Cost-Aware Resource Management for Federated Clouds Using Resource Sharing Contracts

Xu, Jian; Palanisamy, Balaji

doi:10.1109/cloud.2017.38

Cited by 16 publications

(5 citation statements)

References 18 publications

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“…Since lowering the quantity of lively servers commonly will increase the quantity of VM migrations, we similarly reduce the quantity of VM migrations within side the proposed GBLB set of rules. Simulation consequences display that, as compared with different 3 famous algorithms, the proposed GBLB set of rules can lessen the quantity of lively servers and obtain the satisfactory load balancing stage on the fee of some extra migrations (Xu, H., et al, 2017).…”

Section: Energy Consumption Modelingmentioning

confidence: 98%

Energy-Aware VM Scheduler

Shukla¹,

Chaturvedi²

2022

International Journal of Information System Modeling and Design

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Cost is the backbone of any business model to sustain and growth in the competitive business market. Considering this, higher energy consumption may affect the operational cost of any industry. Cloud computing is one of the most popular IT business model for service provider and its users. It is observed in many research, that the average cost of the services are highly dependent upon the run time and power consumption. More power consumption effect the environmental things as well. In this review, more than 100 research articles are considered to evaluate the energy consumption methods, issues and challenges from 2009 to 2021. Based on the study, the energy-aware load balancing methods are classified into four major clusters. The most cited methods are compared based on method used, platform where it is deployed, different evaluation parameters like cost, run time, Virtual Machine utilization, Service-level-agreement violation rate, and Quality-of-service QoS. The result of this review is presented as open research issues and challenges for forthcoming research.

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Section: Energy Consumption Modelingmentioning

confidence: 98%

Energy-Aware VM Scheduler

Shukla¹,

Chaturvedi²

2022

International Journal of Information System Modeling and Design

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“…The two nodes, i.e., resource consumer and resource provider, undergo a service contract that includes a reward for the successful completion of a task. The authors in [25] presented another framework for resource sharing that allows a cellular service provider to initiate a contract that remains valid for a given amount of time. The work proposed in [25] further employed a scheduling policy for efficient utilization of available resources.…”

Section: Resource Sharingmentioning

confidence: 99%

“…The authors in [25] presented another framework for resource sharing that allows a cellular service provider to initiate a contract that remains valid for a given amount of time. The work proposed in [25] further employed a scheduling policy for efficient utilization of available resources. However, increased computational complexity remains one of its drawbacks.…”

Section: Resource Sharingmentioning

confidence: 99%

MACRS: An Enhanced Directory-Based Resource Sharing Framework for Mobile Ad Hoc Networks

et al. 2022

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Recent technological developments have caused a rapid increase in the use of portable devices around the globe. However, these devices comprise limited processing resources that restrict their performance. To overcome this issue, the existing literature provides several frameworks that enable resource sharing through ad hoc clouds. However, these frameworks lack the ability to cater to the omni-directional movements of devices, which adversely affects the cloud stability, thereby, restricting the resource sharing process. To this end, this paper proposes a novel framework, namely Mobility-aware Ad hoc Cloud-based Resource Sharing (MACRS), which aims to enhance resource sharing among devices. To achieve this aim, MACRS proposes a new mobility-aware clustering algorithm that improves cloud stability. Moreover, the proposed framework prevents unfair resource exploitation and introduces an enhanced technique to handle emergency tasks. Furthermore, we employed event-triggered energy valuations’ synchronization, instead of periodic updates, which minimizes network congestion, hence staving off bandwidth wastage. Additionally, MACRS proposes to maintain the local directory at each node, instead of the cellular service provider, to reduce end-to-end delay during energy valuations’ verification and to minimize the overall execution time of tasks. The simulation results demonstrated that MACRS provides considerably improved cloud stability and resource sharing in comparison with eminent frameworks.

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“…Xu, et al [11] have proposed contract-based resource sharing in federated clouds. This proposed model maximizes the revenue and also allocates the resources fairly among the cloud service providers.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

Rajeshwari¹,

Dakshayini²,

Guruprasad³

2021

J. ICT Res. Appl.

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The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

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Cost-Aware Resource Management for Federated Clouds Using Resource Sharing Contracts

Cited by 16 publications

References 18 publications

Energy-Aware VM Scheduler

Energy-Aware VM Scheduler

MACRS: An Enhanced Directory-Based Resource Sharing Framework for Mobile Ad Hoc Networks

Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

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