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Mobile Information Systems
Volume 2016, Article ID 6123234, 15 pages
Research Article

An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing

1Department of Computer Engineering, Kyung Hee University, Yongin-si, Seoul, Republic of Korea
2Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada

Received 29 April 2016; Revised 25 July 2016; Accepted 25 August 2016

Academic Editor: Young-June Choi

Copyright © 2016 Aymen Abdullah Alsaffar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend cloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload. Moreover, we propose algorithm to allocate resources to meet service level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing. Our simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently, optimize big data distribution, and show better performance than other existing methods.