Prepared By:Pooja Gothi En No.-140350702014M.E. Computer Engg.N.G.I.Junagadh

Guided By:Prof. Piyush GohelAsst. ProfessorC.E. DepartmentN.G.I.Junagadh

An Efficient approach for Load balancing using Dynamic AB Algorithm in cloud computing.

OutlineOutlineIntroductionProblem Statements and MotivationObjectivesIntroduction of ApproachExisting SystemLiterature ReviewProposed Work Implementation MethodologyConclusion and Future WorkReferences

IntroductionIntroduction Load balancing is a methodology to distribute workload across

multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload.[1]

Grid computing is aggregation of autonomous resources that are geographically distributed. The nodes in grid permit sharing and selection dynamically at runtime.[1]

Cloud computing refers to a parallel and distributed computing system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements (SLA) established through negotiation between the service provider and consumers. [1,2]

Conti…Conti… Ant colony algorithm: The ants work together in search of new sources

of food and simultaneously use the existing food sources to shift the food back to the nest.

It is a random search algorithm. It takes the behavior of real ant colonies in nature to search the food and connect to each other by pheromone laid down on path optimization algorithm. [2,6]

The Artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm and was proposed by Karaboga in 2005 [15].

This both algorithm is completely inspired by natural foraging behavior .

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MotivationMotivation Cloud Computing is the fast growing technology, which shares the

resource to achieve consistency and economies of scale similar to a utility over a network. Resource sharing requires more optimized algorithm.

In ACO there exist limitations like slow convergence, tendency to stagnancy.

Bee colony algorithm obtain the solution only particular distance, because bee provides optimal solution based on small path.

So I wish to explain both algorithm approaches combine and get more feasible and optimization for dynamic algorithm.

Problem StatementProblem Statement There are certain limitation of ant colony and bee colony algorithm.

In ACO ant’s pheromone is a locally not globally works dynamic in everywhere. After initialization the pheromone of ants by moving through neighbor node of the construction in path . Bee colony is not properly work for the allocating the path in collective some wrong information.

So, to overcome above problems I need to develop new approach for better optimization in load balancing and scheduling to using AB algorithm in effective load balancing.

.

ObjectivesObjectives Ant-Bee algorithm is in fact an optimization on Ant-Net

algorithm and tries to improve its performance .

This algorithm, at the beginning uses forward ants to find a suitable solution from one node to another and then these are the bees who update the pheromone on ants collected data.

It work like as routing table value in pheromone updating , so the we rollback ant pheromone to starting towards bee.

Cloud environmentCloud environment Cloud is a pool of heterogeneous resources. It is a mesh of

huge infrastructure and has no relevance with its name “Cloud”.

In order to make efficient use of these resources and ensure their availability to the end users “Computing” is done based on certain criteria specified in SLA. Infrastructure in the Cloud is made available to the user’s On-Demand basis in pay-as-you-say-manner. [1]

Computation in cloud is done with the aim to achieve maximum resource utilization with higher availability at minimized cost.

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Cloud platformsCloud platforms

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IntroductionIntroductionWhat is load balancing ? why these need of

cloud computing?

Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is a greatest platform which provides storage of data in very lower cost and available for all time over the internet, that’s why load balancing for the cloud has become a very interesting and important research area.

Load balancing helps to attain a high user satisfaction and resource utilization ratio by ensuring an efficient and fair allocation of every computing resource.

Load balancing makes sure that all the processor in the system or every node in the network does more or less the equal amount of work at any moment of time.

Conti…Conti…The goals of load balancing are to: • Improve the performance• Maintain system stability• Build fault tolerance system • Increase the availability • Increase the user satisfaction • Improve the resource utilization ratio

Effective load balancing Effective load balancing Approaches Approaches There are tow main approach for load balancing named static

load balancing and dynamic load balancing. [2]

1. Static load balancing. In this approach of load balancing, we consider static

information of system to choose the least loaded node.

It performs better in terms of complexity issue but compromises with the result as decision is made on statically gathered data.

It is further classified as Distributed and Centralized

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Conti…Conti… 2. dynamic load balancing

This approach provides QOS aware load balancing.

In this strategy, current system state plays major role while making decisions. Despite the fact that dynamic load balancing has higher run rime complexity then static one, dynamic has better performance report as it considers current load of system for choosing next datacenter to serve the request.

This will surely provide an optimal choice from available ones for that state of system.

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Ant Colony optimization Ant Colony optimization Ant colony optimization (ACO) is a population-based metaheuristic that

can be used to find approximate solutions to difficult optimization problems.[7]

In ACO, ant find the food and follows the path and put some chemical substance which called pheromone.

And remaining Ant follows the one kind of instruction on the base of pheromone.

The solution construction process is biased by a pheromone table as like routing table.

