WebQ: A virtual queue for improving user experience during web server overload

Doshi, Bhavin; Kumar, Chandan; Piyush, Pulkit; Vutukuru, Mythili

doi:10.1109/iwqos.2015.7404723

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…1) Evolution of system configuration within a single deferral stage: Deferral stages begin in a configuration of the form (k, d), where 1 ≤ k ≤ K, and 1 ≤ d < D. 3 Subsequently for duration x, the system configuration evolves as per the Markov chain depicted in Figure 4. The transition rate matrix corresponding to this chain is given by…”

Section: Multiple-servers Multiple-deferrals Systemmentioning

confidence: 99%

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“Please Come Back Later”: Benefiting from Deferrals in Service Systems

Kagrecha

Nair

2020

2020 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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The performance evaluation of loss service systems, where customers who cannot be served upon arrival get dropped, has a long history going back to the classical Erlang B model. In this paper, we consider the performance benefits arising from the possibility of deferring customers who cannot be served upon arrival. Specifically, we consider an Erlang B type loss system where the system operator can, subject to certain constraints, ask a customer arriving when all servers are busy, to come back at a specified time in the future. If the system is still fully loaded when the deferred customer returns, she gets dropped for good. For such a system, we ask: How should the system operator determine the 'rearrival' times of the deferred customers based on the state of the system (which includes those customers already deferred and yet to arrive)? How does one quantify the performance benefit of such a deferral policy?Our contributions are as follows. We propose a simple statedependent policy for determining the rearrival times of deferred customers. For this policy, we characterize the long run fraction of customers dropped. We also analyse a relaxation where the deferral times are bounded in expectation. Via extensive numerical evaluations, we demonstrate the superiority of the proposed state-dependent policies over naive state-independent deferral policies.

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Section: Multiple-servers Multiple-deferrals Systemmentioning

confidence: 99%

“…Related literature: This work is motivated by the recent work [3], which implemented a deferral system for a webserver that is prone to congestion. To the best of our knowledge, there is no prior work that provides an analytical treatment of queueing systems that allow deferral.…”

Section: Introductionmentioning

confidence: 99%

“Please Come Back Later”: Benefiting from Deferrals in Service Systems

Kagrecha

Nair

2020

2020 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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“…These 2 indicators, CPUs and Cores, are proportional to processing potential, i.e., the higher they are, the larger the server's capacity. Although these indicators seem to be a good way to determine servers capacity, neither the academy nor the specialists in the area can, exactly, know how to determine the server capacity (Kant and Won, 1999;Doshi et al, 2015). The number of simultaneous users, buffer cache correction, systems standby rates and other metrics could also be used to determine capacity (Choi et al, 2015).…”

Section: Determine the Processing Capacity Of Each Servermentioning

confidence: 99%

Sistemática para o controle gerencial dos custos de Tecnologia da Informação

Andriotti¹,

Filomena

Neto

et al. 2018

BASE

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“…Entre essas responsabilidades encontra-se o balanceamento de carga [Bourke 2001], [Cardellini et al 1999]. De acordo com [Doshi et al 2015], o caso de atraso que consequentemente mais afeta a QoE do usuárioé o congestionamento do servidor, poisé percebido pelo usuário como interrupção e ruído na imagem eáudio do conteúdo.…”

Section: Introductionunclassified

Balanceamento de Tráfego entre Servidores de Vídeo MPEG-DASH em Redes Definidas por Software

Passos¹,

Fiorese²

2019

Anais Do Workshop De Gerência E Operação De Redes E Serviços

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With popularization of video streaming service in recent years, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. However, with the limitation of the TCP/IP network structure, the end user quality of experience may be affected. This is due to several factors such as network congestion, bandwidth limitation, non-optimal choice of a content provider server and server overload. This article presents a load-balancing solution between MPEG-DASH video servers based on Software Defined Networks, using as a balancing metric the throughput of the content server.

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WebQ: A virtual queue for improving user experience during web server overload

Cited by 4 publications

References 8 publications

“Please Come Back Later”: Benefiting from Deferrals in Service Systems

“Please Come Back Later”: Benefiting from Deferrals in Service Systems

Sistemática para o controle gerencial dos custos de Tecnologia da Informação

Balanceamento de Tráfego entre Servidores de Vídeo MPEG-DASH em Redes Definidas por Software

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