Abstract

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Highlights

  • In recent years, volunteer computing (VC) [1] has supported diverse large-scale scientific research applications using idle resources from a large number of heterogeneous volunteer computers.VC provides almost free unlimited computing resources for scientific research projects, such as SETI@home [2], Folding@home [3], and ATLAS@Home [4], and opportunities for volunteers to participate in scientific research

  • Increasingly more researchers have extended the unlimited computing resources provided by VC to cloud computing [5] and big data fields [6]

  • We propose two novel dynamic task scheduling algorithms to solve the task scheduling problem with deadline constraint in VC platforms

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Summary

Introduction

VC provides almost free unlimited computing resources for scientific research projects, such as SETI@home [2], Folding@home [3], and ATLAS@Home [4], and opportunities for volunteers to participate in scientific research. Increasingly more researchers have extended the unlimited computing resources provided by VC to cloud computing [5] and big data fields [6]. One of the challenges is to the algorithm of scheduling parallel tasks in such heterogeneous and dynamic platforms. Studies have shown that assigning parallel tasks to multiple processors are NP-hard, because of its importance, many researchers have done lots of work for this problem [8,9,10,11]

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