Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

Alsarhan, Ayoub; Al-Khasawneh, Ahmad

doi:10.1080/17445760.2015.1057589

Cited by 14 publications

(2 citation statements)

References 35 publications

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“…It was failed to reduce the BUR. A novel auction‐based scheme was developed in Alsarhan for operating free processors in CC environment. It does not improve the RT for trading processors.…”

Section: Related Workmentioning

confidence: 99%

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Gokulraj¹,

Geetha²

2018

Int J Communication

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Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation-based firefly resource cost optimization (PSC-FRCO) technique is designed. Pearson service correlation-based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation-based firefly resource cost optimization technique initially uses Pearson service correlation in which the user-required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost-optimized cloud resources to users to afford required service from the cloud server. Thus, PSC-FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC-FRCO technique enhances enhances RUE and lessens RT as compared to state-of-the-art works.

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Section: Related Workmentioning

confidence: 99%

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Gokulraj¹,

Geetha²

2018

Int J Communication

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“…However, the QoS for clients depends on the allocated resources. A CP may trade anything from infrastructure [1,2,3] such as processors, memory, and Internet access. Despite many studies found in the literature under the umbrella of cloud computing, resource management in multiservice environments is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

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Utilizing an Agent-Based Auction Protocol for Resource Allocation and Load Balancing in Grid Computing

2022

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Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

Cited by 14 publications

References 35 publications

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Utilizing an Agent-Based Auction Protocol for Resource Allocation and Load Balancing in Grid Computing

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