On maximizing service-level-agreement profits

Liu, Zhen; Squillante, Mark S.; Wolf, Joel L.

doi:10.1145/501158.501185

Cited by 155 publications

(116 citation statements)

References 14 publications

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“…Some of the previous works consider probabilistic SLA constraints with violation penalty, e.g., [75]- [77]. Other works consider utility function-based SLA [78] [79].…”

Section: B Overview Of Related Workmentioning

confidence: 99%

Energy Efficient Data Centers

Geet¹

2012

Lecture Notes in Computer Science

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Abstract-Pervasive use of cloud computing and the resulting rise in the number of datacenters and hosting centers (which provide platform or software services to clients who do not have the means to set up and operate their own compute facilities) have brought forth many concerns including the electrical energy cost, peak power dissipation, cooling, carbon emission, etc. With power consumption becoming an increasingly important issue for the operation and maintenance of the hosting centers, corporate and business owners are becoming increasingly concerned. Furthermore, provisioning resources in a cost-optimal manner so as to meet different performance criteria such as throughput or response time has become a critical challenge. The goal of this paper is to provide an introduction to resource provisioning and power/thermal management problems in datacenters and to review strategies that maximize the datacenter energy efficiency subject to peak/total power consumption and thermal constraints while at the same time meeting stipulated service level agreements in terms of task throughput and/or response time.

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“…Some of the previous works consider probabilistic SLA constraints with violation penalty, e.g., [75]- [77]. Other works consider utility function-based SLA [78] [79].…”

Section: B Overview Of Related Workmentioning

confidence: 99%

Energy Efficient Data Centers

Geet¹

2012

Lecture Notes in Computer Science

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“…Being able to make 'what-if' response time predictions for potential allocations of enterprise workload to resources, can enhance automatic enterprise 'workload to resources' allocation systems [1,2]. For example, these predictions can help provide more cost-effective response time-based Service Level Agreement (SLA) management [22].…”

Section: Introductionmentioning

confidence: 99%

“…It is differentiated from other historical modelling work by its focus on simplifying the process of analysing any historical data so as to extract the small number of trends that will be most useful to a management system. Examples of the queuing modelling approach include [7,8,2]. The layered queuing method [9] is of particular interest and will be examined further in this paper as it explicitly models the tiers of servers found in this class of application, and it has been applied to a range of distributed systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

Bacigalupo

Hemert

Chen

et al. 2011

Simulation Modelling Practice and Theory

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The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic − urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management.

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“…In [6] the authors describe a method to manage web server resources based on maximizing revenue. In [7] the authors consider maximizing SLA profits for web server farms. However, all of the above approaches require detailed knowledge of the system being optimized (e.g., in order to construct queueing models).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Quality of Service Using Fuzzy Control

Diao

Hellerstein

Parekh

2002

Management Technologies for E-Commerce and E-Business Applications

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Abstract. The rapid growth of eCommerce increasingly means business revenues depend on providing good quality of service (QoS) for web site interactions. Traditionally, system administrators have been responsible for optimizing tuning parameters, a process that is time-consuming and skills-intensive, and therefore high cost. This paper describes an approach to automating parameter tuning using a fuzzy controller that employs rules incorporating qualitative knowledge of the effect of tuning parameters. An example of such qualitative knowledge in the Apache web server is "MaxClients has a concave upward effect on response times." Our studies using a real Apache web server suggest that such a scheme can improve performance without human intervention. Further, we show that the controller can automatically adapt to changes in workloads.

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On maximizing service-level-agreement profits

Cited by 155 publications

References 14 publications

Energy Efficient Data Centers

Energy Efficient Data Centers

Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

Optimizing Quality of Service Using Fuzzy Control

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