20,129 research outputs found
EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud
Cloud computing has become more popular in provision of computing resources
under virtual machine (VM) abstraction for high performance computing (HPC)
users to run their applications. A HPC cloud is such cloud computing
environment. One of challenges of energy efficient resource allocation for VMs
in HPC cloud is tradeoff between minimizing total energy consumption of
physical machines (PMs) and satisfying Quality of Service (e.g. performance).
On one hand, cloud providers want to maximize their profit by reducing the
power cost (e.g. using the smallest number of running PMs). On the other hand,
cloud customers (users) want highest performance for their applications. In
this paper, we focus on the scenario that scheduler does not know global
information about user jobs and user applications in the future. Users will
request shortterm resources at fixed start times and non interrupted durations.
We then propose a new allocation heuristic (named Energy-aware and Performance
per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to
choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS
per Watt). Using information from Feitelson's Parallel Workload Archive to
model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on
heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF
can reduce significant total energy consumption in comparison with state of the
art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced
Computing and Applications, Journal of Science and Technology, Vietnamese
Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201
Energy-Aware Lease Scheduling in Virtualized Data Centers
Energy efficiency has become an important measurement of scheduling
algorithms in virtualized data centers. One of the challenges of
energy-efficient scheduling algorithms, however, is the trade-off between
minimizing energy consumption and satisfying quality of service (e.g.
performance, resource availability on time for reservation requests). We
consider resource needs in the context of virtualized data centers of a private
cloud system, which provides resource leases in terms of virtual machines (VMs)
for user applications. In this paper, we propose heuristics for scheduling VMs
that address the above challenge. On performance evaluation, simulated results
have shown a significant reduction on total energy consumption of our proposed
algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling
algorithm with the same fulfillment of performance requirements. We also
discuss the improvement of energy saving when additionally using migration
policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International
Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi,
Vietna
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