Journal ArticleDOI
A market-oriented hierarchical scheduling strategy in cloud workflow systems
TLDR
The hierarchical scheduling strategy is being implemented in the SwinDeW-C cloud workflow system and demonstrating satisfactory performance, and the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisations rate on makespan, the optimisation rate on cost and the CPU time.Abstract:
A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.read more
Citations
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Journal ArticleDOI
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
TL;DR: Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources, then paints a landscape of the scheduling problem and solutions, and a comprehensive survey of state-of-the-art approaches is presented systematically.
Journal ArticleDOI
A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges
Sukhpal Singh,Inderveer Chana +1 more
TL;DR: Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload.
Proceedings ArticleDOI
A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling
TL;DR: Experimental results show that the proposed Revised Discrete Particle Swarm Optimization (RDPSO) algorithm can achieve much more cost savings and better performance on make span and cost optimization.
Journal ArticleDOI
Towards workflow scheduling in cloud computing
TL;DR: A comprehensive survey and analysis of state of the art workflow scheduling schemes for scheduling simple and scientific workflows in the cloud computing and provides a classification of the proposed schemes based on the type of scheduling algorithm applied in each scheme.
Journal ArticleDOI
Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets
TL;DR: This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources.
References
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Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility
TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.
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The GRID: Blueprint for a New Computing Infrastructure
TL;DR: The main purpose is to update the designers and users of parallel numerical algorithms with the latest research in the field and present the novel ideas, results and work in progress and advancing state-of-the-art techniques in the area of parallel and distributed computing for numerical and computational optimization problems in scientific and engineering application.
Proceedings ArticleDOI
Cloud Computing and Grid Computing 360-Degree Compared
TL;DR: In this article, the authors compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both the two technologies, and compare the advantages of grid computing and cloud computing.