A market-oriented hierarchical scheduling strategy in cloud workflow systems

Wu, Zheng-Mao; Liu, Xiao; Ni, Zhiwei; Yuan, Dong; Yang, Yun

doi:10.1007/s11227-011-0578-4

Cited by 225 publications

(150 citation statements)

References 44 publications

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“…Liu et al [8] proposed an innovative instance-intensive cost-constraint workflow scheduling algorithm for Cloud which is called as Compromised-Time-Cost (CTC) considering the main factors cost and time that has user input and also incorporating the main characteristics of Cloud Computing. The results obtained from simulation showed that the proposed algorithm is able to achieve lower cost as compared to the other algorithms while maintaining the user designated deadline.…”

Section: Related Work Sadhasivam and Selvaranimentioning

confidence: 99%

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A Survey on Scheduling Techniques in Cloud Computing

Jacob¹,

Jeyakrishnan²

2017

IJRTER

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Abstract-CloudComputing is one of the hot topics in the field of IT. A large number of users request for on-demand computing resources simultaneously. The consumers pay a certain amount of money for using the services and in return expect Quality of Services (QoS) from the Service Providers. In order to achieve efficient QoS, a proper scheduling technique is required. Maintaining the resources is very important as scaling up or down of the resources occurs quite often due to the fluctuation of the resources used by the consumers. Therefore, a proper utilization of the resources becomes a key factor to obtain maximum QoS. This paper mainly provides a comparative study of the different scheduling techniques that are used by the Service Providers for allocating resources in the cloud market.

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Section: Related Work Sadhasivam and Selvaranimentioning

confidence: 99%

“…Wu et al [8] had suggested a scheduling strategy which is market oriented that contains two level scheduling: service level and task level scheduling. Service level scheduling is responsible for assigning Task-to-Service viz.…”

Section: Related Work Sadhasivam and Selvaranimentioning

confidence: 99%

A Survey on Scheduling Techniques in Cloud Computing

Jacob¹,

Jeyakrishnan²

2017

IJRTER

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“…Before analysing the problem of scheduling, we should have a study of the execution conditions and time points of the dependent tasks firstly, and then schedule it according to the correspond scheduling strategy [5].…”

Section: The Analysis Of Tasks Executionmentioning

confidence: 99%

Research of Dependent Tasks Scheduling Algorithm in Cloud Computing Environments

et al. 2016

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With the dependent relationship of tasks submitted by the users in the model of Cloud computing resources scheduling become stronger and stronger, it is worthy of studying how to optimize the scheduling strategy and algorithm to meet the different demands of the users, and it is absolutely importance. In this article, the author analysed the factors that will affect the entire task-sets execution firstly. Then proposed a new tasks scheduling model based on the original priority calculation method and the idea of redundant duplication of tasks. In the phase of tasks scheduling in the model, the execution results of all parent tasks of the subtask that being executing are considered. The costs of communication between task-sets has reduced by the method of redundant duplication of tasks, so that the execution time of some subtasks share be advanced, and the entire execution efficiency of task-sets can be increased. At the end of this article, from the comparative results of the space-time complexity of contrast algorithms and the algorithm proposed by the author during the process of processing dependent tasks, we can find that subtasks execution time can be advanced and the complete time of the whole task-set can be cut down to a certain extent Dependent Tasks and the Resource Node ModellingThe dependent tasks are the tasks that in the task-sets submitted by users exist a certain dependent relationship between each other, and the relationship shows up in that before the subtask be executed, the execution results of its parent tasks must be known. And we can use a directed acyclic graph G to represent the dependent tasks [1]. As is shown in the following figure: The figure is represented as a four-tuples:, ,, , … , and | , ∈ , . The N represents the task node collection and the represents a single task node. The E represents the collection of "edge", and the edge determines the dependent relationship between tasks. If tow task nodes are connected by the edge, then there is a dependency relationship between them, and the sub task is the task node that the arrow points to, another side points to the parent task. The Q represents the average calculation cost collection for each task node, and the average calculation cost here refers to the average value of the cost of the task node in all resource nodes. The W represents the collection of communication cost between the tasks. And the special note here is that if tow dependencies are distributed to the same computing node, there is no communication cost between them [2].We can also make the following definition: Definition 1: pre v represents the predecessor task node set (or parent task node set) of the single task node v , and sub v represents the subsequent task node set (or sub task set) of the single task node v .Here we consider the computing node modelling next. Before that we need to do some declare first, the scheduling model of dependent tasks is consistent with the scheduling model of independent tasks, which has demonstrated in the

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“…Laili, Tao [14] have combined the Service Composition Optimal Selection (SCOS) and Optimal Allocation of Computing Resources (OACR) into one-time decision in one console, named Dual Scheduling of Cloud Services and Computing Resources (DS-CSCR). Wu, Liu [16] have proposed a market-oriented hierarchical scheduling strategy in Cloud workflow systems.  2014…”

Section:  2013mentioning

confidence: 99%

“…Job scheduler is a vital part of any distributed system like Grid [11][12][13], Cloud [14][15][16][17] and P2P networks [18][19][20] which assigns jobs to suitable resources for execution. The scheduling of a task workflow in a distributed computing platform is a well-known NP-hard problem [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Milani¹,

Navin²

2015

IJITCS

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Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

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A market-oriented hierarchical scheduling strategy in cloud workflow systems

Abstract: A market-oriented hierarchical scheduling strategy in cloud workflow systems.

Cited by 225 publications

References 44 publications

A Survey on Scheduling Techniques in Cloud Computing

A Survey on Scheduling Techniques in Cloud Computing

Research of Dependent Tasks Scheduling Algorithm in Cloud Computing Environments

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

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