A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

Abstract

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. fuzzy theory to analyze resource performance, preprocess the resources, and achieve the integration and classification of a large number of resources, then we improve the HEFT list sort and complete the task scheduling according to their priority. PROBLEM DESCRIPTIONS Cloud computingCloud computing is hinting at a future in which we would not compute on local computers but on centralized facilities operated by third-party compute and storage utilities [5]. Computing pioneer John McCarthy predicted that 'computation may someday be organized as a public utility', and went on to speculate how this might occur [3,4]. There is little consensus on how to define the cloud computing, many computing researchers and practitioners have attempted to define clouds in various ways.Buyya et al.[22] thought that cloud computing was a new and promising paradigm delivering services as computing utilities. Clouds were designed to provide services to external users, providers needed to be compensated for sharing their resources and capabilities. Wang L.Z et al.[23] defined the science cloud computing system, they pointed out that cloud computing could not only provide the hardware service HaaS (Hardware as a service), software service SaaS (software as a service) and data resource service DaaS (Data as a service), but also could provide the platform service PaaS (platform as a service) to the user. So the users would submit their own on-demand to the computing platform about the hardware configuration, software installation, data access requirements, and so on. With the increasing demand for process automation in the cloud, there is a requirement to create ...

A fuzzy energy and security aware scheduling in cloud

“…The test cases having various job factors that are used for evaluating the algorithms and also demonstrating its strength are used and the jobs that have less processing time will be assigned some newer priorities of processing thereby improving the user satisfaction. Guo et al, [9] presented a Workflow Task Scheduling algorithm based on the resources' Fuzzy Clustering named FCBWTS. The major objective of scheduling was to be minimized makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph.…”

Application of Ant Colony Algorithm in Cloud Resource Scheduling Based on Three Constraint Conditions

“…1 T , 2 T , 3 T are the boundary constraints, which meet the QoS constraints in cloud computing environment.…”

“…Alrokayan uses the cloud to expand the cluster to deal with the local cluster resource scarcity, and use some job scheduling strategy to evaluate the performance of the job, analyze the resource cost. The experimental results show that, in the case of heavy load, the Navie scheduling strategy to use the resources of the cloud will generate a lot of cost, while the use of request backfilling and redirection strategy based on the expansion factor to use the resources of the cloud, the cost is relatively low [2]. For the resource scheduling in the cloud, Pop proposed a resource load balancing scheduling algorithm based on trust driven.…”

Expert Systems In A Cloud Computing Environment Model For Fast-Paced Decision Making

“…Nonetheless, cloud environments can "evolve" in various ways. Work low scheduling research based on Modi ied Critical Path (MCP) and Dynamic Critical Path (DCP) algorithms can be used inside the cloud environment [9]. Likewise, expert systems can be implemented as well as other applications [16].…”