VM’s Consolidation Using Ant Colony System And Clustering Of PM’s For Energy Efficient Network

International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 2, Issue 9, September - 2015. ISSN 2348 – 4853, Impact Factor – 1.317

29 | © 2015, IJAFRC All Rights Reserved www.ijafrc.org

VM’s Consolidation Using Ant Colony System And Clustering

Of PM’s For Energy Efficient Network Mr. Manoj V. Waghmode, Mr. Dayal B. Tilekar, Mr. Shubham D. Sonawane, Mr. Amol K. Sonavane

Imperial College of Engineering and Research

Pune,Maharashtra India [email protected], [email protected]

A B S T R A C T

In networking there is a big problem of energy consumption. So to improve the energy efficiency

and to reduce the energy consumption we can use distributed system architecture for

performing dynamic VM consolidation. This will help in reducing the energy consumption in

Network by maintaining the desired Quality of Services (QoS). We are planning to improve the

given system model using clustering of PMs and assigning them one consolidation manager to

each cluster. This manager manages the respective cluster which helps to monitor the all tasks

which are performed in that cluster of PMs. Due to monitoring the tasks we will be able to

improve the energy efficiency in a network.

Index Terms : Dynamic VM consolidation, VM migration, ant colony system, k-means clustering,

PMs clustering, energy efficiency.

I. INTRODUCTION

From last few years there are various ways to reduce the energy Consumption in Network. There are two

main techniques which are used that are Virtual Machine (VM) consolidation and dynamic server. We are

going to use Ant colony algorithm for virtual machine migration. In the field of computer science and

operation research field, Ant Colony Optimization (ACO) algorithm is used for finding shortest path

between various nodes. In this paper we are supposed to make the cluster of physical machines for

efficiently migration of virtual machine to other physical machine and assigning the monitor to

respective cluster for observing all the physical machine tasks and load balancing in network.

For clustering of physical machine we are using k-means clustering algorithm. K-means is the simple

unsupervised learning algorithms that is used to solve the clustering problem. K-means follows a simple

and easy way for classification of given data set through a certain number of clusters.

Explanation:

In Network large numbers of physical machine are in working state. In each physical machine some

virtual machines performing their task, for that VMs uses required resources from PMs. If VMs perform

another task and resources of that task are available in another PMs also for accessing those resources

VMs get migrate from one to other PMs.

For migration of VMs before it we should find proper physical machine from network. To find that PM it

consume more energy so to reduce that we are going to create the cluster of PMs. So due to that cluster of

PM finding energy of VM get reduce.

A. Ant colony Algorithm

The ant colony algorithm is used to find the optimal path that is based on the behavior of ants that are

going in search for food.

Initially, the ants search the food randomly. When an ant identifies a source of food, it goes back to its

colony leaving behind the "markers" (pheromones) that shows the path of the food source. When other




ants come across the markers, they are also assumed to follow the path with a certain probability. If they

do, then they populate the path with their own markers while bringing the food back to the colony. When

more ants find the path, the markers get stronger until there are a couple of lines of ants traveling to

different food sources that are near to the colony. This ant colony system algorithm is used for Virtual

machine migration.

B. Algorithm:

/* Initialization phase */ For each pair (r,s) τ(r,s):= End-for

For k:=1 to m do Let be the starting node for ant k

( ):= {1, ..., n} -

/* ( ) is the set of yet to be visited nodes for ant k in node */

:= /* is the node where ant k is located */

End-for

2. /* This is the phase in which ants build their tours. The tour of ant k is stored in Tour k . */

For i:=1 to n do

If i<n

Then

For k:=1 to m do

Choose the next node according to Eq. (3) and Eq. (1)

( ):= ( ) –

(i):=( , )

End-for

Else

For k:=1 to m do /* In this cycle all the ants go back to the initial node */

:=

(i):=( , )

End-for

End-if

/* In this phase local updating occurs and pheromone is updated using Eq. (5)*/

For k:=1 to m do

τ( , ):=(1-ρ) τ( , ) + ρ

:= /* New node for ant k */

End-for

End-for

3. /* In this phase global updating occurs and pheromone is updated */

For k:=1 to m do

Compute /* is the length of the tour done by ant k*/

End-for

Compute

/* Update edges belonging to using Eq. (4) */

For each edge (r,s) τ( , ):=(1-α)τ( , )+ α

End-for

4. If (End_condition = True) then Print shortest of

else goto Phase 2

C. Clustering algorithm:

For clustering physical machine we are using k-means clustering algorithm.




K-means clustering is a method used for vector quantization, originally from signal processing, that is

popular for cluster analysis in data mining. K-means clustering partitions n observations into k clusters

in which each observation belongs to the cluster with the nearest mean serving as a prototype of the

cluster.

