تخلیه بار تحرکآگاه با قابلیت تحملپذیری خطا در رایانش ابری موبایل
محورهای موضوعی : مهندسی برق و کامپیوترراضیه روستایی 1 , زينب موحدي 2 *
1 - دانشگاه علم و صنعت ایران
2 - دانشگاه علم و صنعت ايران
کلید واژه: رایانش ابری موبایل تخلیه بار تحرک قابلیت تحملپذیری خطا,
چکیده مقاله :
امروزه با توجه به توسعه شبکهها و تکنولوژیهای ارتباطی، اینترنت اشیا به عنوان بخش جداییناپذیر از فناوری اطلاعات مطرح شده است. توسعه این فناوری با توجه به محدودیت دستگاههای متحرک از نظر توان محاسباتی، ظرفیت باتری و حافظه با چالشهایی روبهرو میباشد. در راستای حل این چالشها، رایانش ابری موبایل که با به خدمت گرفتن فضای ذخیرهسازی و قدرت محاسباتی ابر، ظرفیت موبايل را برای انجام برنامههای کاربردی بهبود میبخشد، مطرح شده است. به این منظور، برخی از مؤلفههای برنامه کاربردی با هدف بهینهسازی زمان اجرا و انرژی مصرفی کل، برای اجرا به ابر تخلیه میشوند. با توجه به تأثیر تحرک دستگاه متحرک بر شرایط شبکه دسترسی و کیفیت اتصال، تصمیمگیری مؤلفههای مناسب جهت تخلیه به ابر باید با توجه به تحرک دستگاه انجام پذیرد. تا کنون روشهای محدودی در زمینه تخلیه بار تحرکآگاه ارائه شده است. این روشها از مشکلاتی از جمله عدم استفاده از مدل تحرک مناسب، عدم لحاظ قابلیت تحملپذیری خطا، تخلیه یکجای برنامه و عدم توجه به تخلیهبار ریزدانه رنج میبرند. در این مقاله به منظور رفع این مشکلات، یک روش تصمیمگیری تخلیه بار تحرکآگاه با استفاده از زنجیره مارکوف تحرک کاربر و قابلیت تحملپذیری خطا ارائه شده است. نتایج ارزیابیها نشان میدهد که روش پیشنهادی نسبت به روش اخیر مطرح در این زمینه تا 75% در زمان اجرا و 65% در انرژی مصرفی جهت اجرای برنامه کاربردی بهبود ایجاد میکند
Nowadays, Internet of Things (IoT) has emerged as an important field in information and communication technologies. Despite the progress of networks and communication technologies, the development of IoT has encountered some challenges mainly with regard to computation power, battery lifetime and memory of mobile devices. In order to overcome these challenges, mobile cloud computing has been raised which uses the cloud storage space and computation power to extend the capabilities of mobile devices. In this regard, some of application’s components are selected to be offloaded to the cloud in order to optimize the execution time and energy consumption of application. Since the mobility has an important effect on the acquired condition of the access network and the quality of the connection, the mobility should be considered while selecting components for offloading. Although a number of mobility-aware offloading approaches has been already proposed, these works suffer from the lack of an appropriate mobility-model, ignorance of the fault-tolerance capability and use of only coarse-grain offloading. In order to address these issues, we propose a mobility-aware offloading scheme which uses the user mobility Markov chain and the fault tolerance capability in order to optimize the offloading decision. Evaluation results show that our proposed method significantly outperforms the existing alternatives, reaching respectively up to 75 and 65 percent enhancement in terms of the execution time and the energy consumption.
[1] K. Ravindranath and K. Raja Sekhara Rao, "A survey on energy aware offloading techniques for mobile cloud computing," International J. of Computer Trends and Technology, vol. 4, no. 7, pp. 2081-2086, Jul. 2013.
[2] Z. Sanaei, S. Abolfazli, A. Gani, and M. Shiraz, "SAMI: service-based arbitrated multi-tier infrastructure for mobile cloud computing, SAMI: service-based arbitrated multi-tier infrastructure for mobile cloud computing," in Proc. IEEE 1st Int. Conf. on Communications in China Workshops, pp. 14-19, Beijing, China, 15-17 Aug. 2012.