ACO algorithm can be used to schedule large-scale work flows with various QOS parameters .

Here in fig we show the natural Here in fig we show the natural behavior of antbehavior of ant

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Dynamic Ant colony Dynamic Ant colony OptimizationOptimization ACO can be used in many ways , but limitation like slow

conversion and poor performance.

There is only one kind of pheromone in ACO and the path and path weight is stable, and cannot fit for dynamic load balancing.

Dynamically here Ant follows the instruction of Pheromone but not finding the best laid path for searching thus dynamic ACO works behind on the find best path or shortest path to finding the source.

Bee Colony optimization Bee Colony optimization Bee colony optimization (BCO) is a swarm used meta- heuristic

algorithm.[13]

This algorithm simulates the foraging behavior of honey bees. This algorithm has three phases. There are employee bees, onlooker bees and scout bees.

Scout bee: it is responsible for finding new food. The new nectar source. Onlooker bee : It gets the information of food sources from the

employed bees in the hive and select one of the food source to gathers the nectar.

Employee bee: It stay on a food source and provides the neighborhood of the source in its memory

The algorithm has a well-balanced exploration and development ability.

Conti…Bees life algorithm is an optimization algorithm used for job scheduling. Bees colony contains single breeding female bee called queen and male known as Drones.

Bees start with scout bees with initial population first the bees choose randomly in space. Then, fitness is calculated for bees. The highest fitness is chosen as “selected bees” and remaining bees are workers. The selected bees alone visit the site by choosing neighbour search.

Why this combined ? In traditional many static algorithms are proposed, but all these

algorithms do not produce optimal job scheduling and load balancing.

Ant-Bee algorithm is in fact an optimization on Ant-Net algorithm and tries to improve its performance .

The AB algorithm is a combination of two dynamic algorithms, Ant Colony Optimization and Bees Life algorithm. here in path finding the ant pheromone is not rollback then we started bee here to continuing the path finding by the pheromone updating.

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ANT COLONY ALGO.

BEE COLONYALGO.

AB ALGORITHM

Existing system In existing system researchers have work on Ant colony

algo and Bee colony algorithms used for load balancing in cloud computing.

Ant algo is give better work in cloud using load balancing but it’s not attempt dynamic job to pheromone value and bee is not used load balancing to another approach.

So , now more effective load balancing technique to consider both Ant & Bee is AB algo to improve load balancing strategy.

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Paper Method / Algorithm Advantage DrawbacksAnt colony optimization for effective load balancing in cloud computing.[2]

(IJETTCS)

Publication year:2014

In here different kinds of load balancing technique is used.

Provide dynamic and effective load balancing

Ant can move only one direction ,it can’t rollback.

Workflow scheduling in grids : An Ant colony optimization approaches[6]

Ieee paper

Publication year :2007

Aco algorithm used for scheduling to balanced load in grid.

Give dynamic solution using QOS parameters

In shortest path ant’s choice is ignoring it work for priority.

Grid resource management by means of Ant colony optimization.[8]

Ieee paper

Publication Year : 2006

A simple ant-based technique is proposed in for resource management and task scheduling and its scalability is validated

Established optical network for important requirement in load balancing

It is not scalable and can’t extended process to incorporate information about job requirement.

Literature Survey

Conti..Conti..Paper Method / Algorithm Advantage Drawbacks

A bee colony based multi-objective load balancing technique for cloud computing environment.[7]

(IJCA)publication year : 2014

Bee colony algo Bee colony and genetic combination based algorithm provides better efficiency and effectively utilize the resource

Runtime fault tolerance may become unavailable and during heavy load condition improve QOS parameters.

Improve performance of load balancing using artificial Bee colony.[9]

(IJCA)Publication year : march 2015

Load balancing techniqueAnd some threshold aspect.

Improve time efficiency and reduce makespan time and minimize the node of failure

They do not share the load among the available resources. fail in produce load balance schedule.

ContiConti……Paper Method/ Algo Advantages Drawback

Artificial Bee colony algorithm and it’s application to generalized assignment problem.[20]

Source: Swarm Intelligence: Focus on Ant and Particle Swarm OptimizationPublication year : jan 2014

PSO NP-hard problem is presented in detail along with some comparisons.

Effectively solve small to medium size task and also solve complex optimize problem

Not work on large size and tightly constrained generalised assignment problem.

An Ant Colony Based Load Balancing Strategy in Cloud Computing.[13]

Publication year : 2014Springer paper

load balancing strategy traditional approaches like (FCFS), local search algorithm like Stochastic Hill Climbing (SHC),another soft computing approach Genetic Algorithm (GA)

Give surety for QOS approach Requirement is fulfill here.

fault tolerance and different function variation to calculate the pheromone value can be used for further research work.

