Data-Aware Task Dispatching for Batch Queuing System

Li, Xieming; Tatebe, Osamu

doi:10.1109/jsyst.2015.2471850

Cited by 3 publications

(1 citation statement)

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“…For these tasks, it has different expectation on task execution time. While scheduling heterogeneous tasks, the system not only needs to reduce the execution time for periodic tasks, but also response online tasks as quickly as possible, usually within a few minutes, even a few seconds [4]. Hence, it is an important issue to schedule the heterogeneous tasks in data center to support various services, which is also important for energy reduction since longer task execution also consumes more energy.…”

Section: Introductionmentioning

confidence: 99%

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Wang

Abawajy

et al. 2020

Energies

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Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

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Section: Introductionmentioning

confidence: 99%