Ant Colony Optimization for Effective Load Balancing In Cloud Computing
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1 Ant Colony Optimization for Effective Load Balancing In Cloud Computing 1 Shagufta khan 2 Niresh Sharma 1 M-TECH(CSE) RKDFIST BHOPAL (M.P.) 2 professor(cse) RKDFIST Bhopal(M.P) Abstract- Cloud computing is rapidly improving the latest technology. It is a term which is generally used in internet. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion. Cloud computing involves virtualization, distributing computing, platform, infrastructure, software & web based services. Load balancing is challenging issues in cloud environment. It is the process of distribution of the load among various nodes of distributed system so that the better utilization of resources while also avoiding the situation where some nodes are overloaded and some nodes are idle or under loaded. Many algorithm were suggested to provide the better utilization of resources on the basis of on-demand services.all algorithm enhance the overall performance of the cloud.in this paper we investigated the various algorithm that are based on Ant Colony Optimization (ACO) aimed the load balancing of nodes.we discuss and compare the ACO algorithm to provide the overview of latest algorithm in cloud computing environment. Keywords: Cloud computing, Load balancing, Ant colony. 1. INTRODUCTION With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model called cloud computing, in which resources (e.g., CPU and storage) are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion. In a cloud computing environment, the traditional role of service provider is divided into two: (i) The infrastructure providers who manage cloud platforms and lease resources according to a usage-based pricing model, and (ii) service providers, who rent resources from one or many infrastructure providers to serve the end users [8].Generally cloud computing consist of bunch of distributed server providing demanded services to the client in a network with scalability and reliability. Several researchers have inspired from social behavior of insects. In practical, ants have inspired a number of methods of optimization technique known as Ant colony optimization (ACO).ACO takes inspired from the foraging behavior of real ants [11]. ACO algorithm 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. Many researchers used ACO to solve optimization problem [7]. Load balancing in cloud is a mechanism that distributes the dynamic workload among all the nodes. It achieves the high user satisfaction and resource utilization ratio making sure that no single node is overloaded. In this paper we proposed the Ant colony optimization for Effective load balancing in cloud computing. The rest of this paper is organized as follows: Section II describes the Taxonomy of cloud computing. Section III describes Load balancing. Section IV describes the Ant colony optimization. Section V describes the comparative analysis of various ACO S. Finally Section VI concludes the paper 2. TAXONOMY OF CLOUD COMPUTING 2.1 Cloud Architecture - Fig.1 shows the cloud architecture.cloud architecture is the design of software application that uses internet accessible on demand services [3]. Fig.1cloud architecture According the NIST definition of cloud computing there are two types of cloud 2.2 Deployment model-this refer to location and management of the cloud infrastructure. These are four types Volume 2, Issue 6 November December 2013 Page 77
2 Public Cloud Private Cloud Community Cloud Hybrid Cloud 2.3 Service model-this refer to particular types of services that you can access on a cloud computing platform. These are three types IAAS PAAS SAAS Infrastructure as a Service: IaaS refers to ondemand provisioning of infrastructural resources, usually in terms of VMs. The cloud owner who offers IaaS is called an IaaS provider. Examples of IaaS providers include Amazon EC2 [13], GoGrid [14] and Flexiscale [16] Platform as a Service: PaaS refers to providing platform layer resources, including operating system support and software development frameworks. Examples of PaaS providers include Google App Engine [17], Microsoft Windows Azure [20] and Force.com [18] Software as a Service: SaaS refers to providing on demandz applications over the Internet. Examples of SaaS providers include Salesforce.com [18], Rackspace [15] and SAP Business ByDesign [19]. In particular, five essential elements of cloud computing is clearly articulated[5]: 2.4 On-demand self-service: A consumer with an instantaneous need at a particular timeslot can avail computing resources (such as CPU time, network storage, software use, and so forth) in an automatic (i.e. convenient, self-serve) fashion without resorting to human interactions with providers of these resources. 