TY - GEN
T1 - Virtual machine consolidation for cloud data centers using parameter-based adaptive allocation
AU - Mosa, Abdelkhalik
AU - Sakellariou, Rizos
N1 - Publisher Copyright: © 2017 ACM.
PY - 2017/8/31
Y1 - 2017/8/31
N2 - Cloud computing enables cloud providers to offer computing infrastructure as a service (IaaS) in the form of virtual machines (VMs). Cloud management platforms automate the allocation of VMs to physical machines (PMs). An adaptive VM allocation policy is required to handle changes in the cloud environment and utilize the PMs efficiently. In the literature, adaptive VM allocation is typically performed using either reservation-based or demand-based allocation. In this work, we have developed a parameter-based VM consolidation solution that aims to mitigate the issues with the reservation-based and demand-based solutions. This parameter- based VM consolidation exploits the range between demand-based and reservation-based finding VM to PM allocations that strike a delicate balance according to cloud providers' goals. Experiments conducted using CloudSim show how the proposed parameter- based solution gives a cloud provider the flexibility to manage the trade-off between utilization and other requirements.
AB - Cloud computing enables cloud providers to offer computing infrastructure as a service (IaaS) in the form of virtual machines (VMs). Cloud management platforms automate the allocation of VMs to physical machines (PMs). An adaptive VM allocation policy is required to handle changes in the cloud environment and utilize the PMs efficiently. In the literature, adaptive VM allocation is typically performed using either reservation-based or demand-based allocation. In this work, we have developed a parameter-based VM consolidation solution that aims to mitigate the issues with the reservation-based and demand-based solutions. This parameter- based VM consolidation exploits the range between demand-based and reservation-based finding VM to PM allocations that strike a delicate balance according to cloud providers' goals. Experiments conducted using CloudSim show how the proposed parameter- based solution gives a cloud provider the flexibility to manage the trade-off between utilization and other requirements.
KW - cloud data centers
KW - efficient data center utilization
KW - virtual machine consolidation
KW - virtual machine mapping
KW - parameter-based adaptive allocation
KW - virtual machines (VMs)
KW - CloudSim
KW - data centers
UR - http://www.scopus.com/inward/record.url?scp=85030316338&partnerID=8YFLogxK
U2 - 10.1145/3123779.3123807
DO - 10.1145/3123779.3123807
M3 - Conference contribution book
AN - SCOPUS:85030316338
T3 - ACM International Conference Proceeding Series
SP - 1
EP - 10
BT - Proceedings - 5th European Conference on the Engineering of Computer-Based Systems, ECBS 2017
A2 - Rysavy, Ondrej
A2 - Vranic, Valentino
T2 - 5th European Conference on the Engineering of Computer-Based Systems, ECBS 2017
Y2 - 31 August 2017 through 1 September 2017
ER -