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In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of internet users can get able to serve various kinds of services like on-demand access, storage space, software building platforms, data recovery, etc., and pay only for that service that they have consumed. Enormous challenges in the cloud domain such as fault tolerance, energy efficiency, scheduling, resource provisioning, load balancing, etc. This paper is focused on load balancing domain. This can be defined as a redistribution of the workload among various available virtual machines in such an identical manner that would lead to a balanced state. This paper presents the evaluative and inclusive review of numerous load balancing (LB) methods. Quality of services(QoS) is vital role that contain various parameters to evaluate the load balancing methods in respect of makespan, speedup, cost, throughput, etc. This paper is highlighted numerous of load balancing methods with their brief explaination, platform used, different simulator and tools used by these methods and based on QoS parameters. [ABSTRACT FROM AUTHOR]

Cloud Computing Environment (CCE) has gained considerable attention in recent years because of scalability, flexibility, and cost-effectiveness. Workflow scheduling, a critical aspect of CCE, involves assigning tasks of a workflow to suitable resources to optimize various performance metrics. Load balancing plays an important role in achieving efficient resource utilization and reducing execution time in workflow scheduling. There are many scheduling algorithms are developed and Min-Min is out of them that uses independent tasks. However, the original Min-Min heuristic does not consider the load distribution among resources, which can lead to imbalanced resource utilization and increased execution time.To address this limitation, we introduce a modified Min-Min heuristic that incorporates load-balancing principles. Taking into consideration both task completion time and resource load, the method aims to achieve optimal load distribution and minimize the overall execution time of the workflow.To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are performed using benchmark workflow datasets such as randomly generated workflows and Montage workflows. The results show that the modified Min-Min heuristic outperforms as compared to heuristics HEFT and PETS in terms of load balancing, makespan, speedup, efficiency,and resource utilization. The proposed method achieves more balanced resource allocation, reduces the completion time of the workflow, and improves overall system performance. The present study contributes to the area of workflow scheduling in CCE by presenting a load-balancing method that enhances the efficiency of resource allocation. The findings emphasize the importance of considering load-balancing principles in task scheduling to optimize performance in cloud computing environments. The proposed method can serve as a valuable tool for practitioners and researchers involved in workflow scheduling in CCE, offering improved resource utilization and reduced execution time. [ABSTRACT FROM AUTHOR]

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, logistic Regression, Random forest tree and k-nearest neighbours (k-Nns) are over viewed and later performance measures compared for breast cancer analysis and prognosis. [ABSTRACT FROM AUTHOR]

At present, the cloud computing environment (CCE) has emerged as one of the significant technologies in communication, computing, and the Internet. It facilitates on-demand services of different types based on pay-per-use access such as platforms, applications and infrastructure. Because of its growing reputation, the massive requests need to be served in an efficient way which gives the researcher a challenging problem known as task scheduling. These requests are handled by method of efficient allocation of resources. In the process of resource allocation, task scheduling is accomplished where there is a dependency between tasks, which is a Directed Acyclic Graph (DAG) scheduling. DAG is one of the most important scheduling due to wide range of its applicable in different areas such as environmental technology, resources, and energy optimization. NP-complete is a renowned concern, so various models deals with NP-complete that have been suggested in the literature. However, as the Quality of Service (QoS)-aware services in the CCEplatform have turned into an attractive and prevalent way to provide computing resources emerges as a novel critical issue. Therefore, the key aim of this manuscript is to develop a novel DAG scheduling model for optimizing the QoS parameters in the CCEplatform and validation of this can be done with the help of extensive simulation technique. Each simulated result is compared with the existing results, and it is found that newly developed algorithm performs better in comparison to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The major objective of any task-scheduling algorithm in parallel computing is to minimize the overall scheduling length. In this research paper, we present a task-scheduling algorithm in parallel computing called the Critical Path and b-level Based Task Scheduling algorithm. This algorithm is based on a new attribute, which is called the CPB attribute, which is based on two wellknown priority attributes: Critical Path and B-Level. This algorithm uses entry tasks as duplicates in all of the available processors. The major objective of any task-scheduling algorithm is to minimize the overall scheduling length. Here, an application program in task scheduling is represented by a directed acyclic graph. We consider all processors to be homogenous in nature and fully connected. The Critical Path and b-level Based Task Scheduling algorithm gives a minimum scheduling length when compared to heuristic task-scheduling algorithms like the HLFET, MCP, DLS, and ETF. We have also done an analysis of the proposed algorithm and heuristic algorithms based on other priority attributes: Speedup, