The Experts below are selected from a list of 17535 Experts worldwide ranked by ideXlab platform
Rajkumar Buyya - One of the best experts on this subject based on the ideXlab platform.
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a trust based agent learning model for service composition in Mobile Cloud Computing environments
IEEE Access, 2019Co-Authors: Jian Cao, Rajkumar BuyyaAbstract:Mobile Cloud Computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the Mobile Cloud Computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers’ service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the Mobile Cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.
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seamless application execution in Mobile Cloud Computing
Journal of Network and Computer Applications, 2015Co-Authors: Ejaz Ahmed, Abdullah Gani, Rajkumar Buyya, Muhammad Khurram Khan, Samee U. KhanAbstract:Seamless application execution is vital for the usability of various delay-sensitive Mobile Cloud applications. However, the resource-intensive migration process and intrinsic limitations of the wireless medium impede the realization of seamless execution in Mobile Cloud Computing (MCC) environment. This work is the first comprehensive survey that studies the state-of-the-art Cloud-based Mobile application execution frameworks (CMAEFs) in perspective of seamless application execution in MCC and investigates the frameworks suitability for the seamless execution. The seamless execution enabling approaches for the CMAEFs are identified and classified based on the implementation locations. We also investigate the seamless application execution enabling approaches to identify advantages and disadvantages of employing such approaches for attaining the seamless application execution in MCC. The existing frameworks are compared based on the significant parameters derived from the taxonomy of the seamless application execution enabling approaches. The principles for enabling the seamless application execution within the MCC are also highlighted. Finally, open research challenges in realizing the seamless application execution are discussed.
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application partitioning algorithms in Mobile Cloud Computing
Journal of Network and Computer Applications, 2015Co-Authors: Jieyao Liu, Muhammad Shiraz, Rajkumar Buyya, Abdullah Gani, Ejaz Ahmed, Ahsan QureshiAbstract:Mobile Cloud Computing (MCC) enables the development of computational intensive Mobile applications by leveraging the application processing services of computational Clouds. Contemporary distributed application processing frameworks use runtime partitioning of elastic applications in which additional Computing resources are occurred in runtime application profiling and partitioning. A number of recent studies have highlighted the different aspects of MCC. Current studies, however, have overlooked into the mechanism of application partitioning for MCC. We consider application partitioning to be an independent aspect of dynamic computational offloading and therefore we review the current status of application partitioning algorithms (APAs) to identify the issues and challenges. To the best of our knowledge, this paper is the first to propose a thematic taxonomy for APAs in MCC. The APAs are reviewed comprehensively to qualitatively analyze the implications and critical aspects. Furthermore, the APAs are analyzed based on partitioning granularity, partitioning objective, partitioning model, programming language support, presence of a profiler, allocation decision, analysis technique, and annotation. This paper also highlights the issues and challenges in partitioning of elastic application to assist in selecting appropriate research domains and exploring lightweight techniques of distributed application processing in MCC.
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A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service
2015 IEEE 8th International Conference on Cloud Computing, 2015Co-Authors: Bowen Zhou, Amir Vahid Dastjerdi, Rodrigo N. Calheiros, Satish Narayana Srirama, Rajkumar BuyyaAbstract:—Mobile Cloud Computing (MCC) has drawn signif-icant research attention as the popularity and capability of Mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple Cloud resources such as Mobile ad-hoc network, Cloudlet and public Clouds to provide a adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential Cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and Cloud resources based on different context of the Mobile devices, and achieve significant performance improvement.
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Heterogeneity in Mobile Cloud Computing: Taxonomy and open challenges
IEEE Communications Surveys and Tutorials, 2014Co-Authors: Zohreh Sanaei, Saeid Abolfazli, Abdullah Gani, Rajkumar BuyyaAbstract:The unabated flurry of research activities to augment various Mobile devices by leveraging heterogeneous Cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in Mobile Computing and Cloud Computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent Computing (i.e. Mobile Computing and Cloud Computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform Mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.
Houbing Song - One of the best experts on this subject based on the ideXlab platform.
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Elasticity Debt Analytics Exploitation for Green Mobile Cloud Computing: An Equilibrium Model
IEEE Transactions on Green Communications and Networking, 2019Co-Authors: Georgios Skourletopoulos, Houbing Song, Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla, John N. Sahalos, Evangelos PallisAbstract:Mobile Cloud Computing is the model to ubiquitously access a shared pool of Cloud Computing resources, data, and services on-demand. This paper introduces the elasticity debt analytics paradigm as a solution concept for the resource provisioning problem in Mobile Cloud Computing environments, guaranteeing the quality of service requirements. A novel green-centric, game theoretic approach to minimizing the elasticity debt on Mobile Cloud-based service level is proposed, investigating the Mobile Cloud offloading case. The decision to offload a Mobile device user's task on Cloud affects the level of elasticity debt minimization for the provided services. The modeling for the computation of the processing time, energy, and overhead in Mobile opportunistic offloading is presented. A utility-driven elasticity debt and profit quantification approach is also examined for maximization of resource utilization, exploiting the hidden Markov model. The problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to predict elasticity debt, mitigate the risk of service over-utilization, achieve scalability, and optimize Cloud resource provisioning. The experimental results prove the effectiveness of the equilibrium model, which allocates the Mobile device user requests to high elasticity debt-level services and facilitates the elasticity debt minimization for green Mobile Cloud Computing environments.
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Elasticity Debt Analytics Exploitation for Green Mobile Cloud Computing: An Equilibrium Model
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Georgios Skourletopoulos, Houbing Song, Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla, John N. Sahalos, Evangelos PallisAbstract:Mobile Cloud Computing is being accepted as the model for Mobile users to ubiquitously access a shared pool of Cloud Computing resources, data and services on-demand. In this context, elasticity debt analytics can be harnessed as a measure for efficient scheduling of Cloud resources and guarantee of quality of service requirements. This paper proposes a novel green-driven, game theoretic approach to minimizing the elasticity debt on Mobile Cloud-based service level, investigating the case when a task is offloaded, scheduled and executed on a Mobile Cloud Computing system. The decision to offload a Mobile device user's task on Cloud affects the level of elasticity debt minimization for the provided services. The research problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to: (a) predict elasticity debt and mitigate the risk of service overutilization, (b) achieve scalability as the number of Mobile device user requests for Cloud resources increases or decreases accordingly, and (c) optimize Cloud resource provisioning, parameterizing the current pool of active users per service. The experimental results prove the effectiveness of the equilibrium model, which allocates the Mobile device user requests to high elasticity debt-level services and facilitate elasticity debt minimization for greener Mobile Cloud Computing environments.
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A Mobile Cloud Computing Model Using the Cloudlet Scheme for Big Data Applications
2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016Co-Authors: Lo’ai A. Tawalbeh, Waseem Bakheder, Houbing SongAbstract:The wide spread of smart phones and their capabilities made them an important part of many people's life over the world. However, there are many challenges facing these devices such as: low Computing power and fast energy drain from their batteries. One solution is to use Mobile Cloud Computing services to run certain tasks at the Cloud and returning back the results to the Mobile device saving space and processing power. In this research, we introduce efficient Mobile Cloud Computing model based on the Cloudlet sheme. In our model, the Mobile device don't need to communicate with the enterprise Cloud server and instead contact the Cloudlet directly using cheaper technologies such as Wi-Fi, and no need for 3G/4G. Also, we propose a master-Cloudlet management scheme to organize the communication between the Cloudlets themselves. Our efficient Mobile Cloud Computing model can be applied in many environments including universities and hospitals were big amounts of data is collected, stored and processed. The real implementation results show that our model out performs classical non-Cloudlet Mobile Cloud Computing models.
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Cloudlet-Based Mobile Cloud Computing for Healthcare Applications
2016 IEEE Global Communications Conference (GLOBECOM), 2016Co-Authors: W Bakhader, Rashid Mehmood, Houbing SongAbstract:The smart phones are used in many aspects of our life, shopping on the Internet, and creating and distributing many types of files. But these devices have many limitations including: short battery life time and limited storage and processing. Mobile Cloud Computing technology can help to overcome these limitations. Offloading technique reduces the power consumption and saves the Mobile storage by executing the huge tasks at the Cloud. The Mobile devices are connecting to Cloud service providers using 3G or LTE technologies, which introduces some challenges including, limited bandwidth, cost, and latency. In this paper, we propose efficient and secure Mobile Cloud Computing model based on the Cloudlet concept were the Mobile devices users can connect directly to Cloud resources using cheaper technologies such as Wi-Fi. Once needed, and only if the service is not available in the Cloudlet, the user will be connected to the enterprise Cloud. The proposed Cloudlet-based model can be used in many applications where security and efficiency is required. It can be used for health applications to save and analyze patients medical records. The simulation results of our model show that it is more efficient and reliable than the other Mobile could Computing models that don't use the Cloudlet
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Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications
IEEE Access, 2016Co-Authors: Elhadj Benkhlifa, Lo’ai A. Tawalbeh, Rashid Mehmood, Houbing SongAbstract:Mobile devices are increasingly becoming an indispensable part of people's daily life, facilitating to perform a variety of useful tasks. Mobile Cloud Computing integrates Mobile and Cloud Computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of Mobile Cloud Computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of Cloud Computing in healthcare. A Cloudlet-based Mobile Cloud-Computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and Mobile Cloud-Computing technologies. An outlook on networked healthcare is given.
Abdullah Gani - One of the best experts on this subject based on the ideXlab platform.
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Process state synchronization for mobility support in Mobile Cloud Computing
2017 IEEE International Conference on Communications (ICC), 2017Co-Authors: Ejaz Ahmed, Anjum Naveed, Siti Hafizah Ab Hamid, Abdullah Gani, Muhammad Imran, Mohsen GuizaniAbstract:Mobile Cloud Computing (MCC) extends Cloud services to the resource-constrained Mobile devices. Compute-intensive Mobile applications can be augmented using Cloud either in client/server model or through cyber foraging. However, long or permanent network disconnections due to user mobility increase the execution time and in certain cases refrain the Mobile devices from getting response back for the remotely performed execution. In this paper, we propose use of process state synchronization (PSS) as a mechanism to mitigate the impact of network disconnections on the service continuity of Cloud-based interactive Mobile applications. To validate the PSS-based execution, we develop a mathematical model that incorporates the disconnection and synchronization intervals, and Mobile device capabilities along with that of Cloud. The comparison with existing mechanisms shows that PSS reduces the execution time by upto 47% for intermittent network connectivity compared to COMET and by upto 35% for optimized VM-based offloading.
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seamless application execution in Mobile Cloud Computing
Journal of Network and Computer Applications, 2015Co-Authors: Ejaz Ahmed, Abdullah Gani, Rajkumar Buyya, Muhammad Khurram Khan, Samee U. KhanAbstract:Seamless application execution is vital for the usability of various delay-sensitive Mobile Cloud applications. However, the resource-intensive migration process and intrinsic limitations of the wireless medium impede the realization of seamless execution in Mobile Cloud Computing (MCC) environment. This work is the first comprehensive survey that studies the state-of-the-art Cloud-based Mobile application execution frameworks (CMAEFs) in perspective of seamless application execution in MCC and investigates the frameworks suitability for the seamless execution. The seamless execution enabling approaches for the CMAEFs are identified and classified based on the implementation locations. We also investigate the seamless application execution enabling approaches to identify advantages and disadvantages of employing such approaches for attaining the seamless application execution in MCC. The existing frameworks are compared based on the significant parameters derived from the taxonomy of the seamless application execution enabling approaches. The principles for enabling the seamless application execution within the MCC are also highlighted. Finally, open research challenges in realizing the seamless application execution are discussed.
