Data offloading in mobile edge computing: A coalitional game based pricing approach

نویسندگان

  • Tian Zhang
  • Wei Chen
  • Feng Yang
چکیده

Mobile edge computing (MEC), affords service to the vicinity of mobile devices (MDs), has become a key technology for future network. Offloading big data to the MEC server for preprocessing is a attractive choice of MDs. In the paper, we investigate data offloading from MDs to MEC servers. A coalitional game based pricing scheme is proposed. We apply coalitional game to depict the offloading relationship between MDs and MEC servers, and utilize pricing as the stimuli for the offloading. A scheduled MD chooses one MEC server within the same coalition for offloading, and pays the selected MEC server for the MEC service. We formulate a coalitional game, where MDs and MEC servers are players and their utilities are respectively defined. Next, we analyze the formulated game. Specially, the core is studied. Finally, utility performance of the proposed scheme under the 2-MD and 2-MECserver scenario are demonstrated.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Price-Based Distributed Offloading for Mobile-Edge Computing with Computation Capacity Constraints

Mobile-edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to network edge. In this study, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the edge cloud and users, where th...

متن کامل

Mobile Offloading for Energy-efficient Computation on Smartphones

Mobile offloading enables mobile devices to distribute computation-intensive tasks to the cloud or other devices for energy conservation or performance gains. In principle, the idea is to trade the relatively low communication energy expense for high computation power consumption. In this thesis, we first focus on the technique of mobile code offloading to the cloud by proposing the new techniq...

متن کامل

Design and Evaluation of a Method for Partitioning and Offloading Web-based Applications in Mobile Systems with Bandwidth Constraints

Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoid...

متن کامل

Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing

We consider a heterogeneous network with mobile edge computing, where a user can offload its computation to one among multiple servers. In particular, we minimize the system-wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-u...

متن کامل

Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs

Recently, Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today’s interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1709.04148  شماره 

صفحات  -

تاریخ انتشار 2017