Product Form Queueing Networks

Balsamo, Simonetta

doi:10.1007/3-540-46506-5_16

Cited by 22 publications

(15 citation statements)

References 60 publications

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“…While product-form QNs must satisfy some constraints which somewhat reduce their accuracy in modeling real-world systems (see [15] for details), we argue that they are still accurate enough for guiding the process of identifying new configurations. Also, we remark that the accuracy of a performance model is both related to its expressiveness (how precisely the model can capture the actual system behavior), and to the accuracy of the input parameters which are used to evaluate the model.…”

Section: System Modelingmentioning

confidence: 96%

“…In particular, the controller needs a fast way to analyze the impact of different configurations on the system response time; this rules out approaches based on "trial and error", in which some state change is attempted directly on the system. Instead, we use a product-form QN performance model [15], which possess efficient solution techniques, yet being expressive enough to accurately model many kinds of systems. We propose a simple greedy heuristic for identifying an approximate solution to the optimization problem (2) by evaluating a limited number of alternatives.…”

Section: Easy Architecturementioning

confidence: 99%

“…In particular, a special class of QNs, called productform QNs, have a simple closed form expression of the stationary state distribution, so that average performance measures can be computed efficiently [15]. In order to make this paper self-contained, we included additional details on QNs in B.…”

Section: System Modelingmentioning

confidence: 99%

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Dynamic power management for QoS-aware applications

Marzolla

Mirandola

2013

Sustainable Computing: Informatics and Systems

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Section: System Modelingmentioning

confidence: 96%

Section: Easy Architecturementioning

confidence: 99%

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Dynamic power management for QoS-aware applications

Marzolla

Mirandola

2013

Sustainable Computing: Informatics and Systems

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“…The PS service discipline closely approximates the scheduling policies of actual servers, where requests are processed in round-robin fashion each receiving service for a small quantum of time. Under these assumptions, the QN belongs to the class of BCMP networks which have product-form solution [Baskett et al 1975, Balsamo 2000, which basically means that average performance measures (response times, utilizations and so on) can be computed efficiently. This is important because the model must be analyzed at run-time, so we favor computational efficiency over model accuracy.…”

Section: The Plannermentioning

confidence: 99%

Dynamic resource provisioning for cloud-based gaming infrastructures

2012

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Modern massively multiplayer online games (MMOGs) allow hundreds of thousands of players to interact with a large, dynamic virtual world. Implementing a scalable MMOG service is challenging because the system is subject to high workload variability, but nevertheless must always operate under very strict quality of service (QoS) requirements. Traditionally, MMOG services are implemented as large dedicated IT infrastructures with aggressive over-provisioning of resources in order to cope with the worst-case workload scenario. In this article we address the problem of building a large-scale, multitier MMOG service using resources provided by a Cloud computing infrastructure. The Cloud paradigm allows customers to request as many resources as they need using a pay-as-you-go model. We harness this paradigm by proposing a dynamic provisioning algorithm, which can resize the resource pool of a MMOG service to adapt to workload variability and maintain a response time below a given threshold. We use a queuing network performance model to quickly estimate the system response time for different configurations. The performance model is used within a greedy algorithm to compute the minimum number of servers to be allocated on each tier in order to satisfy the system response time constraint. Numerical experiments are used to validate the effectiveness of the proposed approach.

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“…Queuing network models have been extensively applied in many areas and have proven their efficiency in representing and analyzing resource-sharing systems such as computer systems [3]. According to Kendall's Notation, the first M in M/M/m queuing model represents arrival process, which is Markov arrival process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Elasticity of SaaS Applications using Queuing Theory

Shahin¹

2017

ijacsa

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Abstract-Elasticity is one of key features of cloud computing. Elasticity allows Software as a Service (SaaS) applications' provider to reduce cost of running applications. In large SaaS applications that are developed using service-oriented architecture model, each service is deployed in a separated virtual machine and may use one or more services to complete its task. Although, scaling service independently from its required services propagates scaling problem to other services, most of current elasticity approaches do not consider functional dependencies between services, which increases the probability of violating service level agreement. In this paper, architecture of SaaS application is modeled as multi-class M/M/m processor sharing queuing model with deadline to take into account functional dependencies between services during estimating required scaling resources. Experimental results show effectiveness of the proposed model in estimating required resources during scaling virtual resources.

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Product Form Queueing Networks

Cited by 22 publications

References 60 publications

Dynamic power management for QoS-aware applications

Dynamic power management for QoS-aware applications

Dynamic resource provisioning for cloud-based gaming infrastructures

Enhancing Elasticity of SaaS Applications using Queuing Theory

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