An Application-Level Scheduling with Task Bundling Approach for Many-Task Computing in Heterogeneous Environments

Xiao, Jian; Zhang, Yu; Chen, Shuwei; Yu, Huashan

doi:10.1007/978-3-642-35606-3_1

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…However, the ideal performance of each application is computed as that when all the available resources are used without considering interference effects among its own tasks [32], or when an even share of each type of heterogeneous resources is used without reflecting different behaviors of application performance on platforms [20], [31]. Scheduling many-task applications In a heterogeneous computing system, several scheduling algorithms for manytask applications have been investigated to improve the efficiency [6], [20], [22], [36]. A scheduling algorithm that considers the suitability (i.e.…”

Section: Related Workmentioning

confidence: 99%

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Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Hwang

Kim

Choi

2021

IEEE Trans. Serv. Comput.

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In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness.

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Section: Related Workmentioning

confidence: 99%

“…A scheduling algorithm that considers the suitability (i.e. importance) of a platform to an application as well as that of an application to a platform was proposed [36]. Also, preference rankings of applications for different platforms provided by users are used on scheduling many-task applications [22].…”

Section: Related Workmentioning

confidence: 99%

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Hwang

Kim

Choi

2021

IEEE Trans. Serv. Comput.

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“…The system doesn't consider any issues about the IO contention. Yu Zhang, Shuwei Chen, Ziqian Hu (2013) proposed a new scheduling algorithm Extended Application-Level Scheduling with Task Bundling Approach [15] which had overcome all the limitations of ALSTB algorithm, introduced some optimization for IO contention. This system has also reduced the overall communication.…”

Section: Review On Schedulingmentioning

confidence: 99%

Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters

Gokuldev¹,

Jathin²

2018

IJET

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Performing scheduling of tasks with low energy consumption with high performance is one of the major concerns in distributed computing. Most of the existing systems have achieved improved energy efficiency but compromised with QoS metrics such as makespan and resource utilization. A resource scheduling strategy for wireless clusters is proposed by making careful considerations on decisions that would im-prove the battery life of nodes. The proposed strategy also incorporates monitoring system with in the clusters for optimizing the system performance as well as energy consumption. The system ensures “Any case zero loss" performance wherein each cluster will be monitored by at least one cluster monitor. This is implemented by using predictive calculation at each cluster monitor to communicate only if absolutely essential, during assigning jobs to resources, selecting optimal resources by assigning the jobs to the most power efficient resource among the available idle resources within the cluster. The experimental result ensures improved system performance with low power consumption in homogeneous computing environment. The resource sharing strategy is experimentally analyzed, considering the important performance metrics such as starvation deadline, turnaround time, miss hit count through simulations. Significant results were observed with improved efficiency.

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On the role of application and resource characterizations in heterogeneous distributed computing systems

Hwang

Kim

et al. 2016

Cluster Comput

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An Application-Level Scheduling with Task Bundling Approach for Many-Task Computing in Heterogeneous Environments

Cited by 8 publications

References 15 publications

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters

On the role of application and resource characterizations in heterogeneous distributed computing systems

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