Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds
Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increa...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/208983 |
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| _version_ | 1850234221576060928 |
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| author | Supriya Kinger Rajesh Kumar Anju Sharma |
| author_facet | Supriya Kinger Rajesh Kumar Anju Sharma |
| author_sort | Supriya Kinger |
| collection | DOAJ |
| description | Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. |
| format | Article |
| id | doaj-art-15bc183aa857406a86b701b7eccd7909 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-15bc183aa857406a86b701b7eccd79092025-08-20T02:02:41ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/208983208983Prediction Based Proactive Thermal Virtual Machine Scheduling in Green CloudsSupriya Kinger0Rajesh Kumar1Anju Sharma2Department of Computer Science and Engineering, SGGS World University, Fatehgarh Sahib, Punjab, IndiaSchool of Mathematics and Computer Applications, Thapar University, Patiala, IndiaSchool of Mathematics and Computer Applications, Thapar University, Patiala, IndiaCloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.http://dx.doi.org/10.1155/2014/208983 |
| spellingShingle | Supriya Kinger Rajesh Kumar Anju Sharma Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds The Scientific World Journal |
| title | Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds |
| title_full | Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds |
| title_fullStr | Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds |
| title_full_unstemmed | Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds |
| title_short | Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds |
| title_sort | prediction based proactive thermal virtual machine scheduling in green clouds |
| url | http://dx.doi.org/10.1155/2014/208983 |
| work_keys_str_mv | AT supriyakinger predictionbasedproactivethermalvirtualmachineschedulingingreenclouds AT rajeshkumar predictionbasedproactivethermalvirtualmachineschedulingingreenclouds AT anjusharma predictionbasedproactivethermalvirtualmachineschedulingingreenclouds |