[3] S. Abolfazli, Z. Sanaei, M. Alizadeh, A. Gani, and F. Xia, "MOMCC: market-oriented architecture for mobile cloud computing based on service oriented architecture," in Proc. IEEE 1st Int. Conf. on Communications in China Workshops, pp. 8-13, Bejing, China, 15-17 Aug. 2012.
[4] Z. Sanaei, S. Abolfazli, A. Gani, and R. K. Buyya, "Heterogeneity in mobile cloud computing: taxonomy and open challenges," IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 369-392, First Quarter 2014.
[5] S. Deng, L. Huang, J. Taheri, and A. Y. Zomaya, "Computation offloading for service workflow in mobile cloud computing," IEEE Trans. on Parallel and Distributed Systems, vol. 26, no. 12, pp. 3317-3329, Dec. 2015.
[6] H. Wu and D. Huang, "Modeling multi-factor multi-site risk-based offloading for mobile cloud computing," in Proc. 10th Int. Conf. on Network and Service Management, pp. 230-235, Rio de Janeiro, Brazil ,17-21 Nov. 2014.
[7] K. Mitra, S. Saguna, C. Ahlund, and D. Granlund, "M2C2: a mobility management system for mobile cloud computing," in Proc. Wireless Communications and Networking Conf., pp. 1608-1613, New Orleans, LA, USA, 9-12 Mar 2015.
[8] J. Kim, Y. Morioka, and J. Hagiwara, "An optimized seamless ip flow mobility management architecture for traffic offloading," in Proc. Network Operations and Management Symp., pp. 229-236, Maui, HI, USA, 16-20 Apr. 2012.
[9] M. R. Rahimi, N. Venkatasubramanian, and A. V. Vasilakos, "Music: mobility-aware optimal service allocation in mobile cloud computing," in Proc. IEEE 6th Int. Conf. on Cloud Computing, pp. 75-82, Santa Clara, CA, USA, 28 Jun.-3 Jul. 2013.
[10] A. Gani, et al., "A review on interworking and mobility techniques for seamless connectivity in mobile cloud computing," J. of Network and Computer Applications, vol. 43, pp. 84-102, Aug. 2014.
[11] K. Lee and I. Shin, "User mobility model based computation offloading decision for mobile cloud," J. of Computing Science and Engineering, vol. 9, no. 3, pp. 155-162, 2015.
[12] C. Shi, M. H. Ammar, E. W. Zegura, and M. Naik, "Computing in cirrus clouds: the challenge of intermittent connectivity," in Proc. of the 1st Edition of the MCC Workshop on Mobile Cloud Computing, pp. 23-28, Helsinki, Finland, 17-17 Aug. 2012.
[13] A. R. Khan, M. Othman, S. A. Madani, and S. U. Khan, "A survey of mobile cloud computing application models," IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 393-413, First Quarter 2013.
[14] K. Kumar, J. Liu, Y. H. Lu, and B. Bhargava, "A survey of computation offloading for mobile systems," Mobile Networks and Applications, vol. 18, no. 1, pp. 129-140, 2013.
[15] A. C. Olteanu and N. Tapus, "Offloading for mobile devices: a survey," UPB Scientific Bulletin, vol. 76, no. 1, pp. 3-16, Feb. 2014.
[16] S. Abolfazli, Z. Sanaei, M. H. Sanaei, and A. Gani, "Mobile cloud computing: the-state-of-the-art, challenges and future research," in Encyclopedia of Cloud Computing, Wiley & Sons, pp. 1-16, 2015.
[17] W. Junior, A. Franca, K. Dias, and J. N. Souza, "Supporting mobility-aware computational offloading in mobile cloud environment," J. of Network and Computer Applications, vol. 94, pp. 93-108, 15 Sept. 2017.
[18] A. ur Rehman Khan, M. Othman, and F. Xia, "Context-aware mobile cloud computing and its challenges," IEEE Cloud Computing, vol. 2, no. 3, pp. 42-49, May-Jun. 2015.
[19] B. Zhou and R. Buyya, "Augmentation techniques for mobile cloud computing: a taxonomy, survey, and future directions," ACM Computing Surveys, vol. 51, no. 1, Article 13, Feb. 2018.