Improved CloudSim for Simulating QoS-Based Cloud Services

Jung, Jean; Kim, Nam-Uk; Jung, Sung-Min; Chung, Tai-Myoung

doi:10.1007/978-94-007-5857-5_58

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Another algorithm based on evaluating the completion time and sorting tasks by priority to achieve good performance and load balancing was examined [23]. In the research work of [24], an implementation of priority queue on broker side was proposed to be able to control cloudlets with priority and process them differentially. The results showed a faster completion time.…”

Section: Task Scheduling and Resource Allocation 231 Current Existing Strategiesmentioning

confidence: 99%

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

Miao¹

2021

Preprint

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This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

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Section: Task Scheduling and Resource Allocation 231 Current Existing Strategiesmentioning

confidence: 99%

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

Miao¹

2021

Preprint

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“…On the other hand, CloudSim has also received some critiques, for example, for its oversimplified model of I/O processing, limited communication models, inaccuracy of communication models, and lack of support for quality of service …”

Section: Introductionmentioning

confidence: 99%

Cloud simulators in the implementation and evaluation of virtual machine placement algorithms

Mann

2018

Softw Pract Exp

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In recent years, many algorithms have been proposed for the optimized allocation of virtual machines in cloud data centers. Such algorithms are usually implemented and evaluated in a cloud simulator. This paper investigates the impact of the choice of cloud simulator on the implementation of the algorithms and on the evaluation results. In particular, we report our experiences with porting an algorithm and its evaluation framework from one simulator (CloudSim) to another (DISSECT-CF). Our findings include limitations in the design of the simulators and in existing algorithm implementations. Based on this experience, we propose architectural guidelines for the integration of virtual machine allocation algorithms into cloud simulators.

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A Scheduling Algorithm Based on User Satisfaction Degree in Cloud Environment

Chen²,

Huang

2018

Communications in Computer and Information Science

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Improved CloudSim for Simulating QoS-Based Cloud Services

Cited by 6 publications

References 3 publications

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

Cloud simulators in the implementation and evaluation of virtual machine placement algorithms

A Scheduling Algorithm Based on User Satisfaction Degree in Cloud Environment

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