With
the increasing call for green cloud, reducing energy consumption has been an
important requirement for cloud resource providers not only to reduce operating
costs, but also to improve system reliability. Dynamic voltage scaling (DVS)
has been a key technique in exploiting the hardware characteristics of cloud
datacenters to save energy by lowering the supply voltage and operating
frequency. This paper presents a novel stochastic framework for energy
efficiency and performance analysis of DVS-enabled cloud. This framework uses
virtual machine request arrival rate, failure rate, repair rate, and service
rate of datacenter servers as model inputs. Based on a queuing network- based
analysis, this paper gives analytic solutions of three metrics. The proposed
framework can be used to help the design and optimization of energy-aware high
performance cloud systems.
AIM
The
main aim of this paper is presents a novel stochastic framework for energy
efficiency and performance analysis of DVS-enabled cloud. SCOPE
The
scope of this paper is a novel stochastic framework can be used to help the
design and optimization of energy-aware high performance cloud systems.
EXISTING SYSTEM
To
manage the applications in a cloud datacenter in an energy-efficient way
becomes an urgent problem. DVS technologies give rise to a flexible solution to
the above question. DVS tries to address the trade-off between performance and
energy efficiency by taking into account two important characteristics of
today’s computational systems: 1) the energy needed at the peak computing rate
is much higher than the average one and 2) most today’s processors are based on
CMOS logic. They suggest that high performance is needed only for a small
fraction of the time in general, while for the rest of the time, a
low-performance low-power pattern suffices. We can achieve the low performance
by simply lowering the operating frequency of processors since the full speed
mode is less energy-efficient. DVS goes beyond this and scales the operating
voltage of processors along with the frequency. This is supported by today’s
CMOS technology used by mainstream unicore/multicore processors.
DISADVANTAGES
- · Higher energy consumption
- · Reduce operating costs
PROPOSED
SYSTEM
In
this paper proposes a queuing-network based framework for energy efficiency and
performance evaluation of cloud datacenters with DVS capability. We consider
expected VM completion time, VM loss rate, and energy consumption rate as key
metrics of performance and energy efficiency. We employ a continuous-time
Markov model to obtain analytical solutions of these metrics. To validate the
effectiveness of their proposed model, we also conduct a case study on a sample
datacenter with low-energy machines built on Intel X Scale PXA270 processors.
ADVANTAGES
- DVS is used to quantify the effects of variations in workload, processor failure and recovery rates, the number of machines.
- DVS strategies and system capacity on performance and energy efficiency via the probabilistic analysis of continuous Markov chains.
System Architecture
System Configuration
Hardware Requirements
- Speed - 1.1 Ghz
- Processor - Pentium IV
- RAM - 512 MB (min)
- Hard Disk - 40 GB
- Key Board - Standard Windows Keyboard
- Mouse - Two or Three Button Mouse
- Monitor - LCD/LED
Software
requirements
- Operating System : Windows 7
- Front End : ASP.Net and C#
- Database : MSSQL
- Tool : Microsoft Visual studio
References
YunNi
Xia,MengChu Zhou,Xin Luo, ShanChen Pang “A Stochastic Approach to Analysis of
Energy-Aware DVS-Enabled Cloud Datacenters” IEEE Transactions on Systems, Man,
and Cybernetics: Systems Volume 45
Issue 1 August 2014.
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