G-SLAM:Optimizing Energy Efficiency
Presented By : Alfiya Mahmood
Integral University, Lucknow
WHAT IS CLOUD COMPUTING?
Cloud computing is a model for enabling convenient, on-demand
network access to a shared pool of configurable computing
resources like networks, servers, storage, applications, and
Amazon EC2, GoGrid, RightScale
Animoto, Sales Force, Google
Google AppEngine, MapReduce,
Aneka, Microsoft Azure
Dark Side of Cloud
•IT industry contributes 2% of world's total CO2 emissions
• 1.5% of total U.S. power consumption used by data centers
which has more than doubled since 2000 and costs $4.5 billion
• In India, 14% of ICT emission is caused by data centers
• Roughly50%of the emissiondue to data centers isdue to
powersystem lossesand cooling loads
Computer Rm.AC 34%
Compute resources and
particularly servers areat
the heart of a complex,
Where Does the Power Go?
Power Consumption in theDatacenter
Impact on the Environment
• Data centers are not only expensive
to maintain, but also unfriendly to
• High energy costs and huge carbon
footprints are incurred due to the
massive amount of electricity needed
to power and cool the numerous
servers hosted in these data centers.
Green Cloud: “performance”
As energy costs are increasing,
there is a need to shift focus from
optimising data center resource
management for pure performance
alone to optimising for energy
efficiency while maintaining high
service level performance.
Green Service Level Agreement
To fulfill the requirements of green computing, the main need is to maintain a green
SLA. It is an agreement same as the basic service level agreement only with the
difference of eco-efficient parameters. GSLA consists three Es of sustainability- Ethics,
Ecology and Economic.
In the proposed G-SLAM (Green Service Level Agreement-Minimization of Energy)
framework, energy consumed on different levels of cloud services can be optimized with the
help of software, hardware and network techniques. The classification of these approaches
help us to better understand the various issues in the field of green cloud computing.
Fig.4 Classification of Energy Optimization Techniques
• Software approach of energy reduction can be implemented by reducing the
number of active servers.
• By using Multiple Correlation Coefficient method, the selection of servers is
completed. The correlation coefficient is directly proportional to the risk of SLA
• Ant Colony Optimization is another approach for placement of virtual
machines. It maximizes the resource utilization by minimizing the number of
active servers. Energy reduction in homogenous data centers is achieved by
minimizing the number of active servers.
• The Power Aware Best Fit Decreasing algorithm allocates a new request for
VM which provides the least increase in power consumption caused by
allocation. VMs are sorted in decreasing order according to their current
processor utilization and then allocate it to the server that provides least increase
of power consumption.
• The optimization of consumed energy can be done by utilization of flexible
hardware by controlling frequencies and voltage in the server.
• Dynamic Voltage Frequency Scaling (DVFS) technique is applied with a
number of processors that can operate at different voltage and frequency levels.
• Dynamic Voltage Scaling (DVS) is also applied to save energy while testing the
ability of task to meet the deadlines. DVS provides a balance between the task
deadline and power consumption.
• A heuristics workflow algorithm namely Energy Aware Resource Efficient
Workflow Scheduling under Deadline constraint (EARES-D) utilizes DVFS
The communication of virtual machines consumes energy in data center. Reduction
of network traffic reduces the consumption of energy.The algorithm helps in
identification of under-loaded servers and heavily-loaded servers. After that it
identifies the physical servers with sufficient capacity and arranges them in
ascending order according to their load.
Data Center Energy Efficient Network Aware Scheduling (DENS) is used to reduce
energy consumption by optimization of tradeoff between task consolidation and
distribution of traffic pattern. It selects best fit server based on weighted
computational function to execute the job.
Disjoint Edge Node Divided Spanning Tree with traffic-aware Flow Migration
(DENDIST-FM) is applied. It generates various disjoint spanning trees to avoid
path overlapping and chooses the least utilized path for routing.
Energy and Topology aware VM Migration (ETA-VMM) detects and migrates
VMs from under or over utilized servers to the nearest machine. The nearest
machine is based on the nearest network distance.
In recent years, growing needs of cloud services led to uneconomical energy
consumption. Computing resources results in high operational cost as well as
huge amount of carbon di oxide emission to the environment. Therefore,
cloud services required to be eco-friendly. Researchers are continuously
struggling with optimizing energy aspects. This paper class classifies different
energy reduction techniques for making green cloud services. We introduced
cloud simulators focusing on CloudSim. Our future work consists of
implementation and evaluation of energy efficient framework by simulating it
on CloudSim toolkit.
1. NIST, 2011, The NIST Definition of Cloud Computing Recommendations of the
National Institute of Standards and Technology. NIST Special Publication, 145, p.7. Available at:
of paper if known,” unpublished
2. L. Wu, and R. Buyya, “Service Level Agreement (SLA) in Utility Computing Systems, ”
Performance and Dependability in Service Computing: Concepts, Techniques and Research
Directions, V. Cardellini et. al. (eds), ISBN: 978-1-60-960794-4, IGI Global, Hershey, PA, USA,
July 2011, pp.1-25.
3. G. S. Akula and A. Potluri, “Heuristics for migration with consolidation of ensembles of virtual
machines,” Proc. Communication Systems and Networks (COMSNETS), 2014 6th Int. Conf., pp.
1, 4, 6–10.
4. S. K. Garg and R. Buyya, “Green Cloud Computing and Environmental Sustainability” .
5. A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for
efficient management of data centers for Cloud computing,” Futur. Gener. Comput. Syst., vol. 28,
no. 5, pp. 755–768, 2012.
6. Z. Cao, “Energy-aware framework for virtual machine consolidation in Cloud computing,” Int.
Conf. High Perform. Comput. Commun., p. 429, 2013.
7. Habibullah, Khan Mohammad, "Developing strategies to mitigate the energy consumed by
network infrastructures." (2016).
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