In the continous evolving world of mobile computing, optimization of resources for achieving high performance is always the primary rule when developing sophisticated mobile applications. Generally, the richness of an application can only be achieved if it is possible to perform intensive computations on the device. A rich mobile application is one, in which through a real-time interactivity, huge amount of information is processed and presented to the user as a single result. Performing such task in a mobile phone is difficult due to the hardware limitations. Thus, computation offloading is needed for extending the capabilities of the mobile applications in order to cover high user demands in functionality. Useful tools: https://github.com/huberflores/CodeOffloadingAnnotations https://github.com/huberflores/MobileCloudMiddleware
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— In recent years the Smartphone has undergone significant technological advancements but still remains a low computational entity. Mobile Cloud Computing addresses this problem and provides a solution in form of Mobile Computation Offloading (MCO). Computation offloading is a concept in which certain parts (tasks) of an application are executed on cloud whereas the rest of them on the mobile device itself. MCO turns out to be a great help with respect to resource constrained mobile derives as it allows resource intensive tasks to be executed remotely. Though such procedures save mobile resources but incur communication cost between the mobile device and cloud. Thus, it becomes extremely essential for offloading models to take steps (offloading decisions) in order to augment the capabilities of mobile devices along with reduced execution and communication costs. In this paper, we present an application offloading model which offloads an application based upon the nature and execution pattern of its tasks. We also propose an algorithm that depicts the work flow of our computation model. The proposed model is simulated using the CloudSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.
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The current proliferation of mobile systems, such as smart phones, PDA and tablets, has led to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device. However, this convergence penalizes the mobile system in computational resources such as processor speed, memory consumption, disk capacity, as well as in weight, size, ergonomics and the user’s most important component, battery life. Therefore, this current trend aims towards the efficient and effective use of its hardware and software components. Hence, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for off-loading computation and dat.
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Resumen The inherently limited processing power and battery lifetime of mobile phones hinder the possible execution of computationally intensive applications like content-based video analysis or 3D modeling. Offloading of computationally intensive application parts from the mobile platform into a remote cloud infrastructure or nearby idle computers addresses this problem.
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Mobile device functionality is ever richer in daily life, but they still lack the required amount of resources when it comes to handling resourcehungry applications. Nowadays, Mobile Cloud Computing (MCC) bridges the gap between the limited capabilities of mobile devices (battery life, network bandwidth, storage capacity, processor performance) and the increasing user demand of mobile applications, by offloading the computational workloads from local devices to the remote cloud. This paper provides an overview of the back-ground, techniques, and research areas for computation offloading. Moreover, the issues, existing solutions, and approaches are presented.
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Advances in Intelligent Systems and Computing
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Journal of Supercomputing
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Mobile Cloud Computing is an evolving technology that integrates the concept of cloud computing into the mobile environment. Smartphones are boon in the world of technology but they have certain limitations (e.g. battery life, network bandwidth, storage, energy) when running complex applications which require large computations. Using Cloud Computing in mobile phones, these limitations can be addressed. Certain frameworks have been proposed over the years that can address the issues in mobile cloud computing. These frameworks allow the computation to be offloaded in the cloud environment resulting improvementin the battery life, bandwidth, memory capacity and energy consumption in the smartphones. This paper provides the extensive surveyon the various energy efficient frameworks available to offload the computation from the mobile to the cloud environment. INTRODUCTION Smart Mobile Devices (SMDs) such as smart-phones and tablets Personal Computers (PCs) have become important part of daily life. Nowadays, mobile devices are not only used for voice calls but are efficiently able to run heavy and complicated mobile applications using internet. The volume of data being processed by smartphones and complexity of mobile applications are increasing day by day. However, these SMD has certain limitations such as network bandwidth, battery lifeand storage capacity and processor performance. With the advancement in technology over the years, SMD provide multi-core processors, large memory, bigger and sharper screens, multiple sensors as well as enormous applications. All these together put heavy burden on the battery life of smart phones. Cloud computing (CC) provides a wide variety of computing resources from servers and storage to enterprise applications. Cloud computing is a hosting environment that is immediate, flexible, scalable, secure and available. The computing resources from cloud can be easily and quickly accessed and released after use with very less management effort. The concept of CC can be used in mobile applications running on SMDs to boost up their performance. With the integration and support of CC into the complex mobile applications, the term Mobile Cloud Computing (MCC) arises. MCC provides an infrastructure where the data processing and the data storage of mobile cloud applications occur away from the mobile device and into the cloud and bringing applications and mobile computing to a much broader range of mobile subscribers [1]. One of the key features of MCC is migrating the computation intensive tasks to cloud or servers for their executionand then receiving the results from these servers which is known as Computation Offloading. Mobile Devices take advantage of resource-rich infrastructures by offloading the computation to cloud for saving battery's energy consumption and hence increasing battery lifetime of mobiles. Computation offloading allows SMDs to become more capable. In contrast to traditional client-server architecture, where clients always offloads the computation to the server and are completely dependent on it, the computational offloading migrates programs to servers which are outside of the user's computing environment. The term " surrogate computing " or " cyber foraging " is also used for computation offloading.
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— The advancements in the world of mobile technology have enabled computational intensive applications on the latest smart phones. Still Smart Mobile Devices (SMD's) have low potential and are unable to run complex applications due to their limited battery life, storage capacity, processor speed and energy. These limitations of smart phones can be addressed through Mobile Cloud Computing (MCC) which allows the computational intensive applications from smart phones to get offloaded over the cloud environment. In this paper, computation offloading scheme has been proposed that distributes the workload among various Virtual Machines (VM's) on the basis of their distance from the current VM. The offloading will decrease the load on the current VM and increase the performance and speed of execution. The Buffer Allocation Method (BAM) is proposed which will further enhance the performance by eliminating the redundancy of the data to be transmitted during offloading. The results indicate that the speed of execution increases, energy consumption decreases and the load gets balanced due to offloading.
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