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Dynamic Job Scheduling Using Ant Colony Optimization for Mobile Cloud Computing
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DOI：10.1007/978-3-319-11227-5_7.png)
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Cloud computing has been considered as one of the important computing paradigm. Its main purpose is to share computing resources. With the current scenario there is no doubting the incredible impact that mobile technologies have had on both in scientific and commercial applications. The integration of emerging cloud computing concept and the potential mobile communication services is together considered as Mobile Cloud Computing (MCC). A prominent challenge by using mobile devices and the mobile cloud [1] is resource constraints of these handheld devices. The computational complexities in mobile devices compared to the desktop computers are due to its smaller screen size, less memory capacity, lower processing capacity and low battery backup. Due to these resource limitations most of the processing and data handlings are carried out in the cloud, which is known as software as a service (SaaS) cloud. The smart phones are used to access could resources by using the browser. Performance of this mobile cloud is impaired by the time varying characteristics such as, latency, jitter and bandwidth of the wireless channel. In this research we propose a modified task scheduling mechanism called Ant Colony Optimization (ACO) to address the issues related to the performance of mobile devices [ 5] when used in a cloud environment and Hadoop. However there are bottlenecks related to the existing task scheduling techniques in MCC model which uses the built in FIFO algorithm for large amount of tasks. The proposed Ant Colony Optimization algorithm improve the task scheduling process by dynamically scheduling the tasks and improve the throughput and quality of service (QoS) of MCC.
Keyword:
Mobile Cloud Computing (MCC)
Ant Colony Optimization (APO)
Hadoop
Quality of Service (QoS)
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