Partitioning and offloading in smart mobile devices for mobile cloud computing: State of the art and future directions

2018 ◽  
Vol 119 ◽  
pp. 83-96 ◽  
Author(s):  
Fei Gu ◽  
Jianwei Niu ◽  
Zhiping Qi ◽  
Mohammed Atiquzzaman
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
State Of The Art ◽  
Mobile Cloud ◽  
Future Directions ◽  
Smart Mobile ◽  
Smart Mobile Devices

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

2020 ◽  
Vol 39 (6) ◽  
pp. 8285-8297
Author(s):  
V. Meena ◽  
Obulaporam Gireesha ◽  
Kannan Krithivasan ◽  
V.S. Shankar Sriram
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Computational Offloading ◽  
Smart Mobile ◽  
Smart Mobile Devices

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

scholarly journals An Adequate Computation Offloading in Mobile Cloud Computing

2019 ◽  
pp. 208-216
Author(s):  
CH. Ellaji ◽  
C. Pradeepthi ◽  
P. JayaSri
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Storage Capacity ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Battery Lifetime ◽  
Smart Mobile ◽  
Smart Mobile Devices

The development and upgrades that mobile gadgets have encountered, they are as yet considered as restricted registering gadgets. Presently, clients become the more requesting and hope to execute computational serious applications on their mobile gadgets. Accordingly, Mobile Cloud Computing (MCC) coordinates versatile figuring and Cloud Computing (CC) so as to broaden capacities of mobile devices utilizing of?oading procedures. Computational of?oading handles restrictions of Smart Mobile Devices (SMDs, for example, constrained battery lifetime, constrained preparing capabilities, and constrained Storage capacity by of?oading the execution and outstanding burden to other rich frameworks with better execution and assets. Here, the current of?oading systems, Compuatational of?oading strategies, and evaluate them alongside their principle basic issues. In addition, it investigates distinctive significant parameters dependent on which the systems are actualized, for example, of?oading technique and level of dividing. At last, it condenses the issues in of?oading systems in the MCC space that requires further research.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

Accelerating Mobile-Cloud Computing

2013 ◽  
pp. 175-197 ◽  
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today’s mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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