Software testing is the major process in software development life cycle. Regression testing is very costly and inevitable activity that is to be performed in a restricted environment to ensure the validity of modified software. It is inefficient to rerun every test case from test suite when some kind of modification is done in the software. Test case selection and prioritization techniques select and organize the test cases in a test suite based on some criteria such that the faults are covered quickly with minimum execution time. This task can be done on basis of the Ant Colony Optimization technique (ACO) of Swarm Intelligence as it is not deeply studied yet. The main objective of this thesis is to solve the path problem: Means to find the shortest path and Resolve the time problem: Means to minimize the time of finding shortest path. Because of time and cost constraint, it is not possible to perform extensive regression testing. Techniques such as test case selection and prioritization are used to solve the problem of time and cost constraints. In this paper we are modifying the previous technique to get better results in case of execution time and then the Effectiveness of techniques is checked with the help of APFD metric.
Cloud computing has become the hottest issue due to its wide range of services. Due to a large number of users, it becomes more significant to provide high availability of services to cloud users. The majority of existing scheduling techniques in the cloud environment is NP-Complete in nature. Many researchers have utilized meta-heuristic techniques to schedule the jobs in cloud data centers. The majority of existing techniques such as Genetic Algorithm, Ant colony optimization, Non-dominated Sorting Genetic Algorithm (NSGA-III), etc. suffer from poor convergence speed. Also, most of these techniques are either based upon scheduling or load balancing. Therefore, to overcome these issues, a new Variance Honey Bee Behavior with multi-objective optimization method (VHBBMO) is proposed in this paper. Extensive experiments have been conducted by considering the various set of jobs. The experimental results have shown that the proposed method provides more significant results than available methods.
Load balancing techniques play significant role in cloud computing environment because it directly affects the performance of cloud data centers. An efficient load balancing technique not only provides high availability of resources to cloud users, but also enhances the performance of cloud data centers. Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima issues. Also, multiobjective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate and energy consumption. Index terms: Load balancing, Particle swarm optimization, Cloud computing, Energy efficiency. I. INTRODUCTION Cloud computing is growing phase having a world view of substantial scale distributed computing in internet era [1]. It is a combination of grid and 'X' as a resources in wireless environment. Here, 'X' may be infrastructure, platform, software, data, hardware etc. [2]. The primary objective of scheduling is the assignment of jobs to available set of servers so that execution time can be minimized. Another feature of scheduling is decision-making process and is mostly used in service and manufacturing organizations. Scheduling takes place by taking help of load balancing algorithms [3]. Job is a term related with scheduling used at application level and is a script or program to execute a specific set of jobs. Load balancing algorithm is a technique to which jobs are optimistically assigned to data center resources. There is no completely perfect scheduling mechanism available due to different scheduling objectives. Scheduling algorithms can be executed or implemented under suitable conditions according to assigned applications by a good scheduler. Scheduling algorithm is a mechanism that solves a problem in seconds, minutes or even hours. Time used for execution of particular algorithm measures the efficiency of that algorithm and so time complexity can be measured from the efficiency. Time complexity plays a significant role in time execution of an algorithm. There are some time complexity algorithms used for job execution. The problem is feasible, traceable, fast and efficient in case ...
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