Algorithm steps:

Let X = {x1,x2,x3,……..xn} be the data points set and V = {v1,v2,…….,vc} be the set of centers.

1) Randomly select any ‘c’ cluster centers.

2) Then calculate the distance between each data point and cluster centers.

3) Assign the data point to the cluster center whose distance from the cluster center is the

minimum of all the cluster centers.

4) Recalculate the new cluster center using:

Where ‘ci’ represents the number of data points in ith cluster.

5) Recalculate the distance between each data point and new obtained cluster centers.

6) If no data point was reassigned then stop, otherwise repeat from step 3).

II. Architecture Diagram:

In above fig. Each PMs contains a CPU with multi-core. Basically CPU performance is in Millions of

Instructions Per Second (MIPS). Here, we Are using Ant colony alogorithm for VMs Migration. If one of

the PM gets overloaded , then we can migrate VM to the under load PM. To find that PM it consume more

energy so to reduce that we are going to create the cluster of PMs. So due to that cluster of PM finding

energy of VM get reduce. Here, we are going to migrate VM with the nearest PM by using Ant colony

algorithm. We are supposed to make the cluster of physical machines for efficiently migration of virtual

machine to other physical machine and assigning the monitor to respective cluster for observing all the

physical machine tasks and load balancing in network.

III. Flow Diagram:




Fig: Migration of Virtual Machine

In above figure, it consists of two modules:

A. Framework of VM migration

In above figure it is the migration of multiple virtual machines. It consists of four main modules:

Migration decision maker, Migration controller, Resource reservation controller and Resource monitor.

1. Migration Decision Maker:

It is responsible for making effective migration decision.

2. Migration Controller:

It control real migration process and it also choice right target machine.

3. Resource reservation controller:

It implements different resource from source and target machine. It also avoids migration failure.

4. Resource Monitor:

It can monitor resource both VM and PM. It used for workload stability for avoiding migration

threshing.

B. Resource reservation method:

In this method there are two parts

1. Source machine: Source has CPU and Memory




a. CPU: Resource reservation of source machine in xen virtualization platform by adjust CAP

b. Memory: Implemented dynamic adjust virtual machine memory

2. Target machine: If not enough resource for reserved virtual machine, the physical machine remove

target machine list.

IV. Conclusion: Hence we are going to propose a system model by clustering PMs and assigning them a respective consolidation manager. By using Ant colony algorithm we are migrating VMs. This manager manages the respective cluster which helps to monitor the all tasks which are performed in that cluster of PMs. Due to monitoring the tasks we will be able to improve the energy efficiency in a network.

V. REFERENCES

[1] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield, “Live

migration of virtual machines,” in Proc. 2nd Conf. Symp. Netw. Syst. Design Implementation,

2005, vol. 2, pp. 273–286.

[2] M. Dorigo and L. Gambardella, “Ant colony system: A cooperative learning approach to the

traveling salesman problem,” IEEE Trans. Evolutionary Comput., vol. 1, no. 1, pp. 53–66, Apr.

1997.

[3] F. Farahnakian, T. Pahikkala, P. Liljeberg, and J. Plosila, “Energy aware consolidation algorithm

based on K-nearest neighbor regression for cloud data centers,” in Proc. IEEE/ACM 6th Int. Conf.

Utility Cloud Comput., Dec. 2013, pp. 256–259.

[4] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. IT-13,

no. 1, pp. 21–27, Jan. 1967.

[5] M. Mishra and A. Sahoo, “On theory of VM placement: Anomalies in existing methodologies and

their mitigation using a novel vector based approach,” in Proc. IEEE Int. Conf. Cloud Computing,

Jul. 2011, pp. 275–282.

[6] L. Deboosere , B. Vankeirsbilck, P. Simoens, F. Turck, B. Dhoedt, and P. Demeester, “Efficient

resource management for virtual desktop cloud computing,” J. Supercomput., vol. 62, no. 2, pp.

741–767, 2012.

[7] E. Feller, C. Morin, and A. Esnault, “A case for fully decentralized dynamic VM consolidation in

cloud ,” in Proc. IEEE 4th Int. Conf. Cloud Comput. Technol. Sci., Dec. 2012, pp. 26–33.

[8] A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics

for energy and performance efficient dynamic consolidation of virtual machines in cloud data

centers Concurrency Comput.: Prac. Exp., vol. 24, no. 13, pp. 1397–1420, 2012.

[9] A. Ashraf and I. Porres, “Using ant colony system to consolidate multiple web applications in a

cloud environment,” in Proc. 22nd Euromicro Int. Conf. Parallel, Distrib. Netw.-Based Process.,

2014, pp. 482–489.

[10] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. IT-

13, no. 1, pp. 21–27, Jan. 1967.

Documents

VM’s Consolidation Using Ant Colony System And Clustering Of PM’s For Energy Efficient Network