Service Provisioning in Mobile Environments through Opportunistic Computing

Abstract

Opportunistic computing is a paradigm for completely self-organised pervasive networks. Instead of relying only on fixed infrastructures as the cloud, users' devices act as service providers for each other. They use pairwise contacts to collect information about services provided and amount of time to provide them by the encountered nodes. At each node, upon generation of a service request, this information is used to choose the most efficient service, or composition of services, that satisfy that request, based on local knowledge. Opportunistic computing can be exploited in several scenarios, including mobile social networks, IoT, and Internet 4.0. In this paper, we propose an opportunistic computing algorithm based on an analytical model, which ranks the available (composition of) services, based on their expected completion time. Through the model, a service requester picks the one that is expected to be the best. Experiments show that the algorithm is accurate in ranking services, thus providing an effective service-selection policy. Such a policy achieves significantly lower service provisioning times compared to other reference policies. Its performance is tested in a wide range of scenarios varying the nodes mobility, the size of input/output parameters, the level of resource congestion, and the computational complexity of service executions.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research

Comments

This work was partially funded by the EC under the H2020 REPLICATE (691735), SoBigData (654024), and AUTOWARE (723909) projects.

Keywords and Phrases

Analytical models; Carrier mobility; Cloud computing; Mobile computing; Quality of service; Reliability; Social sciences computing; Computational model; Loss measurement; Mobile handsets; Opportunistic networks; Service compositions; Distributed computer systems; Analytical modelling; Mobility

International Standard Serial Number (ISSN)

1536-1233; 1558-0660

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2018

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