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Task-Aware Energy-Efficient Framework for Mobile Cloud Computing

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ICT Systems and Sustainability

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1077))

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Abstract

Mobile devices, including smartphones, are becoming an important part of our daily lives. These devices have powerful features and useful applications that help us to accomplish multiple tasks in minimum time, especially in cloud services. Mobile devices, on the other hand, have limitations like battery life, computing power, and storage capacity. Mobile Cloud Computing (MCC) is a rising technology that helps mobile users to keep away from these limitations, primarily to save energy. In this article, we have discussed energy efficiency in MCC because it is the most important design requirement for mobile devices. Modified Best Fit Decreasing (MBFD) algorithm is used to sort the users as per their task. To minimize energy consumption and completion time required for completing the tasks optimization algorithm named as Artificial Bee Colony (ABC) with supervised learning technique Support Vector Machine (SVM) is used. The performance of the proposed MCC model is analyzed on the basis of energy consumption and completion time. It is analyzed that energy consumption and completion time are reduced by 36.12% and 8.12% respectively.

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Authors and Affiliations

  1. Department of Information Technology, UIET, Panjab University, Chandigarh, India

    Priyanka Sharma, Raj Kumari & Inderdeep Kaur Aulakh

Authors
  1. Priyanka Sharma
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  2. Raj Kumari
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  3. Inderdeep Kaur Aulakh
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Editor information

Editors and Affiliations

  1. Belgrade, Serbia

    Milan Tuba

  2. ITM University, Gwalior, India

    Shyam Akashe

  3. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Sharma, P., Kumari, R., Aulakh, I.K. (2020). Task-Aware Energy-Efficient Framework for Mobile Cloud Computing. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_41

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