2.5 Broad network access: These computing resources are delivered over the network (e.g. Internet) and used by various client applications with heterogeneous platforms (such as mobile phones, laptops, and PDAs) situated at a consumer's site. 2.6 pooling. A cloud service provider s computing resources are 'pooled' together in an effort to serve multiple consumers using either the multi-tenancy or the virtualization model, "with different physical and virtual resources dynamically assigned and reassigned according to consumer demand" [5]. 2.7 Rapid elasticity. For consumers, computing resources become immediate rather than persistent: there are no up-front commitment and contract as they can use them to scale up whenever they want, and release them once they finish to scale down. Moreover, resources provisioning appears to be infinite to them, the consumption can rapidly rise in order to meet peak requirement at any time[5]. 2.8 Measured Service. Although computing resources are pooled and shared by multiple consumers (i.e. multitenancy), the cloud infrastructure is able to use appropriate mechanisms.[5] 3. LOAD BALANCING Load balancing in cloud computing systems is really a challenge now. Always a distributed solution is required. Because it is not always practically feasible or cost efficient to maintain one or more idle services just as to fulfill the required demands. Jobs cant be assigned to appropriate servers and clients individually for efficient load balancing as cloud is a very complex structure and components are present throughout a wide spread area. Load balancing algorithms are classified as static and dynamic algorithms. Static algorithms are mostly suitable for homogeneous and stable environments and can produce very good results in these environments. However, they are usually not flexible and cannot match the dynamic changes to the attributes during the execution time. Dynamic algorithms are more flexible and take into consideration different types of attributes in the system both prior to and during run-time[1] Load balancing is the process of improving the performance of system through a redistribution of load among processor. [2] 3.1 Metrics for Load Balancing in Clouds The existing load balancing techniques in clouds, consider various parameters [6]. Overhead Associated - determines the amount of overhead involved while implementing a load-balancing algorithm. It is composed of overhead due to movement of tasks, inter-processor and inter-process communication. This should be minimized so that a load balancing technique can work efficiently. Fig.2 Load balancing type Throughput - is used to calculate the no. of tasks whose execution has been completed. It should be high to improve the performance of the system. Performance is used to check the efficiency of the system. It has to be improved at a reasonable cost e.g. reduce response time while keeping acceptable delays. Utilization - is used to check the utilization of resources. It should be optimized for an efficient load balancing. Scalability - is the ability of an algorithm to perform load balancing for a system with any finite number of nodes. This metric should be improved. Response Time - is the amount of time taken to respond by a particular load balancing algorithm in a distributed system. This parameter should be minimized. Fault Tolerance - is the ability of an algorithm to perform uniform load balancing in spite of arbitrary node Volume 2, Issue 6 November December 2013 Page 78
3 or link failure. The load balancing should be a good faulttolerant technique. Migration time - is the time to migrate the jobs or resources from one node to other. It should be minimized in order to enhance the performance of the system.[6] 4. ANT COLONY ALGORITHM Dorigo M. introduced the ant algorithm based on the behavior of real ants in 1996[11][7], it is a new heuristic algorithm for the solution of combinatorial optimization problems. Investigations show that: Ant has the ability of finding an optimal path from nest to food. On the way of ants moving, they lay some pheromone on the ground; while an isolated ant encounter a previously laid trail, this ant can detect it and decide with high probability to follow it. Hence, the trail is reinforced with its own pheromone. The probability of ant chooses a way is proportion to the concentration of a way s pheromone. To a way, the more ants choose, the way has denser pheromone, and the denser pheromone attracts more ants. Through this positive feedback mechanism, ant can find an optimal way finally [11]. ACO is inspired from the ant colonies that work together in foraging behavior. In fact the real ants have inspired many researchers for their work, and the ants approach has been used by many researchers for problem solving in various areas. This approach is called on the name of its inspiration ACO. 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 [9]. 