In today's world, the IT industry is emerging day by day; therefore, the need for storage and computing is increasing multifold. Cloud computing has transformed the IT sector to much greater heights by virtualizing the systems, thereby reducing cost of the hardware to greater extent. Cloud computing is based on the pay as per use policy. Due to the exponential growth in cloud computing, users demand supplementary services and improved results which makes load balancing a major challenge. Load balancing distributes the workload across multiple nodes to optimize the performance of the system. Various load balancing algorithms exist to provide better resource utilization. This paper gives a brief analysis of load balancing algorithms and also compared these algorithms on the basis of certain metrics like average response time, processing cost, and data servicing time.

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This article describes how predicting change-prone classes is essential for effective development of software. Evaluating changes from one release of software to the next can enhance software quality. This article proposes an efficient novel-based approach for predicting changes early in the object-oriented software. Earlier researchers have calculated change prone classes using static characteristics such as source line of code e.g. added, deleted and modified. This research work proposes to use dynamic metrics such as execution duration, run time information, regularity, class dependency and popularity for predicting change prone classes. Execution duration and run time information are evaluated directly from the software. Class dependency is obtained from UML2.0 class and sequence diagrams. Regularity and popularity is acquired from frequent item set mining algorithms and an ABC algorithm. For classifying the class as change-prone or non-change-prone class an Interactive Dichotomizer version 3 (ID3) algorithm is used. Further validation of the results is done using two open source software, OpenClinic and OpenHospital.

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