Computer Science ›› 2019, Vol. 46 ›› Issue (12): 95-100.doi: 10.11896/jsjkx.190400106

Special Issue: Network and communication

• Network & Communication • Previous Articles     Next Articles

Node Resource Scheduling for Future Network Experimentation Facility

WANG Chen-xin, YANG Jia-hai, ZHUANG Yi, LUO Nian-long   

  1. (Institute for Network Sciences and Cyberspace,Tsinghua University,Beijing 100084,China)
  • Received:2019-04-18 Online:2019-12-15 Published:2019-12-17

Abstract: With the expansion of the Internet industry,research and innovation of network core technologies are urgent.The Future Network Experimentation Facility (FNEF) was designed to provide efficient and convenient resources for network-related researchers to support innovative research and experiments on network technologies.The resources scheduling management system is a very important task in FNEF.Based on the resource scheduling and test service requirement of FNEF,this paper designed a combination of centralized and distributed architecture.The central resource scheduling management system cooperates with the node resource scheduling management system to schedule the backbone network resources and computing resources in each disperse site.This paper proposed a multi-objective optimized resource scheduling algorithm by considering the communication cost between the virtual machines,the physical machine resource utilization and the resource balance.Simulation experiments show that the proposed algorithm can effectively optimize the above multiple objectives.

Key words: Multi-node hierarchical scheduling architecture, Multi-objective optimization, Virtual machine resource scheduling

CLC Number: 

  • TP302
[1]Wikipedia contributors.Global Environment for Network Innovations[G/OL].(2019-02-19) [2019-03-01].
[3]JIANG H,XIAO Y L.Research on Unified Resource Management and Scheduling System in Cloud Environment[J].Wireless Personal Communications,2018,102(2):963-973.
[4]SINGH S,CHANA I.A Survey on Resource Scheduling in Cloud Computing:Issues and Challenges [J].Journal of Grid Computing,2016,14(2):217-264.
[5]ESWARAPRASAD R,RAJA L.A Review of Virtual Machine (VM) Resource Scheduling Algorithms in Cloud Computing Environment[J].Journal of Statistics and Management Systems,2017,20(4):703-711.
[6]NOSRATI M,CHALECHALE A,KARIMI R.Latency Optimization for Resource Allocation in Cloud Computing System [C]//Computational Science and Its Applications(ICCSA 2015).Springer International Publishing,2015:355-366.
[7]MASDARI M,NABAVI S S,AHMADI V.An Overview of Virtual Machine Placement Schemes in Cloud Computing [J].Journal of Network and Computer Applications,2016,66(C):106-127.
[8]ALICHERRY M,LAKSHMAN T V.Network Aware Resource Allocation in Distributed Clouds[C]//2012 Proceedings IEEE INFOCOM.Orlando,Florida,USA:IEEE,2012:963-971.
[9]SHETTY S,YUCHI X B,SONG M.Optimizing Network-aware Resource Allocation in Cloud Data Centers [M].Moving Target Defense for Distributed Systems.Springer International Publishing,2016:43-55.
[10]CANALI C,LANCELLOTTI R,SHOJAFAR M.A Computa- tion-and Network-aware Energy Optimization Model for Virtual Machines Allocation[C]//Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1:CLOSER.Porto,Portugal:SCITEPRESS,2017:71-81.
[11]COHEN R,LEWIN_EYTAN L,NAOR J,et al.Almost Optimal Virtual Machine Placement for Traffic Intense Data Centers[C]//2013 Proceedings IEEE INFOCOM.Turin,Italy:IEEE,2013:355-359.
[12]TIAN W H,XU M X,CHEN Y,et al.Prepartition:A New Paradigm for the Load Balance of Virtual Machine Reservations in Data Centers[C]//2014 IEEE International Conference on Communications.Sydney,NSW,Australia:IEEE,2014:4017-4022.
[13]NAGPURE M B,DAHIWALE P,MARBATE P.An Efficient Dynamic Resource Allocation Strategy for VM Environment in Cloud[C]//2015 International Conference on Pervasive Computing.St.Louis,Missouri,USA:IEEE,2015.
[14]ZHANG M H,REN H L,XIA C H.A Dynamic Placement Policy of Virtual Machine Based on MOGA in Cloud Environment[C]//2017 IEEE International Symposium on Parallel and Distributed Processing with Applications.Guangzhou,China:IEEE,2017:885-891.
[15]FAN Z Q,SHEN H,WU Y B,et al.Simulated-annealing Load Balancing for Resource Allocation in Cloud Environments[C]//International Conference on Parallel & Distributed Computing.Taipei,Taiwan:IEEE,2014:1-6.
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