scholarly journals Bandwidth-Aware Scheduling of Workflow Application on Multiple Grid Sites

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Harshadkumar B. Prajapati ◽  
Vipul A. Shah
Keyword(s):  
Open Source ◽  
Scheduling Algorithm ◽  
Scientific Workflows ◽  
Workflow Scheduling ◽  
Grid Environment ◽  
Data Movement ◽  
Workflow Mapping ◽  
Workflow Application ◽  
Algorithm Level ◽  
Better Than

Bandwidth-aware workflow scheduling is required to improve the performance of a workflow application in a multisite Grid environment, as the data movement cost between two low-bandwidth sites can adversely affect the makespan of the application. Pegasus WMS, an open-source and freely available WMS, cannot fully utilize its workflow mapping capability due to unavailability of integration of any bandwidth monitoring infrastructure in it. This paper develops the integration of Network Weather Service (NWS) in Pegasus WMS to enable the bandwidth-aware mapping of scientific workflows. Our work demonstrates the applicability of the integration of NWS by making existing Heft site-selector of Pegasus WMS bandwidth aware. Furthermore, this paper proposes and implements a new workflow scheduling algorithm—Level based Highest Input and Processing Weight First. The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. Thus, the proposed bandwidth-aware workflow scheduling enhances capability of Pegasus WMS and can increase performance of workflow applications.

Cost Effective Heuristic workflow scheduling algorithm in Cloud under Deadline Constraint

2019 ◽  
Vol 12 ◽  
Author(s):  
Jasraj Meena ◽  
Manu Vardhan
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Workflow Scheduling ◽  
Performance Variation ◽  
Acquisition Delay ◽  
Very High ◽  
Deadline Constraint

Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. There are lots of algorithms has been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as well as they are not meeting the user-defined deadline constraint. This paper focuses on satisfying the userdefined deadline of a scientific workflow while minimizing the total execution cost. So, to achieve this, we have proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. The proposed CEuD algorithm considers all the essential features of Cloud and resolves the major issues such as performance variation, and acquisition delay. We have compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and getting better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

Two Approaches for Workflow Scheduling with Quality of Service in the Grid

2012 ◽  
pp. 1265-1288
Author(s):  
Fangpeng Dong ◽  
Selim G. Akl
Keyword(s):  
Resource Sharing ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Workflow Scheduling ◽  
Performance Change ◽  
Probability Mass ◽  
Grid Resources ◽  
Mass Functions ◽  
Algorithm Level

Over the past decade, Grid Computing has earned its reputation by facilitating resource sharing in larger communities and providing non-trivial services. However, for Grid users, Grid resources are not usually dedicated, which results in fluctuations of available performance. This situation raises concerns about the quality of services (QoS). The meaning of QoS varies with different concerns of different users. Objective functions that drive job schedulers in the Grid may be different from each other as well. Some are system-oriented, which means they make schedules to favor system metrics such as throughput, load-balance, resource revenue and so on. To narrow the scope of the problem to be discussed in this chapter and to make the discussion substantial, the scheduling objective function considered is minimizing the total completion time of all tasks in a workflow (also known as the makespan). Correspondingly, the meaning of QoS is restricted to the ability that scheduling algorithms can shorten the makespan of a workflow in an environment where resource performance is vibrant. This chapter introduces two approaches that can provide QoS features at the workflow scheduling algorithm level in the Grid. One approach is based on a workflow rescheduling technique, which can reallocate resources for tasks when a resource performance change is observed. The other copes with the stochastic performance change using pre-acquired probability mass functions (PMF) and produces a probability distribution of the final schedule length, which will then be used to handle the different QoS concerns of the users.

