skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Abstract

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths.more » Furthermore, our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [3];  [1]; ORCiD logo [4];  [5]
  1. Huazhong Univ. of Science and Technology, Wuhan (China)
  2. Northumbria Univ., Newcastle upon Tyne (United Kingdom)
  3. Argonne National Lab. (ANL), Lemont, IL (United States)
  4. Univ. of Essex, Colchester (United Kingdom)
  5. Oxford Brookes Univ., Oxford (United Kingdom)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Natural Science Foundation of China (NNSFC); Engineering and Physical Sciences Research Council (EPSRC); European Commission, Community Research and Development Information Service (CORDIS). Seventh Framework Programme (FP7)
OSTI Identifier:
1488541
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Parallel and Distributed Systems
Additional Journal Information:
Journal Volume: 29; Journal Issue: 11; Journal ID: ISSN 1045-9219
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
Cloud radio access network; Lyapunov optimization; Mobile edge computing; Power-performance tradeoff; Scheduling

Citation Formats

Wang, Xinhou, Wang, Kezhi, Wu, Song, Di, Sheng, Jin, Hai, Yang, Kun, and Ou, Shumao. Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network. United States: N. p., 2018. Web. doi:10.1109/TPDS.2018.2832124.
Wang, Xinhou, Wang, Kezhi, Wu, Song, Di, Sheng, Jin, Hai, Yang, Kun, & Ou, Shumao. Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network. United States. doi:10.1109/TPDS.2018.2832124.
Wang, Xinhou, Wang, Kezhi, Wu, Song, Di, Sheng, Jin, Hai, Yang, Kun, and Ou, Shumao. Tue . "Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network". United States. doi:10.1109/TPDS.2018.2832124. https://www.osti.gov/servlets/purl/1488541.
@article{osti_1488541,
title = {Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network},
author = {Wang, Xinhou and Wang, Kezhi and Wu, Song and Di, Sheng and Jin, Hai and Yang, Kun and Ou, Shumao},
abstractNote = {Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Furthermore, our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.},
doi = {10.1109/TPDS.2018.2832124},
journal = {IEEE Transactions on Parallel and Distributed Systems},
issn = {1045-9219},
number = 11,
volume = 29,
place = {United States},
year = {2018},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share: