A computation offloading scheme for performance enhancement of smart mobile devices for mobile cloud computing

2016 ◽  
Author(s):  
Queen Kaur Gill ◽  
Kiranbir Kaur
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Performance Enhancement ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Smart Mobile ◽  
Smart Mobile Devices

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.

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.

High Performance Computing on Mobile Devices

2016 ◽  
pp. 334-348
Author(s):  
Atta ur Rehman Khan ◽  
Abdul Nasir Khan
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Mobile Devices ◽  
High Performance ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Computing Technology ◽  
Wide Range ◽  
Performance Computing

Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.

Mobile Cloud Computing

2015 ◽  
pp. 1-23
Author(s):  
Jyoti Grover ◽  
Gaurav Kheterpal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile Cloud Computing ◽  
Research Area ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Mobile Environment

Mobile Cloud Computing (MCC) has become an important research area due to rapid growth of mobile applications and emergence of cloud computing. MCC refers to integration of cloud computing into a mobile environment. Cloud providers (e.g. Google, Amazon, and Salesforce) support mobile users by providing the required infrastructure (e.g. servers, networks, and storage), platforms, and software. Mobile devices are rapidly becoming a fundamental part of human lives and these enable users to access various mobile applications through remote servers using wireless networks. Traditional mobile device-based computing, data storage, and large-scale information processing is transferred to “cloud,” and therefore, requirement of mobile devices with high computing capability and resources are reduced. This chapter provides a survey of MCC including its definition, architecture, and applications. The authors discuss the issues in MCC, existing solutions, and approaches. They also touch upon the computation offloading mechanism for MCC.

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