A load balancing model based on cloud partitioning for the public cloud

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A load balancing model based on cloud partitioning for the public cloud

  1. 1. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511A Load Balancing Model Based on CloudPartitioning for the Public CloudABSTRACT:Load balancing in the cloud computing environment has an important impact onthe performance. Goodload balancing makes cloud computing more efficient andimproves user satisfaction. This article introduces abetter load balance model forthe public cloud based on the cloud partitioning concept with a switchmechanismto choose different strategies for different situations. The algorithmapplies the game theory to the load balancingstrategy to improve the efficiency inthe public cloud environmentPROPOSED SYSTEMThe load balancing model given in this articleis aimed at the public cloud whichhas numerous nodes with distributed computing resources in manydifferentgeographic locations. Thus, this model divides the public cloud into several cloudpartitions. When the environment is very large and complex, these divisionssimplify the load balancing. The cloud has a main controller that chooses thesuitable partitions forarriving jobs while the balancer for each cloudpartitionchooses the best load balancing strategy. There are several cloudcomputing categories with this work focused on a public cloud. A public cloud isbased on the standard cloud computing model, with service provided by a serviceprovider. A large public cloud will include many nodes and the nodes in Differentgeographical locations. Cloud partitioning is used to manage this large cloud. A
  2. 2. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511cloud partition is a subarea of the public cloud with divisions based on thegeographic locations.The load balancing strategy is based on the cloud partitioningconcept. After creating the cloud partitions, the load balancing then starts: when ajob arrives at the system, with the main controller deciding which cloud partitionshould receive the job. The partition load balancer then decides how to assign thejobs to the nodes. When the load status of a cloud partition isnormal, thispartitioning can be accomplished locally.EXISTING SYSTEM:Since the job arrival pattern is not predictable and the capacities of each node inthe cloud differ, for load balancing problem, workload control is crucial to improvesystem performance and maintain stability. Load balancing schemes depending onwhether the system dynamics are important can be either static and dynamic[6].Static schemes do not use the system information and are less complex whiledynamic schemes will bring additional costs for the system but can change as thesystem status changes. Adynamic scheme is used here for its flexibility. Themodelhas a main controller and balancers to gather andanalyze the information. Thus, thedynamic control haslittle influence on the other working nodes. The systemstatusthen provides a basis for choosing the right load balancing strategy.However, loadbalancing in the cloud is still a new problem that needs new architectures to adaptto many changes. Chaczko et al.[8] described the role that load balancing plays inimproving the performance and maintaining stability. There are many loadbalancing algorithms, suchas Round Robin, Equally Spread CurrentExecutionAlgorithm, and Ant Colony algorithm. Nishant et al.[9] used the antcolony optimization method in nodes load balancing. Randles et al.[10] gave a
  3. 3. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511compared analysis of some algorithms in cloud computing by checking theperformance time and cost. They concluded that the ESCE algorithm and throttledalgorithm are better than the Round Robin algorithm. Some of the classical loadbalancing methods are similar to the allocation method in the operating system, forexample, the Round Robin algorithm and the First Come First Served (FCFS)rules. The Round Robin algorithm is used here because it is fairly simple.MODULES1. Main controller and balancersThe load balance solution is done by the main controllerand the balancers. Themain controller first assigns jobs to the suitable cloud partition and thencommunicates with the balancers in each partition to refresh this statusinformation. Since the main controller deals with information for each partition,smaller data sets will lead to the higher processing rates. The balancers in eachpartition gather the status information from every node and then choose the rightstrategy to distribute the jobs.2. Assigning jobs to the cloud partitionWhen a job arrives at the public cloud, the first step is to choose the right partition.The cloud partition status can be divided into three types:(1) Idle: When the percentage of idle nodes exceeds _, change to idle status(2) Normal: When the percentage of the normal nodes exceeds _, change to normalload status.(3) Overload: When the percentage of the overloaded nodes exceeds , change tooverloaded status. The parameters _, _, and are set by the cloud partition
  4. 4. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511balancers. The main controller has to communicate withthe balancers frequently torefresh the status information. The main controller then dispatches the jobs usingthe following strategy:When job i arrives at the system, the main controller queriesthe cloud partition where job is located. If this location’s status is idle or normal,the job is handledlocally. If not, another cloud partition is found that is notoverloaded.3. MotivationGood load balance will improve the performance of the entire cloud. However,there is no common method thatcan adapt to all possible different situations.Various methods have been developed in improving existing solutions to resolvenew problems. Each particular method has advantage in a particular area but not inall situations. Therefore, the currentmodel integrates several methods and switchesbetween the load balance method based on the system status.A relatively simplemethod can be used for the partition idle state with a more complex method for thenormal state. The load balancers then switch methodsas the status changes. Here,the idle status uses an improved Round Robin algorithm while the normal statususes a game theory based load balancing strategy4. Load balance strategy for the idle statusWhen the cloud partition is idle, many computing resources are available andrelatively few jobs are arriving. In this situation, this cloud partition has the abilityto process jobs as quickly as possible so a simple load balancing method can beused. There are many simple load balance algorithm methods such as the Randomalgorithm, the Weight Round Robin, and the Dynamic Round Robin[12]. TheRound Robin algorithm is used here for its simplicity.The Round Robin algorithm
  5. 5. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511is one of the simplest load balancing algorithms, which passes each new request tothe next server in the queue. The algorithm does not record the status of eachconnection so it has no status information. In the regular Round Robin algorithm,every node has an equal opportunity to be chosen. However, in a public cloud, theconfiguration and the performance of each node will be not the same; thus, thismethod may overload some nodes. Thus, an improved Round Robin algorithm isused , which called “Round Robin based on the load degree evaluation”.5. Load balancing strategy for the normal statusWhen the cloud partition is normal, jobs are arriving much faster than in the idlestate and the situation is far more complex, so a different strategy is used for theload balancing. Each user wants his jobs completed in the shortest time, so thepublic cloud needs a method that can complete the jobs of all users with reasonableresponse time. Penmatsa and Chronopoulos[13] proposed a static load balancingstrategy based on game theory for distributed systems. And this work provides uswith a new review of the load balance problem in the cloud environment. As animplementation of distributed system, the load balancing in the cloud computingenvironment can be viewed as a game. Game theory has non-cooperative gamesand cooperative games. In cooperative games, the decision makers eventuallycome to an agreement which is called a binding agreement. Each decision makerdecides by comparing notes with each others. In non-cooperative games, eachdecision maker makes decisions only for his own benefit. The system then reachsthe Nash equilibrium, where each decision maker makes the optimized decision.The Nash equilibrium is when each player in the game has chosen a strategy and
  6. 6. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511no player can benefit by changing his or her strategy while the other playersstrategies remain unchanged.ARCHITECTURE DIAGRAM:ALGORITHMBest Partition Searching
  7. 7. ECRUITMENT SOLUTIONSFOR PROJECT ENQUIRY PLEASE CONTACT:9751442511CONCLUSION:Load balancing in the cloud computing environment has been an important impacton the performance. Good load balancing makes cloud computing more efficientand improves user satisfaction. This article introduced a better load balance modelfor the public cloud based on the cloud partitioning concept with a switchmechanism to choose different strategies for different situations. The algorithmapplied the game theory to the load balancing strategy to improve the efficiency inthe public cloud environment

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