5. COMPARITIVE ANALYSIS OF VARIOUS ACO S In this section we discuss the comparison of different Ant Colony Algorithm. Different approaches for Load balancing that suit some suitable situation but on the other hand all has some issues. In which some algorithm are suitable for static nature and some other dynamic nature. These algorithms are work on decentralized manner. Table 1 shows the brief description and merits, demerits of each algorithm. As, the first algorithm states for three level cloud computing network [10],works efficiently and achieve better utilization of resources but, gives more overhead during run time. In a Cloud task Scheduling based on Load balancing ACO[7] they proposed the LBACO(Load Balancing Ant Colony Optimization) which is used to find the optimal resource allocation for each task by dynamically. In this algorithm comparison with Basic ACO algorithm and FCFS algorithm, in result nodes are balanced dynamically. In addition to this, tasks are mutually independent i e that is there is no precedence constraints between task and computationally intensive which is not realistic for cloud system. Main drawback of this technique is the heterogeneity of system. The problem of the heterogeneity of system is removed in a technique using Load Balancing mechanism based on Ant colony & complex network theory [12] it provides the good scalability of nodes and better fault tolerant system. Practically the overhead increases during run time environment and poor response time. An Ant encounters dead state at the end due to lack of synchronization of Ants. In Cloud computing initiative using modified ACO framework [4] algorithm it minimize the make span (throughput of heterogeneous computing system).in his algorithm modification involved in basic pheromone updating formula. This modification gives the better utilization of resource but does not give the good fault tolerant system. Every load balancing has primary goal the better utilization of resources. Load balancing of nodes using ACO optimization [9].It is best case for effective load balancing. Because in previous work Ant can move in only one direction [12].But in this algorithm an ant can move both direction that is forward direction and backward direction. Such as an ant finds the food is called forward direction and return to the nest is called backward direction. This is beneficial for balanced the node quickly. This algorithm work fast because an ant can move concurrently in both directions for balanced the node. This algorithm gives the better utilization of resources. But this algorithm [9] works on limited parameter such as speed, network overhead and fault tolerance. This algorithm is more power consuming and it is not consider the energy related issues. Several operational cost does not consider which may result in poor performance. Technique Description Merits Demerits Metrics Considered By Load Balancing Towards a load balancing in Three level cloud computing network [10] Table 1. Comparison of Load Balancing Algorithm 1. Nodes are distributed in three level structures where work is distributed among nodes. 2. It combine OLB 1. Efficient utilization of resource 2.Every node is in the working state 1. The task of every level of node will depend on Upper level Node(i.e. service node depend on service manager utilization Volume 2, Issue 6 November December 2013 Page 79
4 Cloud task Scheduling based on Load balancing ACO[7] A Load Balancing mechanism based on Ant colony & complex network theory in open cloud computing federation[12] Cloud computing initiative using modified ACO framework [4] Load balancing of node in cloud using Ant colony optimization (ACO)[9] (Opportunistic Load Balancing) to keep each node busy & LBMM(Load Balancing Min Min) to achieve the minimum execution time of each task. 1. Proposed ( LBACO) load balancing Ant colony Optimization algorithm to find the optimal resource allocation for each task in dynamic cloud system. 2. it works on minimization of the task completion time that are distributed among virtual machine 3. Comparison with the basic ACO algorithm and FCFS algorithm. 1.It uses small-world & Scale free characteristics of a complex network to support better load balancing 2. This overcomes the heterogeneity. 3. This algorithm is dynamic in nature 1.it minimize the make span (throughput of heterogeneous computing system) 2. This algorithm modified the basic ACO. 3. It is based on heuristics algorithm based on ACO. 4. Modification involved the basic pheromone updation formula. 1. Efficicient distribution of load such that ant never dead. 2.it provide the better synchronization of ants 3. It is best case that finds the node is under loaded or not. 1. Nodes are balanced dynamically 2. LBMM Balanced the entire system whether the entire task are the same or not LBMM can handle all type of condition. 1. Good Scalability 2. It has excellent fault tolerance 1.Better utilization of resources 2. Probability of servicing the request also has been converged using modified scheduling. 3. It is also dynamic in nature. 1. It gives optimum solution of load. 2. it is centralized no single point of failure. 3. the ant updated the single result set rather than updating their own node & service manager node depend on Request manager node) 2. Every time array value is changed. 