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Cluster Energy optimization in Cloud Computing Disclosure Number: IPCOM000238962D
Publication Date: 2014-Sep-29
Document File: 8 page(s) / 121K

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The Prior Art Database


In fact, Gartner projected global revenue for cloud computing to reach almost $150 billion by 2014. However, The 2011 market is already approx $68 billion globally. With increase in web technologies and Internet, a proportional increase in Cloud computing technologies has been cited. Cloud computing has been emerging as a flexible and powerful computational architecture to offer ubiquitous services to users. A variety of hardware and software resources are integrated together as a resource pool, the software is no longer resided in a single hardware environment, it is performed upon the schedule of the resource pool for optimized resource utilization. The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. The need of different resources in cloud environment is unpredictable. It is observed that load management in cloud is utmost needed in order to provide QOS. The jobs at over-loaded physical machine are shifted to under-loaded physical machine and turning the idle machine off in order to provide green cloud. For energy optimization, DVFS and Power-Nap are good strategies. As much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. In this paper, we have proposed an algorithm for energy optimization having the constraint QOS and SLA.

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Page 01 of 8

Cluster Energy optimization in Cloud Computing

As we know, Cloud Computing is the best computing service model these days but lot of its energy is wasted in idle systems, typical deployments, even when the server utilization is below 30%. But idle servers still consume 60% of their peak power and they run traditional algorithms for load balancing and searching the best idle node having under-loaded nodes.

We have proposed hybrid approach for energy optimization(Green Computing) using Genetic algorithms like:

(1). Ant Colony optimization: for finding shortest paths between requester and computing nodes

(2). Bee Colony optimization: for load balancing between overloaded and under-loaded nodes,

Energy Efficient Modes and Techniques:

3. PowerNap Mode: Less Power Consumption Mode.

4. DVFS(Dynamic Voltage Frequency Scaling: Server speed-up and down as per incoming requests in the queue
5. RAILS: Replacement of simple power supply unit which have the constraint QoS(Quality of Service).

Existing Solution for Cluster Energy Optimization:
1. For Traversing between the nodes, traditional algorithms like DFS, BFS etc are used.

2. For load balancing, static algorithms are used, which have very high response time.

3. For power saving, we use sleep mode which degrades the quality of service thereby violating SLA.

4. Existing servers have same voltage consumption, it does not change as per number of incoming requests.

5. Simple PSU's are used for power supply.

Drawbacks in Known Solution
1. All of the traditional searching algorithm have a long execution time
2. Existing Load balancing algorithm are static and have long response time.

3. In existing data center, there are three modes: Active, Sleep and Inactive. For resuming the active mode from inactive mode, it takes too much time so it causes delay in response.

4. Static voltage frequency scaling does not happen automatically as per number of requests.
5. Simple PSU have poor power distribution model.

Additional Solution Requirement:

In my solution, I am providing hybrid approach to solve problems like

Load balancing
how to find shortest path
Energy efficient mode
Controlling speed of server
Power Supply model.


Page 02 of 8


Link for my research papers:

My aim is to enable Green Computing with respect to Cloud Computing. I have given two Genetic algorithms for Load balancing(BCO) and traversing all the nodes(ACO). For energy optimization, we want to use PowerNap and DVFS mode. RAILS is used for power distribution .


In the proposed approach, we have used following existing algorithms for different purposes.

A. Bee Colony optimization for Load-balancing.

B. Ant Colony optimization for Traversing all nodes.

C. PowerNap and DVFS for energy optimization.

D. RAILS for distribution of power.


Ant Colony optimization technique was proposed by Marco Dorigo in the early of 90's. In our