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A User-Satisfaction Based Offloading Technique for Smart City Applications

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There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks. The following are the major contributions of this paper towards mobile cloud computing: A novel architecture for the dynamic task offloading in MCC is designed, which follows the client‐server model. In this architecture, three modules are integrated into the mobile environment—task partitioning, energy model, and decision‐making engine. An algorithm for decision making to decide whether or where the task has to execute is developed. A collective task execution problem is developed and proposed the solution based on energy and completion time of the submitted job.
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A smart city scenario based on an efficient wireless network allows users to benefit from multimedia services in an ubiquitous, seamless and interoperable way. In this context Mobile Cloud Computing (MCC) and Heterogeneous Networks (HetNets) are viewed as infrastructures providing together a key solution for the major facing problems: the former allows to offload application to powerful remote servers, shortening execution time and extending battery life of mobile devices, while the latter allows the use of small cells in addition to macrocells, exploiting high-speed and stable connectivity in an ever grown mobile traffic trend. In this paper, we propose a technique aiming to move towards the cloud only a fraction of the computing application by minimizing a cost function, that take into account a tradeoff between energy consumption and execution time, in a non-trivial multi-objective optimization approach. The results show that when the application requires high execution and data workload and simultaneously the network is overloaded, a particular value of this percentage best fits the performance.
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Mobile cloud computing (MCC) is an appealing paradigm enabling users to enjoy the vast computation power and abundant network services ubiquitously with the support of remote cloud. However, the wireless networks and mobile devices have to face many challenges due to the limited radio resources, battery power and communications capabilities, which may significantly impede the improvement of service qualities. Heterogeneous Network (HetNet), which has multiple types of low power radio access nodes in addition to the traditional macrocell nodes in a wireless network, is widely accepted as a promising way to satisfy the unrelenting traffic demand. In this article, we first introduce the framework of HetNet for MCC, identifying the main functional blocks. Then, the current state of the art techniques for each functional block are briefly surveyed, and the challenges for supporting MCC applications in HetNet under our proposed framework are discussed. We also envision the future for MCC in HetNet before drawing the conclusion.
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Cloud computation offloading is a promising method that sending heavy computation to resourceful servers on cloud and then receiving the results from them. In this paper, we study the offloading techniques and further explore the tradeoff between shortening execution time and extending battery life of mobile devices. A novel adaptive offloading scheme is proposed and analyzed based on the tradeoff analysis. And it can be realized thanks to the elasticity of cloud computing that the resources can be bought on demand. We have tried to find a server on cloud with a critical value of speedup F for a specified mobile device. When satisfying the requirement such as performance improvement by the system, it is worth sacrificing large F when taking economic factor into consideration.
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Mobile cloud computing is a new rapidly growing field. In addition to the conventional fashion that mobile clients access cloud services as in the well-known client/server model, existing work has proposed to explore cloud functionalities in another perspective - offloading part of the mobile codes to the cloud for remote execution in order to optimize the application performance and energy efficiency of the mobile device. In this position paper, we investigate the state of the art of code offloading for mobile devices, highlight the significant challenges towards a more efficient cloud-based offloading framework, and also point out how existing technologies can provide us opportunities to facilitate the framework implementation.
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The cloud heralds a new era of computing where application services are provided through the Internet. Cloud computing can enhance the computing capability of mobile systems, but is it the ultimate solution for extending such systems' battery lifetimes?
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One of the most promising trends for next generation networks is to consider an integrated approach to the communication infrastructure and the processing layer. In particular, the introduction of broadband and reliable wireless networks allows the interaction of a huge number of devices all creating a single network. On the other hand, the grid paradigm is considered as one of the most promising approach for pervasive and dynamic applications. Aim of this paper is to present a novel integrated approach between grid paradigm and wireless networks by highlighting the main advantages of their cooperation. In particular, it will be shown here how a wireless heterogeneous network can be exploited for implementing a pervasive and dynamic grid (mobile grid) and, on the other hand, a mobile grid allows the optimization of the communication infrastructure. The integrated approach can be an effective method for solving applications, such its emergency management, where a huge amount of data derived from a wireless infrastructure needs to be processed efficiently and adaptively, and the traffic flow in the wide area wireless networks needs to be coordinated and optimized. Copyright (C) 2008 John Wiley & Sons, Ltd.
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Cloud computing heralds a new era of computing where application services are provided through the Internet. Cloud computing can enhance the computing capability of mobile systems. Is cloud computing the ultimate solution for extending battery lifetimes of mobile systems?
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The rapidly increasing demand for mobile data services urges operators to explore alternative technologies to handle the voluminous data traffic on their networks. In this regard, mobile data offloading is expected to become a key industry segment in near future. In this paper, we propose a policy based offloading framework for cellular network which is based on a cost function approach. We discuss user centric, network centric and hybrid policies where the decision making is shared between the user and the network. We also present a novel mechanism for the decision sharing between user and network which is based on principles of autonomic networking and where policies are chosen dynamically according to the variation of network conditions and the operator strategies. Detailed simulation studies are conducted to validate the effectiveness of these policies in real networks.
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Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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In this paper, we study the connection between radio resource management (RRM) and economic parameters, whose application in multimedia communication system is a challenging task. In fact, a real network provider has to consider other parameters, besides the common goals of RRM like throughput maximisation or meeting constraints connected with the quality of service. In particular, when the financial needs of the provider and the reaction of the users to prices are taken into account, economics have to be introduced in the analysis. We intend to study multimedia communication systems by including well-known economic models and reasonable considerations in the usual radio resource allocation scenario. To do this, we present a model of users' satisfaction, which considers the effects of both users' request and price paid. In this way, it is possible to investigate the relationship between the radio resource allocation and the provider revenue. Other conclusions can be derived as well, e.g. for the pricing strategy planning or the network dimensioning. Thus, we give analytical insight and numerical results, which highlight that the network management is heavily affected by the economic scenario.