Research Article An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing Aymen Abdullah Alsaffar, 1 Hung Phuoc Pham, 1 Choong-Seon Hong, 1 Eui-Nam Huh, 1 and Mohammad Aazam 2 1 Department of Computer Engineering, Kyung Hee University, Yongin-si, Seoul, Republic of Korea 2 Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada Correspondence should be addressed to Eui-Nam Huh; johnhuh@khu.ac.kr Received 29 April 2016; Revised 25 July 2016; Accepted 25 August 2016 Academic Editor: Young-June Choi Copyright © 2016 Aymen Abdullah Alsafar et al. Tis 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 beneft 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 efcient 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 efciently balance workload, improve resource allocation efciently, optimize big data distribution, and show better performance than other existing methods. 1. Introduction Cloud computing is not only a technology that continuously advances for ofering a variety of services and resources to many cloud consumers smart devices (e.g., IoT, smart wearable devices, smart phone, smart tablets, and smart home appliances) but also an enabling developer to develop more applications, tools, and services. Cloud computing architecture can empower ubiquitous, advantageous, and on- demand network access to a shared pool of confgurable com- puting resources, providing many other benefts (e.g., stor- ages, services, applications, networks, virtualized resources, large scale computation, schedulable virtual servers, high expansibility, computing power, low price services, virtual network, network bandwidth, and high reliability) [1–3]. One of the technologies that is gaining popularity is known as Internet of things (IoT). IoT is a technology that is still developing and enables many objects (e.g., thin-client, smart phone, smart tablets, smart home appliances, smart wearable devices, and sensor) to connect to Internet to perform variety of services (e.g., memory, storage space, processing, virtualization, resource allocation, services delegation, surf- ing, send/receive big data, and viewing social sites). Tus, smart devices services are present in every aspect of our daily life (e.g., health care, medicine treatment, education, and remotely controlled smart devices). Cloud computing technology is being widely used to support variety of cloud consumer devices, services, and applications. Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, Hindawi Publishing Corporation Mobile Information Systems Volume 2016, Article ID 6123234, 15 pages http://dx.doi.org/10.1155/2016/6123234