JOURNAL ARTICLE

Fine-Grained Task-Dependency Offloading in Mobile Cloud Computing

Abstract

In mobile cloud computing, mobile devices such as smartphones, can reduce the energy consumption while achieving higher performances through offloading their computational workloads to the remote cloud. This gives rise to a fundamental and critical problem, i.e., how to schedule their workloads under the constraint of task dependency while strictly subject to the latency tolerance. In this paper, we attempt to solve this task scheduling problem in the scenario, where both the time and the energy consumed by computing and communication are sufficiently modeled. Since it is NP-hard to such a problem, we propose a greedy algorithm instead so as to obtain an approximate solution. Extensive simulations show that the performance of our proposed algorithm is approximately optimal.

Keywords:
Computer science Cloud computing Distributed computing Mobile cloud computing Energy consumption Schedule Scheduling (production processes) Mobile device Mobile computing Latency (audio) Task (project management) Dependency (UML) Greedy algorithm Task analysis Computer network Mathematical optimization Algorithm Operating system

Metrics

2
Cited By
0.42
FWCI (Field Weighted Citation Impact)
20
Refs
0.66
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Cloud Computing and Resource Management
Physical Sciences →  Computer Science →  Information Systems
Blockchain Technology Applications and Security
Physical Sciences →  Computer Science →  Information Systems
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