Two Approaches of Workflow Scheduling with QoS in the Grid

2011 ◽  
pp. 1-27
Author(s):  
Fangpeng Dong ◽  
Selim G. Akl
Keyword(s):  
Resource Sharing ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Workflow Scheduling ◽  
Performance Change ◽  
Probability Mass ◽  
Grid Resources ◽  
Mass Functions ◽  
Algorithm Level

Over the past decade, Grid Computing has earned its reputation by facilitating resource sharing in larger communities and providing non-trivial services. However, for Grid users, Grid resources are not usually dedicated, which results in fluctuations of available performance. This situation raises concerns about the quality of services (QoS). The meaning of QoS varies with different concerns of different users. Objective functions that drive job schedulers in the Grid may be different from each other as well. Some are system-oriented, which means they make schedules to favor system metrics such as throughput,load-balance, resource revenue and so on. To narrow the scope of the problem to be discussed in this chapter and to make the discussion substantial, the scheduling objective function considered is minimizing the total completion time of all tasks in a workflow (also known as the makespan). Correspondingly, the meaning of QoS is restricted to the ability that scheduling algorithms can shorten the makespan of a workflow in an environment where resource performance is vibrant. This chapter introduces two approaches that can provide QoS features at the workflow scheduling algorithm level in the Grid. One approach is based on a workflow rescheduling technique, which can reallocate resources for tasks when a resource performance change is observed. The other copes with the stochastic performance change using pre-acquired probability mass functions (PMF) and produces a probability distribution of the final schedule length, which will then be used to handle the different QoS concerns of the users.

Workflow Scheduling Algorithm Based on Reliance Group in Cloud Environments

2014 ◽  
Vol 513-517 ◽  
pp. 2203-2206
Author(s):  
Yin Juan Zhang ◽  
Bo Liu ◽  
Chen Li ◽  
Yun Li
Keyword(s):  
Service Providers ◽  
Scheduling Algorithm ◽  
Service Performance ◽  
Workflow Scheduling ◽  
Data Intensive ◽  
Cloud Environments ◽  
High Degree ◽  
Resource Service ◽  
Workflow Application ◽  
The Ideal

Cloud computing has attracted more people's attention and been extensive research. In order to provide satisfying service for users during the implement process of data-intensive workflow application in cloud environments, an improved algorithm is proposed which based on reliance group with high degree of dependence. By this measure, users can obtain the ideal acceleration radio of cost and service performance. Furthermore, more comprehensive resource service is provided by service providers.

scholarly journals Scalable workflow scheduling algorithm for minimizing makespan and failure probability

2019 ◽  
Vol 8 (1) ◽  
pp. 283-290
Author(s):  
Maslina Abdul Aziz ◽  
Izuan Hafez Ninggal
Keyword(s):  
Failure Probability ◽  
Critical Path ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Workflow Scheduling ◽  
System A ◽  
Conflicting Objectives ◽  
Simulation Results ◽  
Earliest Finish Time ◽  
Workflow Application

This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algorithm discussed in this paper schedules parallel applications on homogeneous systems without sacrificing the two conflicting objectives: reliability and makespan. The proposed algorithm handles unexpected failure causes rescheduling of the failed task to available resources. In order to analyse the performance of the FAWS algorithm, it will be compared with the popular scheduling algorithm namely Heterogeneous Earliest Finish Time (or HEFT) and Critical Path (CP). A simulation-driven analysis based on realistic workflow application was demonstrated using DAG graph as a continuation of the Layered Workflow Scheduling Algorithm (LWFS). The FAWS algorithm aims to minimize the makespan, increases reliability and therefore boosts the performance of the whole system. A workflow generator was developed to generate large task graphs randomly and scheduled the parallel applications. Based on the simulation results, the proposed algorithm has improved the overall workflow scheduling effectiveness in comparison with existing algorithms.

Workflow scheduling algorithm based on load balance in grid

2011 ◽  
Vol 30 (12) ◽  
pp. 3184-3186
Author(s):  
Ming-quan WANG ◽  
Jiong YU ◽  
Yuan TIAN ◽  
Yun HAN
Keyword(s):  
Load Balance ◽  
Scheduling Algorithm ◽  
Workflow Scheduling
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