TY - GEN
T1 - Predictive VM Consolidation for Latency Sensitive Tasks in Heterogeneous Cloud
AU - Kumar Swain, Chinmaya
AU - Routray, Preeti
AU - Kumar Mishra, Sambit
AU - Alwabel, Abdulelah
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Virtualization technology plays a crucial role for reducing the cost in a cloud environment. Efficient virtual machine (VM) packing method that focuses on compaction of hosts such that most of its resources are used when it serves the user requests. Here our aim is to reduce the power requirements of a cloud system by focusing on minimizing the number of hosts. We propose a predictive scheduling approach considering the deadline of a task request and make flexible decisions to allocate the tasks to hosts. Experimental results show that the proposed approach can save around 5 to 10% power consumption than the standard VM packing methods in most scenarios. Even when the total power consumption requirements remain the same as that of standard methods in some scenarios, the average number of hosts required in the cloud environment are reduced and thereby reducing the cost.
AB - Virtualization technology plays a crucial role for reducing the cost in a cloud environment. Efficient virtual machine (VM) packing method that focuses on compaction of hosts such that most of its resources are used when it serves the user requests. Here our aim is to reduce the power requirements of a cloud system by focusing on minimizing the number of hosts. We propose a predictive scheduling approach considering the deadline of a task request and make flexible decisions to allocate the tasks to hosts. Experimental results show that the proposed approach can save around 5 to 10% power consumption than the standard VM packing methods in most scenarios. Even when the total power consumption requirements remain the same as that of standard methods in some scenarios, the average number of hosts required in the cloud environment are reduced and thereby reducing the cost.
KW - Cloud computing
KW - Consolidation
KW - Heterogeneous
KW - Power consumption
KW - Virtual machine
UR - http://www.scopus.com/inward/record.url?scp=85164964111&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-1203-2_12
DO - 10.1007/978-981-99-1203-2_12
M3 - Conference contribution
AN - SCOPUS:85164964111
SN - 9789819912025
T3 - Lecture Notes in Networks and Systems
SP - 135
EP - 150
BT - Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2023
A2 - Chinara, Suchismita
A2 - Tripathy, Asis Kumar
A2 - Li, Kuan-Ching
A2 - Sahoo, Jyoti Prakash
A2 - Mishra, Alekha Kumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023
Y2 - 15 January 2023 through 16 January 2023
ER -