal Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing

Sieber

Schwarzmann

Blenk

et al. 2020

ACM Trans. Model. Perform. Eval. Comput. Syst.

View full text Add to dashboard Cite

show abstract

“…Moreover, the underlying assumption for BJP to work efficiently is to have the incoming traffic paced before the ingress ports to some extent [22]. QLBP does not require such an assumption to be made about the incoming traffic and it has been shown to work effectively under realistic traffic with short-lived flows [23].…”

Section: Related Workmentioning

confidence: 99%