Energy-constrained Dynamic Scheduling and Dynamic Pricing Algorithm in Wireless Cloud Computing

PAN Su; Lü Pupu; CHEN Yuqing

doi:10.11999/JEIT160590

Volume 39 Issue 5

May 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(5): 1150-1156

PAN Su, Lü Pupu, CHEN Yuqing. Energy-constrained Dynamic Scheduling and Dynamic Pricing Algorithm in Wireless Cloud Computing[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1150-1156. doi: 10.11999/JEIT160590

Citation:

PAN Su, Lü Pupu, CHEN Yuqing. Energy-constrained Dynamic Scheduling and Dynamic Pricing Algorithm in Wireless Cloud Computing[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1150-1156. doi: 10.11999/JEIT160590

Citation:

PDF( 242 KB)

Energy-constrained Dynamic Scheduling and Dynamic Pricing Algorithm in Wireless Cloud Computing

doi: 10.11999/JEIT160590

Funds:

The National Natural Science Foundation of China (61271235), The Priority Academic Program Development of Jiangsu Higher Education Institutions

Received Date: 2016-06-03
Rev Recd Date: 2017-03-24
Publish Date: 2017-05-19

Abstract

Abstract

A novel energy-constrained joint dynamic scheduling and pricing algorithm in wireless cloud computing system is proposed. A Lyapunov function of energy constraints and traffic restrictions is constructed. The long-term profit optimization problem?with multiple?constraints is turned into minimizing the upper bound of Lyapunov offset and weighted penalty function. The algorithm ensures the limited energy requirements of cloud service providers as well as the traffic demands of cloud users, furthermore, it optimizes the long-term profit of cloud service providers.
- Wireless cloud computing,
- Energy-constrained,
- Dynamic scheduling,
- Lyapunov optimization

FullText(HTML)

References(17)

References

KALAGIAKOS P and KARAMPELAS P. Cloud computing learning[C]. International Conference on Application of information and Communication Technologies (AICT), Baku, 2011: 1-4. doi: 10.1109/ICAICT.2011.6110925.

BORU D, KLIAZOVICH D, GRANELLI F, et al. Models for efficient data replication in cloud computing datacenters[C]. IEEE International Conference on Communications, London, 2015: 6056-6061. doi: 10.1109/ICC.2015.7249287.

REN C, WANG D, URGAONKAR B, et al. Carbon- awareenergy capacity planning for datacenters[C]. The 20th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Washington, D.C., 2012: 391-400.

GOYAL Y, ARYA M S, and NAGPAL S. Energy efficient hybrid policy in green cloud computing[C]. International Conference on Green Computing and Internet of Things, Noida, 2015: 1065-1069. doi: 10.1109/ICGCIoT.2015. 7380621.

YOU C, HUANG K, and CHAE H. Energy efficient mobile cloud computing powered by wireless energy transfer[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(5): 1757-1771. doi: 10.1109/JSAC.2016.2545382.

EROL-KANTARCI M and MOUFTAH H T. Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues[J]. IEEE Communications Surveys Tutorials, 2015, 17(1): 179-197. doi: 10.1109/COMST.2014.2341600.

ISA N B M, WEI T C, and YATIM A H M. Smart grid technology: Communications, power electronics and control system[C]. International Conference on Sustainable Energy Engineering and Application, Bandung, 2015: 10-14. doi: 10.1109/ICSEEA.2015.7380737.

GUENTER B, JAIN N, and WILLIAMS C. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning[C]. IEEE International Conference on Computer Communications, Shanghai, 2011: 1332-1340. doi: 10.1109/ INFCOM.2011.5934917.

REN S, HE Y, and XU F. Provably-efficient job scheduling for energy and fairness in geographically distributed data centers[C]. The 32nd IEEE International Conference on Distributed Computing Systems (ICDCS), Macau, China, 2012: 22-31. doi: 10.1109/ICDCS.2012.77.

LIN M, WIERMAN A, ANDREW L L H, et al. Dynamic right-sizing for power-proportional data centers[J]. IEEE/ ACM Transactions on Networking, 2013, 21(5): 1378-1391.

JOE-WONG C, SEN S, HA S, et al. Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility[J]. IEEE Journal on Selected Areas in Communications, 2012, 30(6): 1075-1085.

REN S and VAN M. Dynamic scheduling and pricing in wireless cloud computing[J]. IEEE Transactions on Mobile Computing, 2014, 13(10): 2283-2292. doi: 10.1109/TMC. 2013.57.

POLVERINI M, REN S, and CIANFRANI A. Capacity provisioning and pricing for cloud computing with energy capping[C]. The 51st Annual Allerton Conference on Communication, Control, and Computing, Monticello, 2013: 413-420. doi: 10.1109/Allerton.2013.6736554.

NEELY M J. Stochastic Network Optimization with Application to Communication and Queuing Systems[M]. Berkeley: Morgan Claypool, 2010: 211.

ZHANG Y, WANG Y, and WANG X. Electricity bill capping for cloud-scale datacenters that impact the power markets[C]. The 41st International Conference on Parallel Processing, Pittsburgh, PA, 2012: 440-449. doi: 10.1109/ICPP.2012.23.

HANDE P, CHIANG M, CALDERBANK R, et al. Network pricing and rate allocation with content provider participation[C]. IEEE International Conference on Computer Communications (INFOCOM), Rio de Janeiro, 2011: 990-998.

NEELY M J. Universal scheduling for networks with arbitrary traffic, channels, and mobility[C]. The 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, 2010: 1822-1829. doi: 10.1109/CDC.2010.5717885.

Relative Articles

Supplements(0)

Cited By

Proportional views