Virtualization based cloud capacity prediction

Mallick, Sayanta; Hains, Gaétan

doi:10.1109/hpcsim.2011.5999920

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The basis of this approach lies in the existence of prediction models that are able to forecast resource usages using historical monitoring data. Indeed, there are several works in the literature, such as [30], that produce statistical information in the form of probability distributions of the resource requirements experienced at run-time by a service in virtualized environment. Given that such statistical knowledge is known, then it can be leveraged inside the problem to employ an over-allocation strategy ensuring the service can run flawlessly with at least a minimum probability φ s (see Section 3.5).…”

Section: Probabilistic Elasticitymentioning

confidence: 99%

Elastic Admission Control for Federated Cloud Services

Konstanteli

Cucinotta²,

Psychas

et al. 2014

IEEE Trans. Cloud Comput.

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This paper presents a technique for admission control of a set of horizontally scalable services, and their optimal placement, into a federated Cloud environment. In the proposed model, the focus is on hosting elastic services whose resource requirements may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. The request may also be partially accommodated in federated external providers, if needed or more convenient. In finding the optimum allocation, the presented mechanism uses a probabilistic optimization model, which takes into account eco-efficiency and cost, as well as affinity and anti-affinity rules possibly in place for the components that comprise the services. In addition to modelling and solving the exact optimization problem, we also introduce a heuristic solver that exhibits a reduced complexity and solving time. We show evaluation results for the proposed technique under various scenarios.

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Section: Probabilistic Elasticitymentioning

confidence: 99%

Elastic Admission Control for Federated Cloud Services

Konstanteli

Cucinotta²,

Psychas

et al. 2014

IEEE Trans. Cloud Comput.

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“…The basis of this approach lies in the existence of prediction models that are able to forecast resource usages based on historical monitoring data. Indeed, there are several works to this direction in the literature, such as [11], that produce statistical information in the form of probability distributions of the actual resource requirements experienced at run-time by a service in a virtualized environment by monitoring previous runs of the service. Therefore, given that this statistical knowledge is known, then it can be leveraged inside the problem to tune the allocation in such a way that the service can run flawlessly with at least a minimum probability φ s , which is the minimum probability that there will be sufficient resources for the activation of the VMs when needed as expressed in the SLA (see Section III-D).…”

Section: E Probabilistic Elasticitymentioning

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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