Energy-aware workflow job scheduling for green clouds

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

With the increasing deployment of many data centers and computer servers around the globe, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have increased dramatically. In order to maintain sustainable Cloud computing with ever-increasing problem scale, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission without sacrificing Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). The underlying available computing capacity and network bandwidth is represented as time-dependent because of the dual operation modes of on-demand and reservation instances supported by many commercial Cloud data centers. The Dynamic Voltage and Frequency Scaling (DVFS) is utilized to lower the CPU frequencies of virtual machines as long as the finishing time is still before the specified deadline. Our resource provision and allocation algorithm aims to meet the response time requirement and minimize the Virtual Machine (VM) overhead for reduced energy consumption. The consolidated VM reuse can lead to higher resource utilization rate for higher system throughput. The effectiveness of our algorithm is evaluated under various performance metrics and experimental scenarios using software adapted from open source CloudSim simulator. The simulation results show that our algorithm is able to achieve an average up to 30% of energy savings.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Pages232-239
Number of pages8
DOIs
StatePublished - 1 Dec 2013
Event2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 - Beijing, China
Duration: 20 Aug 201323 Aug 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

Other

Other2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
CountryChina
CityBeijing
Period20/08/1323/08/13

Fingerprint

Scheduling
Energy utilization
Response time (computer systems)
Cloud computing
Scheduling algorithms
Program processors
Energy conservation
Quality of service
Servers
Simulators
Throughput
Cooling
Bandwidth
Communication
Virtual machine
Costs
Voltage scaling
Dynamic frequency scaling

Cite this

Cao, F., & Zhu, M. M. (2013). Energy-aware workflow job scheduling for green clouds. In Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 (pp. 232-239). [6682072] (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.58
Cao, Fei ; Zhu, Michelle M. / Energy-aware workflow job scheduling for green clouds. Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. pp. 232-239 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013).
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Cao, F & Zhu, MM 2013, Energy-aware workflow job scheduling for green clouds. in Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013., 6682072, Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, pp. 232-239, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, Beijing, China, 20/08/13. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.58

Energy-aware workflow job scheduling for green clouds. / Cao, Fei; Zhu, Michelle M.

Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. p. 232-239 6682072 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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AU - Cao, Fei

AU - Zhu, Michelle M.

PY - 2013/12/1

Y1 - 2013/12/1

N2 - With the increasing deployment of many data centers and computer servers around the globe, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have increased dramatically. In order to maintain sustainable Cloud computing with ever-increasing problem scale, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission without sacrificing Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). The underlying available computing capacity and network bandwidth is represented as time-dependent because of the dual operation modes of on-demand and reservation instances supported by many commercial Cloud data centers. The Dynamic Voltage and Frequency Scaling (DVFS) is utilized to lower the CPU frequencies of virtual machines as long as the finishing time is still before the specified deadline. Our resource provision and allocation algorithm aims to meet the response time requirement and minimize the Virtual Machine (VM) overhead for reduced energy consumption. The consolidated VM reuse can lead to higher resource utilization rate for higher system throughput. The effectiveness of our algorithm is evaluated under various performance metrics and experimental scenarios using software adapted from open source CloudSim simulator. The simulation results show that our algorithm is able to achieve an average up to 30% of energy savings.

AB - With the increasing deployment of many data centers and computer servers around the globe, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have increased dramatically. In order to maintain sustainable Cloud computing with ever-increasing problem scale, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission without sacrificing Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). The underlying available computing capacity and network bandwidth is represented as time-dependent because of the dual operation modes of on-demand and reservation instances supported by many commercial Cloud data centers. The Dynamic Voltage and Frequency Scaling (DVFS) is utilized to lower the CPU frequencies of virtual machines as long as the finishing time is still before the specified deadline. Our resource provision and allocation algorithm aims to meet the response time requirement and minimize the Virtual Machine (VM) overhead for reduced energy consumption. The consolidated VM reuse can lead to higher resource utilization rate for higher system throughput. The effectiveness of our algorithm is evaluated under various performance metrics and experimental scenarios using software adapted from open source CloudSim simulator. The simulation results show that our algorithm is able to achieve an average up to 30% of energy savings.

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U2 - 10.1109/GreenCom-iThings-CPSCom.2013.58

DO - 10.1109/GreenCom-iThings-CPSCom.2013.58

M3 - Conference contribution

AN - SCOPUS:84893433648

SN - 9780769550466

T3 - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

SP - 232

EP - 239

BT - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

ER -

Cao F, Zhu MM. Energy-aware workflow job scheduling for green clouds. In Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. p. 232-239. 6682072. (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.58