SDBATS: A Novel Algorithm for Task Scheduling in Heterogeneous Computing Systems

Munir, Ehsan Ullah; Mohsin, Sajjad; Hussain, Altaf; Nisar, Muhammad Wasif; Ali, Shoukat

doi:10.1109/ipdpsw.2013.259

Cited by 44 publications

(21 citation statements)

References 19 publications

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“…List scheduling algorithms are generally preferred since they generate good quality schedules with less complexity [7]. HEFT (Heterogeneous Earliest Finish Time) [6], CPOP (Critical Path on a Processor) [6], PETS (low complexity Performance Effective Task Scheduling) [7], HCPT (Heterogeneous Critical Parent Trees) [8], HPS (High-Performance task Scheduling) [9], Lookahead [10], SDBATS (Standard Deviation-Based Algorithm for Task Scheduling) [11], and PEFT (Predict Earliest Finish Time algorithm) [12] are typical list algorithms. HEFT, CPOP, HCPT, SDBATS, Lookahead and PEFT have two phases, including a task prioritizing phase to decide scheduling sequence of tasks, and a processor selection phase to assign a processor to each task sequentially for a minimum makespan.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Scheduling of Workflow for Makespan and Robustness Improvement in the IaaS Cloud

Jiang

Haihong

Song

2017

IEICE Trans. Inf. & Syst.

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SUMMARYThe Infrastructure-as-a-Service (IaaS) cloud is attracting applications due to the scalability, dynamic resource provision, and payas-you-go cost model. Scheduling scientific workflow in the IaaS cloud is faced with uncertainties like resource performance variations and unknown failures. A schedule is said to be robust if it is able to absorb some degree of the uncertainties during the workflow execution. In this paper, we propose a novel workflow scheduling algorithm called Dynamic Earliest-Finish-Time (DEFT) in the IaaS cloud improving both makespan and robustness. DEFT is a dynamic scheduling containing a set of list scheduling loops invoked when some tasks complete successfully and release resources. In each loop, unscheduled tasks are ranked, a best virtual machine (VM) with minimum estimated earliest finish time for each task is selected. A task is scheduled only when all its parents complete, and the selected best VM is ready. Intermediate data is sent from the finished task to each of its child and the selected best VM before the child is scheduled. Experiments show that DEFT can produce shorter makespans with larger robustness than existing typical list and dynamic scheduling algorithms in the IaaS cloud.

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Section: Related Workmentioning

confidence: 99%

Dynamic Scheduling of Workflow for Makespan and Robustness Improvement in the IaaS Cloud

Jiang

Haihong

Song

2017

IEICE Trans. Inf. & Syst.

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“…The heuristic-based group comprises three subcategories: list, clustering, and duplication scheduling. Heterogeneous Earliest Finish Time [6], Critical Path On a Processor [6], Standard Deviation-Based Algorithm for Task Scheduling [7], Predict Earliest Finish Time [8], Longest Dynamic Critical Path (LDCP) [16], Heterogeneous Critical Parent Trees (HCPT) [17], High-Performance Task Scheduling (HPS) [18], low complexity Performance Effective Task Scheduling (PETS) [19], Heterogeneous Earliest Finish with Duplicator (HEFD) [20], and Selective Duplication Algorithm [21] are typical heuristic-based algorithms. Clustering heuristics are primarily proposed for homogeneous systems, but they have limitations in higher level heterogeneity systems.…”

Section: Related Workmentioning

confidence: 99%

“…Typical task scheduling algorithms include Heterogeneous Earliest Finish Time [6] and Critical Path On a Processor [6], Standard Deviation-Based Algorithm for Task Scheduling [7], and Predict Earliest Finish Time [8]. Although they have been widely used in heterogeneous computing systems, they still have three drawbacks.…”

Section: Introductionmentioning

confidence: 99%

HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing

Wang¹,

Wang

Liu³

et al. 2016

Scientific Programming

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High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable; (2) an entry task duplication selection policy to make the makespan shorter; and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.

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“…Munir et al [21], stated a SD-Based Algorithm Task Scheduling (SDBATS) for scheduling precedence constrained task graphs, which uses the standard deviation of the expected execution time of a given task on the available resources in the heterogeneous computing system as a key factor assigning task priority for achieving high quality schedules with minimum makespan. This new approach takes into account the task heterogeneity and achieves a significant reduction in the overall execution time of a given application.…”

Section: Sdbats: a Novel Algorithm For Task Schedulingmentioning

confidence: 99%

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

Yazdanpanah¹,

Shouraki²,

Jamali³

2016

IJCA

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A heterogeneous computing environment is a large-scale distributed data processing environment, it is depends to some extent parameters on the application and that classified in three main categories such as the hardware, the communication layer, and the software. A computer system is consists of hardware and software from two or more different manufacturers. Scheduling is one of the important factors in the heterogeneous environment and the aim of task scheduling in the processing environment is to move computation towards data. In order to achieve improve performance, increase the throughput and minimizing the makespan; scheduler must avoid unnecessary data transmission. Hence, different scheduling algorithms for heterogeneous computing environment are necessary to provide good performance. How to speedup scheduling the service resources to achieve the lowest cost becomes more and more important. This paper tries to illustrate and analyze the overview of eighteen different scheduling algorithms for heterogeneous computing environment and their scheduling issues and problems.

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SDBATS: A Novel Algorithm for Task Scheduling in Heterogeneous Computing Systems

Cited by 44 publications

References 19 publications

Dynamic Scheduling of Workflow for Makespan and Robustness Improvement in the IaaS Cloud

Dynamic Scheduling of Workflow for Makespan and Robustness Improvement in the IaaS Cloud

HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

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