Abstract
Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure. Smart devices lead to emerging technologies and play a vital role in rapid evolution. Smart devices have become a primary 24/7 need in today’s information technology world and include a wide range of supporting processing-intensive applications. Extensive use of many applications on smart devices results in increasing complexity of mobile software applications and consumption of resources at a massive level, including smart device battery power, processor, and RAM, and hinders their normal operation. Appropriate resource utilization and energy efficiency are fundamental considerations for smart devices because limited resources are sporadic and make it more difficult for users to complete their tasks. In this study we propose the model of mobile energy augmentation using cloud computing (MEACC), a new framework to address the challenges of massive power consumption and inefficient resource utilization in smart devices. MEACC efficiently filters the applications to be executed on a smart device or offloaded to the cloud. Moreover, MEACC efficiently calculates the total execution cost on both the mobile and cloud sides including communication costs for any application to be offloaded. In addition, resources are monitored before making the decision to offload the application. MEACC is a promising model for load balancing and power consumption reduction in emerging mobile computing environments.
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Babur HAYAT guided the research on offloading mechanism using cloud computing. Zuhaib IMTIAZ and Ayesha RAANA designed the research and drafted the manuscript. Khalid ALSUBHI helped organize the manuscript. M. Usman ASHRAF helped design the data collection mechanism and edited and finalized the paper.
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Fig. S1 Total execution time on mobile device D1 (Galaxy Grand Prime)
Fig. S2 Total execution time on mobile device D2 (Google Pixel 2)
Fig. S3 Total execution time on mobile device D3 (Google Pixel 3)
Table S1 Total cloud execution costs (Hadoop simulator)
Fig. S4 Resource consumption on mobile device D1 (Galaxy Grand Prime)
Fig. S5 Resource consumption on mobile device D2 (Google Pixel 2)
Fig. S6 Resource consumption on mobile device D3 (Google Pixel 3)
Compliance with ethics guidelines
Khalid ALSUBHI, Zuhaib IMTIAZ, Ayesha RAANA, M. Usman ASHRAF, and Babur HAYAT declare that they have no conflict of interest.
Project supported by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia (No. D-154-611-1440)
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Alsubhi, K., Imtiaz, Z., Raana, A. et al. MEACC: an energy-efficient framework for smart devices using cloud computing systems. Front Inform Technol Electron Eng 21, 917–930 (2020). https://doi.org/10.1631/FITEE.1900198
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DOI: https://doi.org/10.1631/FITEE.1900198