A New Strategy to Optimize the Load Migration Process in Cloud Environment

Cloud computing is a model of internet-based service that provides easy access to a set of changeable computational sources through internet for users based on their demand. Load balancing in cloud have to manage service provider resources appropriately. Load balancing in cloud computing is the process of load distribution between distributed computational nodes for optimal use of resources and have to decrease latency in order to prevent a situation in which some nodes overloaded and some others under-loaded or be in the idle mode. Load migration is a potential solution for most of critical conditions such as load imbalance. However, many load migration methods are only based on one purpose. Practically, considering just one objective for migration can be in contrary to the other objectives and may lose optimal solution to work in existing situation. Therefore, having a strategy to make load migration process purposeful is essential in cloud environment. The main idea of this research is to reduce cost and increase efficiency in order to be compatible with cloud different conditions. In the recommended method, it is tried to improve load migration process using several different criteria simultaneously and apply some changes in previous methods. The simulated annealing algorithm is employed to implement the recommended strategy in the present research. Obtained result show desired performance and efficiency in general. This algorithm is highly flexible by which several important criteria can be calculated simultaneously.

@article{Mirvaziri2016,
  title     = {A New Strategy to Optimize the Load Migration Process in Cloud Environment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Hamid Mirvaziri and ZhilaTajrobekar},
  doi       = {10.14569/IJACSA.2016.070745},
  url       = {https://doi.org/10.14569/IJACSA.2016.070745}
}