Resource Allocation Using Improved Grey Wolf andThe Ant Colony Optimization Using in Cloud Environment

Cloud computing is the latest trend in data storage, processing, visualization, and analysis. As more government agencies and commercial businesses adopt cloud computing, its usage has grown dramatically. It entails distributing resources dynamically as required in order to offer clients secure services. Cloud computing is another rapidly expanding segment of the computer industry. Users can use this paradigm to access computational resources such as Internet streaming. Scheduling and resource distribution are important but difficult issues in cloud computing. To overcome the issues using Ant Optimization (ACO) and Improved Gray Wolf (IGW) for allocating the resource in cloud. Initially, we monitoring the performance based on send and receive data from cloud server and user. Then second step using Improved Gray Wolf (IGW) to allocating the task based on the position and weights. And Then Third step is ACO, is connecting with the second step and its evaluating and analysis the sequence of data position. And enhances cloud service performance through the use of fuzzy algorithms. In order to estimate cloud server workloads and resource requirements, fuzzy rules are used to route resource volume and usage throughout the resource allocation process. Based on estimated workloads, determine the completion time for each cloud server. Experiments were conducted using a Java programming environmentand compared with existing work in this direction in terms of resource utilization, resource allocation, Recourse utilization, CPU load and performance, and cloud performance improvements.