Ant Colony Optimization for Efficient Resource Allocation in Cloud Computing

Resource scheduling and energy consumption is an important issue of Cloud Computing. The intention of optimization for scheduling resources is an important issue to be considered in scheduling of different resources among heterogeneous users. The resources are placed in a distributed location in cloud and the major task is to distribute the resources effectively such that the processing time and energy is reduced. In this paper, Ant Colony optimization technique is proposed to optimize the resources in an efficient manner. ACO is used to choose one among the different alternative paths to determine the processing order of each resource. The search space is reduced to provide a better solution. Travelling Salesman Problem(TSP) is the application that is used here to find the shortest path to the destination. This reduces the delay in allocating resources to the user by providing a global search technique. The energy conservation which is the main objective of Green Computing, can also be achieved using this technique

AN ENHANCED SCHEDULING APPROACH WITH CLOUDLET MIGRATIONS FOR RESOURCE INTENSIVE APPLICATIONS

Cloud computing is one of the most advanced technologies to present computerized generation. Scheduling plays a major role in it. The connectivity of Virtual Machines (VM) to schedule the assigned tasks (cloudlet) is a most attractive field to research. This paper introduces a confined Cloudlet Migration based scheduling algorithm using Enhanced-First Come First Serve (CMeFCFS). The objective of this work is to minimize the makespan, cost and to optimize the resource utilization. The proposed work has been simulated in the CloudSim toolkit package. The results have been compared with pre-existing scheduling algorithms with same experimental configuration. Important parameters like execution time, completion time, cost, makespan and utilization of resources are compared to measure the performance of the proposed algorithm. Extensive simulation results prove that introduced work has better results than existing approaches. 99.8% resource utilization has been achieved by CMeFCFS. Plotted graphs and calculated values show that the proposed algorithm is very effective for cloudlet scheduling

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

An efficient evolutionary algorithm for solving incrementally structured problems

Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.United States. Dept. of Energy (award DESC0005288