محاسبات لبه تلفن همراه برای تشریک و ذخیره سازی منابع
ترجمه نشده

محاسبات لبه تلفن همراه برای تشریک و ذخیره سازی منابع

عنوان فارسی مقاله: توانمندسازی چندعامله شناختی محاسبات لبه تلفن همراه برای تشریک و ذخیره سازی منابع
عنوان انگلیسی مقاله: Cognitive multi-agent empowering mobile edge computing for resource caching and collaboration
مجله/کنفرانس: نسل آینده سیستم های رایانه ای - Future Generation Computer Systems
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: محاسبات ابری، مهندسی نرم افزار، امنیت اطلاعات، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: عامل شناختی، محاسبات لبه موبایل، استراتژی ذخیره سازی، تشریک منابع
کلمات کلیدی انگلیسی: Cognitive agent، Mobile edge computing، Caching strategy، Resource collaboration
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.future.2019.08.001
دانشگاه: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
صفحات مقاله انگلیسی: 9
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 7/007 در سال 2019
شاخص H_index: 93 در سال 2020
شاخص SJR: 0/835 در سال 2019
شناسه ISSN: 0167-739X
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14314
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Related work

3- Proposed the cognitive agent paradigm and design issues

4- Problem formulation and solution

5- Performance analysis

6- Conclusion

References

بخشی از مقاله (انگلیسی)

Abstract

The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users’ behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users’ equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users.

Introduction

At present, emerging information services and applications increase rapidly with the development of wireless communication and Internet of Things (IoT) [1,2]. More and more intelligent devices, such as wearable devices, remote medical system and virtual/augmented reality (VR/AR), need advanced communication technology and computing ability to guarantee ultra-reliable low-latency communication (URLLC), which is a great challenges for network suppliers [3,4]. Besides, wireless access device in large quantity outputs massive quantity of data all the time [5]. The reasonable use of valuable information is greatly significant for improving the quality of services supplied to users [6]. Mobile edge computing (MEC) supplies infrastructures of communication, computing and caching at the network edge that is close to data source or users [7]. It offers cloud services and information technology environment for edge applications and supplies high-quality communication services to users [8–13]. The advantages of the emerging service model MEC can be summarized below: (i). Due to be close to users or data source, some problems can be solved, such as long delay and heavy data traffic. It well supports the real-time and bandwidth-dense Internet of Things facilities, especially for the emotion-aware multimedia systems [14–16]. Taking unmanned driving for example, automobile needs to calculate running speed and the distance from others and sense surroundings in real time.