1. The task are mutually independent that is there is no precedence constraints between task 2. The task are Computationally intensive which is not realistic for cloud system 3. it is not heterogeneity of system. 1. Poor response time 2. Overhead increases 3. Migration time is also increases. 4. At the end ant encounter dead state. 5. Lack of Synchronization of ants. 1. Fault tolerance is not good. 2. Overhead increases. 1. Network overhead because of the large number of ants 2. Points of initiation of ants and number of ants are not clear 3. Nodes status change after ants visits to them is not Utilization, Throughput, Scaling. Utilization, Throughput, Scaling, Heterogeneity Response time, Scaling, Throughput Speed, Network, Overhead, Fault Tolerance, Volume 2, Issue 6 November December 2013 Page 80
5 4. Ant Collect the information faster. 5. Two types movement of ant that is forward movement and backward movement 6. CONCLUSION AND FUTURE WORK In this paper we surveyed multiple algorithms for load balancing in cloud computing environment. We discussed and challenged that must be considered the most suitable and effective load balancing. Our research on Load balancing of node in cloud using Ant colony optimization (ACO)[9] concentrates on efficient load balancing algorithm and future improve it so that it reaches on best and more efficient load balancing. In this algorithm Full replication of data on multiple site i.e. which wastages the storage resources and therefore as our future work this algorithm is modified in terms of storage utilization, heterogeneity, energy efficient metric also considered. REFERENCES [1] Klaithem Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi and Jameela Al-Jaroodi, A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms, 2012 IEEE Second Symposium on Network Cloud Computing and Applications, /12, 2012 IEEE. [2] Abhijit A. Rajguru, S.S. Apte, A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters, International Journal of Recent Technology and Engineering (IJRTE), ISSN: , Volume-1, Issue-3, August [3] Bhaskar Prasad rimal,eunmi choi,ian Lumb, A Taxonomy and Survey of Cloud Computing system, Proceedings of the Fifth International Joint Conference on INC, IMS and IDC, / IEEE [4] S. Banerjee, I. Mukherjee and P.K. Mahanti, Cloud Computing Initiative using Modified Ant Colony Framework, World Academy of Science and Technology, 56, pp , [5] Tharam Dillon, Chen Wu and Elizabeth Chang, Cloud Computing: Issues and Challenges, Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, DOI /AINA , X/ IEEE. [6] Nidhi Jain Kansal, Inderveer Chana, Cloud Load Balancing Techniques : A Step Towards GreenComputing, IJCSI International Journal of result set. It is compiled only once duration of movement of ant taken into account 4. Only availability of node is being considered, while there are other factors that should be taken into consideration 5.full replication of data Utilization Computer Science Issues, Vol. 9, Issue 1, No 1, January [7] Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang, Cloud Task scheduling based on Load Balancing Ant Colony Optimization, 2011 Sixth Annual ChinaGrid Conference, /11, 2011 IEEE. [8] Qi Zhang Lu Cheng Raouf Boutaba, Cloud computing: state-of-the-art and research challenges, J Internet Serv Appl (2010) 1: 7 18 DOI /s J Internet Serv Appl (2010) 1: [9] R. Rastogi Kumar Nishant, Pratik Sharma,, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization." Proceedings of the 14th International Conference on Computer Modelling and Simulation (UKSim), IEEE, pp: 3-8, March [10] S.C. Wang, K.Q. Yan, W.P. Liao and S.S. Wang, Towards a Load Balancing in a Three-level Cloud Computing Network, Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology, pp , [11] M. Dorigo, M. Birattari and T. Stutzle, Ant Colony Optimization-Artificial Ants as a Computational Intelligence Technique, IEEE Computational Intelligence Magazine, pp [12] Z. Zhang, and X. Zhang, A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation, Proceedings of 2nd International Conference on Industrial Mechatronics and Automation (ICIMA), Wuhan, China, May 2010, pages [13] Amazon Elastic Computing Cloud, aws.amazon.com/ec2 [14] Cloud Hosting, CLoud Computing and Hybrid Infrastructure fromgogrid, [15] Dedicated Server, Managed Hosting, Web Hosting by Rackspace Hosting, [16] FlexiScale Cloud Comp and Hosting, [17] Google App Engine, URL [18] Salesforce CRM, [19] SAP Business ByDesign, sinessbydesign/index.epx [20] Windows Azure, Volume 2, Issue 6 November December 2013 Page 81
6 [21] Jinhua Hu, Guofei Sun, Tianhai Zhao, A Scheduling Strategy on Load Balancing of Virtual Machine s in Cloud Computing Environment 3rd International Symposium on Parallel Architectures, Algorithms and Programming, / IEEE. [22] Dr. Hemant S. Mahalle Prof. Parag R. Kaveri Dr.Vinay Chavan, Load Balancing On Cloud Data Centres International Journal of Advanced Research in Computer Science and Software Engineering ISSN: X.2013 Volume 2, Issue 6 November December 2013 Page 82
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