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application optimization in Mobile Cloud Computing
Journal of Network and Computer Applications, 2015Co-Authors: Ejaz Ahmed, Siti Hafizah Ab Hamid, Abdullah Gani, Mehdi Sookhak, Feng XiaAbstract:In Mobile Cloud Computing (MCC), migrating an application processing to the Cloud data centers enables the execution of resource-intensive applications on the Mobile devices. However, the resource-intensive migration approaches and the intrinsic limitations of the wireless medium impede the applications from attaining optimal performance in the Cloud. Hence, executing the application with low cost, minimal overhead, and non-obtrusive migration is a challenging research area. This paper presents the state-of-the-art Mobile application execution frameworks and provides the readers a discussion on the optimization strategies that facilitate attaining the effective design, efficient deployment, and application migration with optimal performance in MCC. We highlight the significance of optimizing the application performance by providing real-life scenarios requiring the effective design, efficient deployment, and optimal application execution in MCC. The paper also presents Cloud-based Mobile application-related taxonomies. Moreover, we compare the application execution frameworks on the basis of significant optimization parameters that affect performance of the applications and Mobile devices in MCC. We also discuss the future research directions for optimizing the application in MCC. Finally, we conclude the paper by highlighting the key contributions and possible research directions in Cloud-based Mobile application optimization.
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application partitioning algorithms in Mobile Cloud Computing
Journal of Network and Computer Applications, 2015Co-Authors: Jieyao Liu, Muhammad Shiraz, Rajkumar Buyya, Abdullah Gani, Ejaz Ahmed, Ahsan QureshiAbstract:Mobile Cloud Computing (MCC) enables the development of computational intensive Mobile applications by leveraging the application processing services of computational Clouds. Contemporary distributed application processing frameworks use runtime partitioning of elastic applications in which additional Computing resources are occurred in runtime application profiling and partitioning. A number of recent studies have highlighted the different aspects of MCC. Current studies, however, have overlooked into the mechanism of application partitioning for MCC. We consider application partitioning to be an independent aspect of dynamic computational offloading and therefore we review the current status of application partitioning algorithms (APAs) to identify the issues and challenges. To the best of our knowledge, this paper is the first to propose a thematic taxonomy for APAs in MCC. The APAs are reviewed comprehensively to qualitatively analyze the implications and critical aspects. Furthermore, the APAs are analyzed based on partitioning granularity, partitioning objective, partitioning model, programming language support, presence of a profiler, allocation decision, analysis technique, and annotation. This paper also highlights the issues and challenges in partitioning of elastic application to assist in selecting appropriate research domains and exploring lightweight techniques of distributed application processing in MCC.
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Heterogeneity in Mobile Cloud Computing: Taxonomy and open challenges
IEEE Communications Surveys and Tutorials, 2014Co-Authors: Zohreh Sanaei, Saeid Abolfazli, Abdullah Gani, Rajkumar BuyyaAbstract:The unabated flurry of research activities to augment various Mobile devices by leveraging heterogeneous Cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in Mobile Computing and Cloud Computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent Computing (i.e. Mobile Computing and Cloud Computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform Mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.
Saeid Abolfazli - One of the best experts on this subject based on the ideXlab platform.
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authentication in Mobile Cloud Computing
Journal of Network and Computer Applications, 2016Co-Authors: Mojtaba Alizadeh, Saeid Abolfazli, Mazdak Zamani, Sabariah Baharun, Kouichi SakuraiAbstract:Mobile Cloud Computing (MCC) is the state-of-the-art Mobile distributed Computing model that incorporates multitude of heterogeneous Cloud-based resources to augment computational capabilities of the plethora of resource-constraint Mobile devices. In MCC, execution time and energy consumption are significantly improved by transferring execution of resource-intensive tasks such as image processing, 3D rendering, and voice recognition from the hosting Mobile to the Cloud-based resources. However, accessing and exploiting remote Cloud-based resources is associated with numerous security and privacy implications, including user authentication and authorization. User authentication in MCC is a critical requirement in securing Cloud-based computations and communications. Despite its critical role, there is a gap for a comprehensive study of the authentication approaches in MCC which can provide a deep insight into the state-of-the-art research. This paper presents a comprehensive study of authentication methods in MCC to describe MCC authentication and compare it with that of Cloud Computing. The taxonomy of the state-of-the-art authentication methods is devised and the most credible efforts are critically reviewed. Moreover, we present a comparison of the state-of-the-art MCC authentication methods considering five evaluation metrics. The results suggest the need for futuristic authentication methods that are designed based on capabilities and limitations of MCC environment. Finally, the design factors deemed could lead to effective authentication mechanisms are presented, and open challenges are highlighted based on the weaknesses and strengths of existing authentication methods.
