مشخصات مقاله | |
ترجمه عنوان مقاله | یک طرح تخلیه با بهره وری انرژی برای تأخیر کم در رایانش لبه مشترک |
عنوان انگلیسی مقاله | An Energy-Efficient Off-Loading Scheme for Low Latency in Collaborative Edge Computing |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 9 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
4.641 در سال 2018 |
شاخص H_index | 56 در سال 2019 |
شاخص SJR | 0.609 در سال 2018 |
شناسه ISSN | 2169-3536 |
شاخص Quartile (چارک) | Q2 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | مهندسی الگوریتم و محاسبات |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410000, China |
کلمات کلیدی | تاخیر، انرژی، تخلیه، رایانش لبه ای |
کلمات کلیدی انگلیسی | Latency, energy, offloading, edge computing |
شناسه دیجیتال – doi |
http://doi.org/10.1109/ACCESS.2019.2946683 |
کد محصول | E13863 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
ABSTRACT
I. INTRODUCTION II. RELATED WORK III. SYSTEM MODEL IV. ALGORUTHM DESIGN V. EVALUATE THE PERFORMANCE VI. CONCLUSION AND FUTURE WORK REFERENCES |
بخشی از متن مقاله: |
ABSTRACT
Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals’ tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as ‘‘big task’’) may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption. INTRODUCTION Mobile terminal devices are connected through the internet to accomplish many different applications and services, such as smartphones, laptops, sensors, machines, and vehicles, etc[1]. To extract valuable information from the huge amount of users’ data, local computation with terminal devices are no longer provide demanding quality of services such as low latency and energy consumption[2], [3], especially for video image stream data processing[4]–[6]. In-vehicle networks, tasks with high latency sensitivity require lower processing time. Otherwise, message propagation among vehicles may fail [7]. Therefore, light-weighted servers are deployed on the edge around terminals to bring computation and storage resource from the centralized cloud (CC), which is called as Mobile Edge Computing (MEC) [8]. Tasks generated by terminals can be offloaded and processed on edge servers [9]–[10] instead of being transferred to CC with large delay, and tasks or applications can effectively meet the delay requirements [11]–[13]. As privacy and security become more important in our daily life [14]–[17], a low delay would be particularly important in privacy and security issues for mobile edge computing systems. |