In multiprocessor system, scheduling of tasks to assigned on the number of processors. The major objective of task scheduling is to find minimum execution time of a program. It is well known that the complexity of a general scheduling problem is NP-Complete [9], there are number of heuristic have been developed. Each of which may either find optimal or near optimal scheduling under the different conditions. The task scheduling is represented by a directed acyclic graph (DAG). In this paper, we present a new scheduling algorithm which is called Task Scheduling based on Breath First Search(TSB). The TSB is queue based approach to schedule parallel tasks on the homogenous parallel multiprocessor system. Its performance is evaluated in comparison with Highest Level First with Estimate Time (HLFET) algorithm, Modified Critical Path (MCP) algorithm, Earliest Time First (ETF) algorithm and Dynamic Level Scheduling (DLS) algorithm in terms of Speedup, Efficiency, Load Balance and Normalized Scheduling Length (NSL).

The computer technologies have rapidly developed in both software and hardware field. The complexity of software is increasing as per the market demand because the manual systems are going to become automation as well as the cost of hardware is decreasing. High Performance Computing (HPC) is very demanding technology and an attractive area of computing due to huge data processing in many applications of computing. The paper focus upon different applications of HPC and the types of HPC such as Cluster Computing, Grid Computing and Cloud Computing. It also studies, different classifications and applications of above types of HPC. All these types of HPC are demanding area of computer science. This paper also done comparative study of grid, cloud and cluster computing based on benefits, drawbacks, key areas of research, characterstics, issues and challenges.

No abstract

Task scheduling algorithms are also known as multiprocessor scheduling algorithms. These are mainly used in scientific and engineering applications. It is also considered as a NP-complete problem. The primary objective of list task scheduling algorithms is to minimize the overall execution time. Task scheduling in multiprocessor environment is represented by directed acyclic graph (DAG). It is classified into static and dynamic task scheduling. List task scheduling algorithms is a type of static task scheduling algorithm. In this paper, we have studied different list of task scheduling algorithms: HLFET, ISH, MCP, ETF, DLS and CNPT algorithms. A comparative study amongst the list task scheduling can be based on the following matrices: scheduling length, speedup, efficiency, load balancing, and normalized scheduling length(NSL). 

Now days breast cancer has emerged as a diseases effecting women to suffer a life threating phase and eventually lead to death world wide. The prediction of breast cancer in woman at the initial stage can aggrandize recovery and chance of abidance considerably as the essential medical treatments can be adapted on time and stop its further growth. Moreover the precise categorization of tumor eliminates the avoidable treatments and patients skips from witnessing the medical emergencies. Thus the exact categorization of breast cancer either benign or malignant and the precised analysis of each is a matter of important exploration. Machine learning have extensively beneficial aspects in critical feature extraction from the breast cancer dataset. Thus the machine learning can be astronomically honored as a alternative methodology in breast cancer pattern categorization and forecast modeling. In this paper ML techniques namely Support vector machines (SVM), logistic Regression, Random forest tree (RDT) and k-nearest neighbours (k-Nns) are over viewed and later performance measures compared for breast cancer analysis and prognosis.