Service Scheduling in Cloud Computing based on Queuing Game Model

doi:10.3837/tiis.2014.05.003

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Abrishami et.al proposes a new QoS-based workflow-scheduling algorithm named partial critical paths (PCP), which makes efforts on minimizing the cost of workflow execution while meeting deadline constraints [2]. In addition, Lin et al in [7] use queuing game model to scheduling problem in service level of cloud. Hassan et al in [8] presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications.…”

Section: Related Workmentioning

confidence: 99%

Topology-based Workflow Scheduling in Commercial Clouds

2015

KSII TIIS

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Cloud computing has become a new paradigm by enabling on-demand provisioning of applications, platforms or computing resources for clients. Workflow scheduling has always been treated as one of the most challenging problems in clouds. Commercial clouds have been widely used in scientific research, such as biology, astronomy and weather forecasting. Certainly, it is very important for a cloud service provider to pursue the profits for the commercial essence of clouds. This is also significantly important for the case of providing services to workflow tasks. In this paper, we address the issues of workflow scheduling in commercial clouds. This work takes the communication into account, which has always been ignored. And then, a topology-based workflow-scheduling algorithm named Resource Auction Algorithm (REAL) is proposed in the objective of getting more profits. The algorithm gives a good performance on searching for the optimum schedule for a sample workflow. Also, we find that there exists a certain resource amount, which gets the most profits to help us get more enthusiasm for further developing the research. Experimental results demonstrate that the analysis of the strategies for most profits is reasonable, and REAL gives a good performance on efficiently getting an optimized scheme with low computing complexity.

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Section: Related Workmentioning

confidence: 99%

Topology-based Workflow Scheduling in Commercial Clouds

2015

KSII TIIS

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“…Cloudlet: Users' requests are grouped together, and this is termed as a Cloudlet [20]. Each cloudlet is consolidated for a time quantum.…”

Section: Components Of Rasmentioning

confidence: 99%

RAS: Request Assignment Simulator for Cloud-Based Applications

2015

KSII TIIS

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Applications deployed in cloud receive a huge volume of requests from geographically distributed users whose satisfaction with the cloud service is directly proportional to the efficiency with which the requests are handled. Assignment of user requests based on appropriate load balancing principles significantly improves the performance of such cloud-based applications. To study the behavior of such systems, there is a need for simulation tools that will help the designer to set a test bed and evaluate the performance of the system by experimenting with different load balancing principles. In this paper, a novel architecture for cloud called Request Assignment Simulator (RAS) is proposed. It is a customizable, visual tool that simulates the request assignment process based on load balancing principles with a set of parameters that impact resource utilization. This simulator will help to ascertain the best possible resource allocation technique by facilitating the designer to apply and test different load balancing principles for a given scenario.

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“…Also cloud computing technology can deal with many services across the Internet [1]. According to NIST (National Institute of Standards and Technology), service modes in cloud computing are classified as SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) [2][3][4]. IaaS providers supply storage space, computing and network resources with which users can execute operating systems, applications and any software.…”

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

Xue¹,

Jin²,

Wang³

2018

TIIS

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Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

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Service Scheduling in Cloud Computing based on Queuing Game Model

Cited by 7 publications

References 14 publications

Topology-based Workflow Scheduling in Commercial Clouds

Topology-based Workflow Scheduling in Commercial Clouds

RAS: Request Assignment Simulator for Cloud-Based Applications

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

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