Performance Evaluation of Cloud Services with Profit Optimization

Jaiganesh, M.; Ramasamy, Balakrishnan; Kumar, Amit; Mercy, S.

doi:10.1016/j.procs.2015.06.003

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…[ ] E T be the average sojourn time of a delay sensitive task processed by the rapid cloud. Referencing to [14], 2 [ ] E T is given as follows:…”

Section: Performance Measuresmentioning

confidence: 99%

“…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%

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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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“…[ ] E T be the average sojourn time of a delay sensitive task processed by the rapid cloud. Referencing to [14], 2 [ ] E T is given as follows:…”

Section: Performance Measuresmentioning

confidence: 99%