Optimal Resource Allocation in Clouds

Chang, Fangzhe; Ren, Jennifer; Viswanathan, Ramesh

doi:10.1109/cloud.2010.38

Cited by 92 publications

(38 citation statements)

References 19 publications

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“…The problem of determining the optimal set of resources of various types with multiplicity been shown to be NP-Complete by Chang et al [8]. They also present an approximation algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Auto-scaling Controller for Cloud Based Applications

Londoño-Peláez¹,

Florez-Samur²

2013

IJACSA

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Abstract-One of the key promises of Cloud Computing is elasticity -applications have at their disposal a very large pool of resources from which they can allocate whatever they need. For any fair-size application the amount of resources is significant and both overprovisioning and under provisioning have a negative impact on the customer. In the first case it leads to over costs and in the second case to poor application performance with negative business repercussions as well. It is then an important problem to provision resources appropriately.In addition, it is well known that application workloads exhibit high variability over short time periods. This creates the necessity of having autonomic mechanisms that make resource management decisions in real time and optimizing both cost and performance. To address these problems we present and autonomic auto-scaling controller that based on the stream of measurements from the system maintains the optimal number of resources and responds efficiently to workload variations, without incurring in over costs for high churn of resources or short duration peaks in the workload.To evaluate the performance of our system we conducted extensive evaluations based on traces of real applications deployed in the cloud. Our results show significant improvements over existing techniques.

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“…The problem of determining the optimal set of resources of various types with multiplicity been shown to be NP-Complete by Chang et al [8]. They also present an approximation algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Auto-scaling Controller for Cloud Based Applications

Londoño-Peláez¹,

Florez-Samur²

2013

IJACSA

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show abstract

“…Although, the cpu utilization of the cluster is only 75% with 4 unused cores, and only 62,5% of memory that equals to 12 GB of unused memory, scientist A is unable to run his application as there are no free machines. Better assignment of these resources could be done using a virtualization, where each scientist could be assigned a perfect-fit size virtual machines [21]. Not only that a resource utilization would improve, it would also improve resource assignment and lower the waiting queues.…”

Section: Use Casesmentioning

confidence: 99%

TimeCap: Methodology for Comparing IT Infrastructures Based on Time and Capacity Metrics

Mastelić

Brandić

2013

2013 IEEE Sixth International Conference on Cloud Computing

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Abstract-Scientific community is one of the major driving forces for developing and utilizing IT technologies such as Supercomputers and Grid. Although, the main race has always been for bigger and faster infrastructures, an easier access to such infrastructures in recent years created a demand for more customizable and scalable environments. However, introducing new technologies and paradigms such as Cloud computing requires a comprehensive analysis of its benefits before the actual implementation.In this paper we introduce the TimeCap, a methodology for comparing IT infrastructures based on time requirements and resource capacity wastage. We go beyond comparing just the execution time by introducing the Time Complexity as part of TimeCap, a methodology used for comparing arbitrary time related tasks required for completing a procedure, i.e., obtaining the scientific results. Moreover, a resource capacity wastage is compared using the Discrete Capacity, a second methodology as part of TimeCap used for analyzing resource assignment and utilization. We evaluate our methodology by comparing a traditional physical infrastructure and a Cloud infrastructure using our local IT resources. We use a real world scientific application for calculating plasma instabilities for analyzing time and capacity required for computation.

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“…Mazzucco et al [12], addressed the problem of optimal allocation for maximizing the revenues of Cloud providers by minimizing the amount of consumed electricity. In [4], a resource allocation problem is formulated in which later tasks can reuse resources released by earlier tasks, and an approximation algorithm that can yield close to optimum solutions in polynomial time is presented. In all these works, the objectives are profit-driven, whereas in this paper, the presented optimization model combines a more complete set of parameters both on the SP and the IP side, such as the trustworthiness of the SP that owns the service and the ecoefficiency of the IP's resources.…”

Section: Related Workmentioning

confidence: 99%

Admission Control for Elastic Cloud Services

Konstanteli

Cucinotta

Psychas

et al. 2012

2012 IEEE Fifth International Conference on Cloud Computing

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Abstract-This paper presents an admission control test for deciding whether or not it is worth to admit a set of services into a Cloud, and in case of acceptance, obtain the optimum allocation for each of the components that comprise the services. In the proposed model, the focus is on hosting elastic services the resource requirements of which may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. In finding the optimum allocation, the presented admission control test uses an optimization model, which incorporates business rules in terms of trust, eco-efficiency and cost, and also takes into account affinity rules the components that comprise the service may have. The problem is modeled on the General Algebraic Modeling System (GAMS) and solved under realistic provider's settings that demonstrate the efficiency of the proposed method.

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Optimal Resource Allocation in Clouds

Cited by 92 publications

References 19 publications

An Autonomic Auto-scaling Controller for Cloud Based Applications

An Autonomic Auto-scaling Controller for Cloud Based Applications

TimeCap: Methodology for Comparing IT Infrastructures Based on Time and Capacity Metrics

Admission Control for Elastic Cloud Services

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