A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation

Ficco, Massimo; Esposito, Christian; Palmieri, Francesco; Castiglione, Aniello

doi:10.1016/j.future.2016.05.025

Cited by 65 publications

(16 citation statements)

References 31 publications

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“…In turn, each of these virtual devices can be used and managed easily, and thus, it reduces cost by increasing the infrastructure utilizing, also it provides the agility required to speed up IT processes [1]. In particular, this technology allows multiple VMs to run concurrently on a single physical Host Machine (HM), in which every VM hosts its applications, operating system, and middleware, by using a partition of the hardware resources capacity (memory, CPU power, network bandwidth, and store capability) [23], [24]. Fig.…”

Section: Cloud Computing Virtualizationmentioning

confidence: 99%

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Shukur

Zeebaree

Zebari

et al. 2020

JASTT

103

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Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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Section: Cloud Computing Virtualizationmentioning

confidence: 99%

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Shukur

Zeebaree

Zebari

et al. 2020

JASTT

103

View full text Add to dashboard Cite

show abstract

“…However, these two works did not regard the minimization of resource costs as an optimization goal, which may lead to excessive resource costs. Ficco et al [11] proposed a meta-heuristic resource allocation approach with the game theory for optimizing policies. Moreover, a clustering-based heuristic approach for edge resource allocation was proposed in [12] to reduce the average service response time of applications.…”

Section: A Literature Reviewmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

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Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods. INDEX TERMS Cloud-based software services, resource allocation, QoS prediction, workload-time windows, particle swarm optimization, genetic algorithm.

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“…In the resources coordination problem for multi‐layer applications which deployed on cloud computing, most of solutions have the assessment and prognostication for circumstance of service quality based on queue theory and use them as the input parameter for the resource coordination algorithm [4, 6, 28, 29 ]. In [4 ], Wei‐Hua Ba et al proposed a method to evaluate the performance of applications on cloud computing.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to the traditional methods of using resources by sudden workloads, this method can effectively improve resource use and ensures service quality. Massimo Ficco et al [29 ] proposed a meta‐heuristic approach for the allocation of cloud computing resources based on the coral reefs optimisation algorithm. Authors relied on classic game theory to optimise resource allocation strategy guarantees the objectives of service providers as well as customers.…”

Section: Related Workmentioning

confidence: 99%

Virtual machines migration game approach for multi‐tier application in infrastructure as a service cloud computing

Bui

Ho²,

Pham

et al. 2020

IET Networks

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Virtual machines migration is an essential feature of virtualisation technology which brings many advantages in cloud computing management process such as proactive fault tolerance, load balancing, and power management. There have been many virtual migration algorithms that have been developed but it is hard to find one that best suits all applications as well as ensures the desire of all stakeholders. The optimal virtual machines migration of cloud computing is usually NP-hard or NPcomplete. In addition, determining when virtual machines migrate is a challenge because of the difficulty of fault detection in cloud computing. In this study, the authors propose a virtual machines migration game approach for multi-tier application in infrastructure-as-a-service cloud computing where they first proposed fault detection model based on Takagi-Sugeno fuzzy system with metric's physical machines and then optimal or near optimal migration solution is approximated based on Nash equilibrium. The results of experiment demonstrate the efficiency of their proposal. The proposed virtual machines migration algorithm has been benchmarked in order to highlight their strength and feasibility.

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A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation

Cited by 65 publications

References 31 publications

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Virtual machines migration game approach for multi‐tier application in infrastructure as a service cloud computing

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