3,729 research outputs found
PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers
Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%
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
A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud
Energy efficiency has become an important measurement of scheduling algorithm
for private cloud. The challenge is trade-off between minimizing of energy
consumption and satisfying Quality of Service (QoS) (e.g. performance or
resource availability on time for reservation request). We consider resource
needs in context of a private cloud system to provide resources for
applications in teaching and researching. In which users request computing
resources for laboratory classes at start times and non-interrupted duration in
some hours in prior. Many previous works are based on migrating techniques to
move online virtual machines (VMs) from low utilization hosts and turn these
hosts off to reduce energy consumption. However, the techniques for migration
of VMs could not use in our case. In this paper, a genetic algorithm for
power-aware in scheduling of resource allocation (GAPA) has been proposed to
solve the static virtual machine allocation problem (SVMAP). Due to limited
resources (i.e. memory) for executing simulation, we created a workload that
contains a sample of one-day timetable of lab hours in our university. We
evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list
of virtual machines in start time (i.e. earliest start time first) and using
best-fit decreasing (i.e. least increased power consumption) algorithm, for
solving the same SVMAP. As a result, the GAPA algorithm obtains total energy
consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page
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
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
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