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Heterogeneity in Mobile Cloud Computing: Taxonomy and open challenges
IEEE Communications Surveys and Tutorials, 2014Co-Authors: Zohreh Sanaei, Saeid Abolfazli, Abdullah Gani, Rajkumar BuyyaAbstract:The unabated flurry of research activities to augment various Mobile devices by leveraging heterogeneous Cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in Mobile Computing and Cloud Computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent Computing (i.e. Mobile Computing and Cloud Computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform Mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.
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tripod of requirements in horizontal heterogeneous Mobile Cloud Computing
arXiv: Distributed Parallel and Cluster Computing, 2012Co-Authors: Zohreh Sanaei, Saeid Abolfazli, Abdullah Gani, Rashid Hafeez KhokharAbstract:Recent trend of Mobile Computing is emerging toward executing resource-intensive applications in Mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of Cloud Computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment Computing and storage capabilities of Mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to Cloud Computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on Mobile-Cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency.
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tripod of requirements in horizontal heterogeneous Mobile Cloud Computing
Systems Communications, 2012Co-Authors: Zohreh Sanaei, Saeid Abolfazli, Abdullah Gani, Rashid Hafeez KhokharAbstract:Recent trend of Mobile Computing is emerging toward executing resource-intensive applications in Mobile devices regardless of underlying resource restrictions (e.g. limited processor and energy) that necessitate imminent technologies. Prosperity of Cloud Computing in stationary computers breeds Mobile Cloud Computing (MCC) technology that aims to augment Computing and storage capabilities of Mobile devices besides conserving energy. However, MCC is more heterogeneous and unreliable (due to wireless connectivity) compare to Cloud Computing. Problems like variations in OS, data fragmentation, and security and privacy discourage and decelerate implementation and pervasiveness of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem and identify thirteen critical metrics and approaches that influence on Mobile-Cloud solutions and success of MCC. We divide them into three major classes, namely ubiquity, trust, and energy efficiency and devise a tripod of requirements in MCC. Our proposed tripod shows that success of MCC is achievable by reducing mobility challenges (e.g. seamless connectivity, fragmentation), increasing trust, and enhancing energy efficiency. Key-Word: Mobile Cloud Computing, Heterogeneity, Ubiquitous Computing, Context-Awareness, Trust, Energy Efficiency.
Hyukjin Chae - One of the best experts on this subject based on the ideXlab platform.
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energy efficient Mobile Cloud Computing powered by wireless energy transfer
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Changsheng You, Kaibin Huang, Hyukjin ChaeAbstract:Achieving long battery lives or even self sustainability has been a long standing challenge for designing Mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, Mobile Cloud Computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable Computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a Mobile to the Cloud; the Mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient Computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local Computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully Computing given data, called Computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the Mobile energy consumption for local Computing and maximizing the Mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the Computing probability. Last, simulation demonstrates the feasibility of wirelessly powered Mobile Cloud Computing and the gain of its optimal control.
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energy efficient Mobile Cloud Computing powered by wireless energy transfer
arXiv: Information Theory, 2015Co-Authors: Changsheng You, Kaibin Huang, Hyukjin ChaeAbstract:Achieving long battery lives or even self sustainability has been a long standing challenge for designing Mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, Mobile Cloud Computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable Computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a Mobile to the Cloud; the Mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient Computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local Computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully Computing given data, called Computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the Mobile energy consumption for local Computing and maximizing the Mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases Computing probability. Last, simulation demonstrates the feasibility of wirelessly powered Mobile Cloud Computing and the gain of its optimal control.