Paper Title
Dynamic Virtual Machine Load Balancing In Cloud Network

Cloud Computing is a large set of geographically distributed heterogeneous resources for solving problems in science, but to select the best resource for a given job is still a major problem in the area of Cloud Computing. Hence the most prevalent problem in Cloud computing is the problem of load balancing. During Load balancing task under clouds certain types of information including the jobs waiting in the queue, arrival rate of Jobs, Physical Machine’s processing rate, at each processor, in addition to neighboring processors, must be exchanged among the processors to make any improvement the overall performance. A distributed solution is required and always in need, as it is not always practical feasible or cost efficient to maintain one or more idle services just as to fulfill the required demand. Jobs cannot 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 can be defined as static and dynamic algorithms. The Static algorithms are typically suitable for homogeneous and stable settings and they produce good results in these settings. But, their inflexible nature and dynamic changes to the attributes during the execution time makes them unfit for Most Cloud Environments. Our proposed work is dynamic algorithm that is Fast and flexible and is able take into consideration various types of changes in heterogeneous cloud environments. This paper presents work on Ant colony optimization technique to find the optimal solution in order to reduce the cost of load balancing between tasks and machines and compares the results of genetic algorithm for load balancing with the Ant colony optimization technology which shows that for the Ant colony optimization produces best optimal results in less time. Index Terms—Cloud computing, Ant Colony, Genetic Algorithm, Load Balancing.