Conti…Conti…Paper Method/Algo Advantage Drawback

Comparison of Ant Colony and Bee Colony Optimization for Spam Host Detection.[10]

Ijerd journal Publication year :Nov-2012

Compare both algo….. Here give performance by the set of classification rule.

Cloud result not give better exact through put in any critical condition

An efficient load balancing using Bee foraging technique with Random stealing.

[11]

(IOSR-JCE) Publication year :Mar-Apr2014

Use random stealing method.

Virtual machine is idle. It thus saves the idle time of the processing element in a Virtual machine

It provide energy aware scheduling

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Proposed systemProposed system In proposed system, the ant colony and bees life algorithm are

combined to improve the effectiveness of load balancing .

The ants in our proposed algorithm will continuously originate from the Head node. These ants traverse the width and length of the network in such a way that they know about the location of underloaded or overloaded nodes in the network.

These Ants along with their traversal will be updating a pheromone table, which will keep a tab on the resources utilization by each node.

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AB algorithm used in system AB algorithm used in system

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Conti…Conti…Ant-Bee algorithm In this section we are briefly explaining Ant-Bee algorithm:[24]

A forward ant starts traveling through the network. Whenever this forward ant reaches a node, if this node is not the destination node, it is directed toward the destination. These forward ants have the same priority as data packets.

When a forward ant reaches the destination, based on the provided information by her, a backward bee is created then the forward ant is killed and the new born bee continues the journey.

This backward bee traverses the forward ant’s travelled route in reversed direction and on its way updates the pheromone tables and is finally killed at the starting node (the node which had initiated the forward ant). It has to be mentioned that backward bees have more priority than data packets to be able to apply the emergency changes as rapidly as possible.

Process…Process…

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Work of Ant Bee colonyWork of Ant Bee colony The pheromone evaporation at time t be ρt , where the value

of ρt lies on [0,1] now evaporation of pheromone at time t+1 is suggest one natural and traditional equation is

ρt+1 = α ρt + β(1- ρt) = k ρt + β

Where α , β = constantk= α – β0 ≤ α , β ≤ 1 f(ρt ) = ρt +1

Conti…Conti… Here for effective load balancing in our existing system ant

optimization use updated pheromone is as follows equation. Modified ACO Pheromone Updated Strategy: Pheromone is a chemical substance which the ants release on

path while traversing the cloud network. The probability of traversing a particular path by ants

depends on the pheromone concentration on the path, which can be retrieved from foraging, trailing pheromone

Conti…Conti… The main aim of the two types of pheromone updating

according to the types of nodes they are currently searching for.

The ants after originating from the head node, by default follow the Foraging pheromone, and in the process, they update the FP trails according to the formula.

After coming upon an overloaded node they follow the Trailing Pheromones and simultaneously update the TP trails of the path.

After reaching an underloaded node of the same type they update the data structure so as to move a particular amount of data from the overloaded node to under loaded node.

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Conti…Conti…

We also proposed the movement of ants in two ways similar to the classical ACO, which are as follows:

1) Forward movement-The ants continuously move in the forward direction in the cloud encounter in overloaded node or under loaded node.

2) Backward movement-If an ant encounters an overloaded node in its movement when it has previously encountered an under loaded node then it will go backward to the under loaded node to check if the node is still under loaded or not and if it finds it still under loaded then it will redistribute the work to the under loaded node. The vice-versa is also feasible and possible.

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Conti…Conti…Foraging Pheromone (FP) :

While moving from underloaded node to overloaded node, ant will update FP. Equation for updating FP pheromone is

FP( t+1 ) = ( 1 - βeva )FP(t) + ∆FP.

Where, β eva = Pheromone evaporation rate FP = Foraging pheromone of the edge before the move FP( t+1 ) = Foraging pheromone of the edge after the move ∆FP = Change in FP

Conti…Conti…Trailing Pheromone (TP): While moving from overloaded node to underloaded node,

ant will update TP. Equation for updating TP pheromone is

TP( t+1) = ( 1 –βeva )TP(t) + ∆TP

Where, β eva = Pheromone evaporation rate TP = Trailing pheromone of the edge before the move TP( t+1 ) = Trailing pheromone of the edge after the move∆TP = Change in TP

Bee optimization in systemBee optimization in system

The distinguishing element between Ant-Bee and Ant-Net algorithms is Ant-Bee’s use of backward bees , different kinds of bees which are in use in this algorithm are:

1. Dancer bee: whenever this bee reaches a node, after updating the node’s pheromone table, sets down its traverse time in related field. The pheromone updating strategy is the same as backward ants in Ant-Net algorithm.

2. Follower bee: These are considered as naive bees that should collect information based on dancer bee’s dancing parameters. They use equation for doing so. Assume that the follower bee has come from node j to i on its way to destination d.

