TY - GEN
T1 - Online Virtual Machine Allocation with Lifetime and Load Predictions
AU - Buchbinder, Niv
AU - Fairstein, Yaron
AU - Mellou, Konstantina
AU - Menache, Ishai
AU - Naor, Joseph Seffi
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/5/31
Y1 - 2021/5/31
N2 - The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information about future (total) demand. We show that the competitive factor can be dramatically improved with this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.
AB - The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information about future (total) demand. We show that the competitive factor can be dramatically improved with this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.
KW - cloud computing
KW - dynamic bin packing
KW - virtual machine scheduling
UR - http://www.scopus.com/inward/record.url?scp=85107892976&partnerID=8YFLogxK
U2 - 10.1145/3410220.3456278
DO - 10.1145/3410220.3456278
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AN - SCOPUS:85107892976
T3 - SIGMETRICS 2021 - Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
SP - 9
EP - 10
BT - SIGMETRICS 2021 - Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2021
Y2 - 14 June 2021 through 18 June 2021
ER -