Fairness Resource Sharing for Dynamic Workflow Scheduling on Heterogeneous Systems

2012 ◽  
Author(s):  
Hamid Arabnejad ◽  
Jorge Barbosa
Keyword(s):  
Resource Sharing ◽  
Heterogeneous Systems ◽  
Workflow Scheduling ◽  
Dynamic Workflow

A look-ahead algorithm for online multiple workflow scheduling problem in heterogeneous systems

2017 ◽  
Vol 25 (4) ◽  
pp. 331-342 ◽  
Author(s):  
Zhenzhen Xu ◽  
Xin Chen ◽  
Xiujuan Xu ◽  
Xiaowei Zhao ◽  
Jie Dai ◽  
...  
Keyword(s):  
Heterogeneous Systems ◽  
Workflow Scheduling ◽  
Scheduling Problem ◽  
Look Ahead

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.

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