Conti…Conti… T= β T_foll+(1- β) T_dan 0 ≤ β ≤ 1

Where T_foll= follower bee’s trip time. T_dan = selected trip time among dance bee

based

β : An impact factor which determines the two first factor’s effect. If β = 1 , it means the follower bee doesn’t pay any attention to dancer bees and this algorithm will work the same as Ant-Net algorithm. In contrast, if the follower bee is entirely following the dancer bee.

Process…Process… Ants attract to pheromone laid from ants in some colony, the

so called ” repulsion ” and “attraction” strategy prevents ants from different colony in same path.

This strategy is introduced by varela & sinclire. it used in multiple way network for virtually.

General structure of a pheromone table for Multiple Ant-Bee colony is shown in Table .

Pheromone tablePheromone table ToFrom

1 2 ….…….

N

1 . .

P11 , M11 . .

P12 , M12 . .

……………..

P1n , M1n . .

. . .

. . .

. . .

……………

. . .

L Pl1 , Ml1

P12 , M12

……………

Pln , Mln

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L= All the outgoing links N =number of nodes minus one (the node itself). Each cell contains values M ij and Pij . Pij= chance of node i to be selected as the next node for colony K Mij =represents some of the recorded trip times of dancer bees form K th colony which have travelled from i to j

Conti…Conti… Here the proposed algorithm considers some pheromone value for

selecting optimal VM for load balancing.

Here VMs are defined based on their loadsOVM – Overloaded node.UVM – Underloaded node

Here some information to when random node is overloaded then we put it in by underloaded to balanced the node.

When load fairly distributing on the server node but finding a load is imbalance then to achieving this, define a THRESHOLD variable which tells how much load should exchange between nodes.

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Selection process of nodeSelection process of node Now updating the pheromone value then selecting a next

node whenever ant is killed here.We used two strategy

1) Attraction strategy2) Repulsion strategy

Attraction strategy: we use α parameter to indicate attraction of a forward ant from colony K to node I on its way to destination.

Repulsion strategy: In the proposed algorithm we use β parameter to show repulsion of forward ants of colony K to j on its way to destination d.

Conti…Conti…

hbijn : quantity of pheromone K in the edge linked to node j on its way to destination d(jd element in pheromone table of K th colony) Ni= A set containing all possible outgoing edge for forward ant.

: Repulsion of forward ants of colony K i to j on its way to destination d.

: Amount of pheromone from all colonies (except K itself) in edge linked to node j on its way toward destination d.Ni= sum of all possible edges for the forward ant.

AB Algo steps.AB Algo steps. AB algo Programming Steps:- 1. Configure cloud analyst tool in develop environment. 2. Create data center and host servers into it. 3. Create Virtual Machines (VM) and allocate resources (HDD, RAM, CPU,

and bandwidth) to it. 4. Run AB algorithm. 5. Initialize the threshold value per Virtual Machine (VM) in terms of

resources. 6. Run modified ACO and Artificial Bee algorithm simultaneously and

output optimal solutions. It is done by initializing pheromone and bee's population. Then pheromone concentration and bees fitness is checked simultaneously to get the optimal node.

7. Select common best optimal solution nodes. 8. Distribute load on them and achieve load balancing. 9. Move to step 5.

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Flow of AB algorithmFlow of AB algorithm

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startInitialize

pheromone table for ants

valueDeclare to

nodeLoad move to

node

overloaded

Traverse to

maximum Tp

Update pheromone

Load is >

Traverse to minimum

FP Update pheromoneMove on

next strategy

Bee Colony strategy

Check both

Attraction strategy

Repulsion Strategy

Node are balanced

StopEnd of process

Implementation MethodologyImplementation Methodology In cloud environment different users, resources , scheduler

implement by Cloudsim.

Cloudsim is a well known simulation for cloud computing and designed to support various simulation tests across the IAAS ,PAAS and SAAS.

Cloud Sim provides support for modeling and simulation of virtualized Cloud-based data center environments such as dedicated management interfaces for VMs, memory, storage, and bandwidth. So AB algorithm used Cloudsim as a developing tool .

Conclusion and future workConclusion and future work We have represented survey of existing Ant and Bee colony

work different dynamic strategy in load balancing.

It can be extended with scenario having more data center scattered around different location and trying to improve load balancing approach according to some different way like used AB algorithm approaches. In future AB algorithm work establishing to task scheduling and node allocate on load to random VM.

In future work I will show whole system in brief that provide good solution for effective load balancing.

Paper publication detail.Paper publication detail.Title of paper: ” An Effective Load Balancing Approach

in Cloud Using Dynamic AB Algorithm ”

Journal name: International Journal of Innovative Research in Science, Engineering and Technology

Impact Factor : 5.45 DOI:10.15680/IJIRSET.2015.0408129

Date of publication: 24/8/2015

Issue: Vol. 4, Issue 8, August 2015

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