Exploiting composition of mobile devices for maximizing user QoS under energy constraints in mobile grid

The mobile grids bring some additional features into the grid, such as mobility, energy-constrained, etc. And the task scheduling becomes a more challenge thing. We propose a mobile grid task scheduling model considering the mobility of both user and resource, and the resource energy consumption. Through analyzing the architecture of mobile grid, a mathematical model is built to calculate the average distance between the resource and Base Station (BS). Then, it can decide which mobile grid the mobile devices are apt to stay in, which can deal with the mobility of mobile devices. On the other hand, the resource energy consumption is also considered, which ensure that the resources have enough energy to finish the task. As a result, the task can be assigned to the best resources in the suitable mobile grids. The failures may happen in the task scheduling because of many unpredictable factors. So the fault-tolerance scheme based on the notion of replication is proposed.

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Resource Allocation and Provisioning in Computational Mobile Grid

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2015040101 ◽

2015 ◽

Vol 6 (2) ◽

pp. 1-24 ◽

Cited By ~ 1

Author(s):

Dinesh Prasad Sahu ◽

Karan Singh ◽

Shiv Prakash

Keyword(s):

Resource Allocation ◽

Mobile Devices ◽

Mobile Agent ◽

Fixed Number ◽

Mobile Grid ◽

Java Language ◽

Computational Capability ◽

Proposed Model ◽

Drastic Increase

Recent years have seen drastic increase in number of mobile devices which are becoming popular not only by their communication flexibility but also for their computational capability. A collection of mobile devices together form a grid. In the proposed model, it is assumed that the set of jobs are accumulated to the primary machine, though they might have been submitted anywhere in the grid. It is also assumed that each job consists of one or more number of sub jobs. Mobile Grid comprises with number of machines and speed of execution of individual processor may be different. Each machine can handle fixed number of sub jobs. A set of jobs accumulated at the primary machines are distributed to different secondary machines. A rigorous set of experiment has been carried out by simulating the model using java language on Eclipse IDE integrated with Gridsim. The model has been tested with various numbers of inputs in different cases and result has been observed. The authors found some of the key findings of the experiments. In most of the cases, resource allocation is better when mobile agent is employed for the work.

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Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent

The Knowledge Engineering Review ◽

10.1017/s0269888914000149 ◽

2014 ◽

Vol 29 (4) ◽

pp. 409-432 ◽

Cited By ~ 2

Author(s):

Jonghyuk Lee ◽

Sungjin Choi ◽

Taeweon Suh ◽

Heonchang Yu

Keyword(s):

Load Balancing ◽

Mobile Devices ◽

Scheduling Algorithm ◽

Superior Performance ◽

Battery Life ◽

Location Awareness ◽

Message Delivery ◽

Mobile Grid ◽

Fixed Grid ◽

Grid Resources

AbstractThe emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.

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Computational Mobile Grid

Risk Assessment and Management in Pervasive Computing ◽

10.4018/978-1-60566-220-6.ch003 ◽

2011 ◽

pp. 40-51 ◽

Cited By ~ 1

Author(s):

Deo Prakash Vidyarthi

Keyword(s):

Mobile Devices ◽

Computational Grid ◽

Web Portal ◽

Short Term ◽

Computing Power ◽

Mobile Grid

The proliferation of the capable mobile devices has given the opportunity to utilize these devices for various purposes. The mobile devices being used as a Web portal is its short-term use as these devices have added many features and facility that does not only facilitate communication, but also adds to the huge computing power put together. The chapter proposes how a huge computational grid of these compute capable mobile devices can be formed, and the computing power from such a grid can be extracted. This kind of computational mobile grid put fourth many issues that require great attention before such a concept is fully functional.

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Parallel Processing of Images in Mobile Devices using BOINC

Open Engineering ◽

10.1515/eng-2018-0012 ◽

2018 ◽

Vol 8 (1) ◽

pp. 87-101 ◽

Cited By ~ 2

Author(s):

Mariela Curiel ◽

David F. Calle ◽

Alfredo S. Santamaría ◽

David F. Suarez ◽

Leonardo Flórez

Keyword(s):

Image Processing ◽

Parallel Processing ◽

Mobile Devices ◽

Health Professionals ◽

Medical Image ◽

Medical Image Processing ◽

Mobile Grid ◽

Desktop Computers ◽

Computing Infrastructure ◽

Previous Step

Abstract Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. A mobile grid can be an adequate computing infrastructure for this problem. A mobile grid is a grid that includes mobile devices as resource providers. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. This article presents answers to these four challenges.

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