Revenue Maximization Using Adaptive Resource Provisioning in Cloud Computing Environments

Feng, Gao; Garg, Saurabh; Buyya, Rajkumar; Li, Wenzhong

doi:10.1109/grid.2012.16

Cited by 56 publications

(22 citation statements)

References 13 publications

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“…Hence, building such a model of relations at the application-level can greatly improve application and service-level controllers. This is an area of particular relevance for other computation models as well, such as Cloud computing (Emeakaroha et al, 2011;Sironi et al, 2012;Yoo and Kim, 2013;Feng et al, 2012), Virtualization (Weng et al, 2011), and Map Reduce (Ibrahim et al, 2011), (Polo et al, 2011), where advanced resource and activity aware adaptive schedulers can be equipped with services activity and resources decomposition methods for better SLA and revenue awareness. An application level control and management is a widely adopted approach (Katchabaw et al, 1999;Chen et al, 2002), where control can be also done on a service level directly, with use of deeper instrumentation of the application running in ASM environment.…”

Section: Industry Practicementioning

confidence: 99%

Evolutionary approaches to signal decomposition in an application service management system

Sikora

Magoulas

2015

Soft Comput

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The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a-priori. In our approach, the Application Service Management System is treated as a black-or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queuebased computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.

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Section: Industry Practicementioning

confidence: 99%

Evolutionary approaches to signal decomposition in an application service management system

Sikora

Magoulas

2015

Soft Comput

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“…Guofu Feng et al [27] addresses the issue of amplifying the supplier's income through SLA-based dynamic asset designation as SLA assumes an essential part in distributed computing to connect specialist organizations and clients. Creator formalizes the asset portion issue thinking about different Quality of Service (QoS) parameters.…”

Section: G Adaptive Based Resource Provisioningmentioning

confidence: 99%

“…It isn't a simple errand to change the client arranged administration measurements in to working level measurements, and control the cloud assets adaptively in light of Service Level Agreement (SLA) [27].…”

Section: G Adaptive Based Resource Provisioningmentioning

confidence: 99%

Investigating Methods of Resource Provisioning Mechanisms in Cloud: A Review

Malik¹,

Anwar²,

Ilyas³

et al. 2018

ijacsa

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Abstract-Delivering information through cloud computing become a modern computation. For this purpose, electronic device is required to access with an active web server. For delivering different resources, the cloud supplier provides computing power for the cloud users to organize their multiple type of application at any time on different platforms. In cloud computing, the main drawback is relevant to the best use of resources as well as resource provisioning. In cloud computing there is a lack of desired resources that is why the cloud resource provision becomes a daring work. To maintain the quality of services, the provisioning of reasonable resources is need of workloads. The main problem is to find the appropriate workload that depends on the cloud user that is related to resource pair application requirements. This paper reveals the cloud resource provisioning and identification in general and in specific, respectively. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource provisioning, different types of resource provisioning mechanisms and their comparisons, and benefits are described.

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“…Maximizing the revenue from infrastructure operation is an important objective of Cloud providers, analyzed in [14] based on SLA relationships. This work does not take into account the possible energy savings that may further help in reducing the costs.…”

Section: Related Workmentioning

confidence: 99%

An architecture to stimulate behavioral development of academic cloud users

Kecskeméti

Ostermann

Prodan

2014

Sustainable Computing: Informatics and Systems

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Academic cloud infrastructures are constructed and maintained so they minimally constrain their users. Since they are free and do not limit usage patterns, academics developed such behavior that jeopardizes fair and flexible resource provisioning. For efficiency, related work either explicitly limits user access to resources, or introduce automatic rationing techniques. Surprisingly, the root cause (i.e., the user behavior) is disregarded by these approaches. This article compares academic cloud user behavior to its commercial equivalent. We deduce, that academics should behave like commercial cloud users to relieve resource provisioning. To encourage commercial like behavior, we propose an architectural extension to existing academic infrastructure clouds. First, every user's energy consumption and efficiency is monitored. Then, energy efficiency based leader boards are used to ignite competition between academics and reveal their worst practices. Leader boards are not sufficient to completely change user behavior. Thus, we introduce engaging options that encourage academics to delay resource requests and prefer resources more suitable for the infrastructure's internal provisioning. Finally, we evaluate our extensions via a simulation using real life academic resource request traces. We show a potential resource utilization reduction (by the factor of at most 2.6) while maintaining the unlimited nature of academic clouds.

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Revenue Maximization Using Adaptive Resource Provisioning in Cloud Computing Environments

Cited by 56 publications

References 13 publications

Evolutionary approaches to signal decomposition in an application service management system

Evolutionary approaches to signal decomposition in an application service management system

Investigating Methods of Resource Provisioning Mechanisms in Cloud: A Review

An architecture to stimulate behavioral development of academic cloud users

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