Joint QoS-aware and Cost-efficient Task Scheduling for Fog-Cloud Resources in a Volunteer Computing System

TL;DR

This paper introduces two novel task scheduling algorithms for volunteer fog-cloud systems that optimize QoS and cost, significantly improving deadline satisfaction and reducing costs compared to existing methods.

Contribution

The paper proposes two new scheduling algorithms, Min-CCV and Min-V, designed to efficiently allocate heterogeneous resources in volunteer computing systems for IoT requests.

Findings

Algorithms improve deadline satisfaction task rates to 99.5%.

Reduce total cost by 15-53% compared to genetic algorithms.

Efficient resource allocation outperforms state-of-the-art methods.

Abstract

Volunteer computing is an Internet-based distributed computing system in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both the high Quality of Service (QoS) and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered one of the main critical challenges in a heterogeneous VCS. Due to this, in this paper, we design two task scheduling algorithms for VCSs, named Min-CCV and Min-V. The main goal of the proposed algorithms is jointly minimizing the computation, communication and delay violation cost for the Internet of Things (IoT) requests. Our extensive simulation results show that proposed algorithms are able to allocate tasks to volunteer fog/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates by around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.