Work Allocating Strategy Using a Powerful Prioritized Tasks in Mobile Cloud Computing Atmosphere

2021 ◽  
Vol 9 (11) ◽  
pp. 361-375
Author(s):  
C. Maddilety
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Search Algorithm ◽  
Transmission Rate ◽  
Computation Time ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Cloud

Abstract: In recent times, users necessitate and expect more demanding criteria to perform computational in-depth applications on their mobile devices. Based on the mobile device limitations such as processing power and battery life, Mobile Cloud Computing (MCC) is turned to be a more attractive choice to influence these drawbacks as a mobile computation can be provided to the cloud, which is coined as Mobile computation deceive. Prevailing researches on mobile computation offloading determines offloading mobile computation to single cloud. Moreover, in real time environment, computation service can be offered by multiple clouds for every computation services. Therefore, a novel and an interesting research crisis in mobile computation offloading begins with, how to choose a computation service for every tasks of mobile computation like computation time, energy consumption and cost of using these computation services. This is also termed as multi-site computation offloading in mobile cloud computation. In this examination deceive computation to diverse cloudlets/data centres with respect to task scheduling is formulated for examination. So, a Searching algorithm known as Accelerated Cuckoo Search Algorithm based job splittingis designed to attain higher data transmission rate in the MCC. The results of the certain method outperform the prevailing methods in terms of effectual job splitting; transmission speed, Bandwidth used, execution time of a job, transmission value, through put value, buffering overhead and reduced waiting time. The simulation was carried out in Clouds environment for good output. Keywords: Computation Deceive, Mobile Cloud Computing, Scheduling, Searching Algorithm, WorkSplitting.

Context-Awareness in Opportunistic Mobile Cloud Computing

2015 ◽  
pp. 203-237
Author(s):  
Radu-Corneliu Marin ◽  
Radu-Ioan Ciobanu ◽  
Radu Pasea ◽  
Vlad Barosan ◽  
Mihail Costea ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Context Awareness ◽  
Mobile Cloud Computing ◽  
Search Algorithm ◽  
Power Saving ◽  
Mobile Network ◽  
Handheld Devices ◽  
Mobile Cloud ◽  
Mobile Code ◽  
Battery Technology

Smartphones have shaped the mobile computing community. Unfortunately, their power consumption overreaches the limits of current battery technology. Most solutions for energy efficiency turn towards offloading code from the mobile device into the cloud. Although mobile cloud computing inherits all the Cloud Computing advantages, it does not treat user mobility, the lack of connectivity, or the high cost of mobile network traffic. In this chapter, the authors introduce mobile-to-mobile contextual offloading, a novel collaboration concept for handheld devices that takes advantage of an adaptive contextual search algorithm for scheduling mobile code execution over smartphone communities, based on predicting the availability and mobility of nearby devices. They present the HYCCUPS framework, which implements the contextual offloading model in an on-the-fly opportunistic hybrid computing cloud. The authors emulate HYCCUPS based on real user traces and prove that it maximizes power saving, minimizes overall execution time, and preserves user experience.

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.

An Adaptive Approach Towards Computation Offloading for Mobile Cloud Computing

2019 ◽  
Vol 14 (2) ◽  
pp. 52-73 ◽  
Author(s):  
Archana Kero ◽  
Abhirup Khanna ◽  
Devendra Kumar ◽  
Amit Agarwal
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Adaptive Computation ◽  
Proposed Model ◽  
Environment Variables ◽  
Real Time Information ◽  
Time Information

The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. Mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.

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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