Improvised Solution of Complex Engineering Problems Through Soft Computing Techniques

Abstract

Query optimization is a efficient way to execute a query with different plans or algorithms. The query optimization is implemented in distributed database management system (DDBMS) and crowd sourcing. The crowd sourcing is known as a combined process enlargement or issue resolving method that needs unofficial and geographical dispersed participants or crowd for solving the problem. In query optimization used in DDBMS, it has the following challenges namely; high cost values, increase execution time and complexity. To overcome the challenges, in DDBMS query optimization, a modified grey wolf optimization (MGWO) technique is presented that requires less iteration to compute the optimal plans (i.e. takes less amount of time to compute optimal QPs (Query Processing) for the user query) than the existing methods. In crowd sourcing during query optimization, it suffers from less balance between latency and cost. To overcome these problems, a multi objective ant lion optimization is presented that finds the shortest optimal path for obtaining the result on the bases of user query by cost, latency and accuracy factors. Besides we also have focused the research over Virtual Machine (VM) migration in cloud computing. In cloud computing environment VM movement is the task of transferring a virtual machine from one physical environment to another. Minimum Migration Time (MMT) is used to select the VM selection. The under loaded and over loaded hosts in VM migration can be identified by using Adaptive Inter Quartile Range (IQR). The IQR can find outliers in data. In cloud computing during VM migration process the following challenges are been employed; Standard deviation of VM placement and migration, Energy consumption, Service-level agreement (SLA) performance degradation, and total SLA violation. To overcome these challenges number of optimization techniques are been used to improve the VM migration process in cloud computing. newline