Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Kaur, Amanpreet; Kaur, Bikrampal; Singh, Dheerendra

doi:10.5815/ijieeb.2017.01.04

Cited by 15 publications

(12 citation statements)

References 12 publications

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“…As cloud is getting increasingly used in production environments, a considerable body of research is dealing with cost optimisation and SLA fulfilment in cloud environments, including different strategies for achieving the goal of cost minimisation. Some of the most common approaches include optimisation of load balancing [7] and service scaling algorithms [8], time scheduling of task invocation and performing [9,10], as well as the strategy of optimising service placement with the goal of lowering its execution cost [11,12]. Various approaches to optimisation of cloud cost are using techniques from operations research and in particular game theory [13], meta-heuristics [14,15] with the focus on genetic and other evolutionary algorithms [9,16].…”

Section: Related Workmentioning

confidence: 99%

Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Stupar

Huljenić

2021

Preprint

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Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

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Section: Related Workmentioning

confidence: 99%

Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Stupar

Huljenić

2021

Preprint

View full text Add to dashboard Cite

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“…A body of research is dealing with cost optimisation in cloud environments, including different strategies for achieving the goal of cost minimisation. Some of the most common approaches include optimisation of load-balancing [6] and service scaling algorithms [7], time scheduling of task invocation and performing [8,9], and the strategy of optimising service placement for lowering its execution cost [10,11]. Various approaches to optimisation of cloud cost are using techniques from operations research and in particular game theory [12] and metaheuristics [13,14] with the focus on genetic and other evolutionary algorithms [8,15].…”

Section: Related Workmentioning

confidence: 99%

“…Research approaches in the area of cloud cost optimisation are also divided by the perspective of optimisation. As observed through literature review and several surveys [6,13,14,[26][27][28], many resource scheduling solutions and optimisation algorithms are intended for infrastructure service providers, dealing with the optimisation of resource allocation in data centres [29][30][31][32]. Fewer solutions are provided for optimisation of services for SaaS providers, who often need a way to evaluate if the placement of the service in a cloud environment is optimal from the perspective of execution cost and quality of service.…”

Section: Related Workmentioning

confidence: 99%

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Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

Stupar

Huljenić

2019

Scientific Programming

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With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

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“…This combination allows multifaceted and dynamic aspects of cloud computing and requires efficient and optimized techniques for resource provisioning and load balancing. For this purpose, cloud monitoring is required to identifying overutilized and underutilized of PMs which hosting VMs [14].…”

Section: Introductionmentioning

confidence: 99%

Phoenix: A Framework to Support Transient Overloads on Cloud Computing Environments

Bernardo¹,

Pinheiro²,

Pinto³

2018

IJITCS

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Abstract-This paper aims to present a computational framework capable of withstanding the effects produced by transient overloads on physical and virtual servers hosted on cloud computing environment. The proposed framework aims at automating management of virtual machines that are hosted in this environment, combining a proactive strategy, which performs load balancing when there is not overload of physical and/or virtual machines with a reactive strategy, which is triggered in the event of overload in these machines. On both strategies, it is observed the service level agreement (SLA) established for each hosted service according to the infrastructure as a service (IaaS) model. The main contribution of this paper is the implementation of a computational framework called Phoenix, capable of handling momentary overloads, considering the CPU, memory and network resources of physical and virtual machines and guaranteeing SLAs. The results demonstrate that Phoenix framework is effective, and it has outstanding performance in handling overloads virtual machine network, which has achieved the isolation of momentary overload on the physical machine preventing the propagation of their effects on the cloud.

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Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Cited by 15 publications

References 12 publications

Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

Phoenix: A Framework to Support Transient Overloads on Cloud Computing Environments

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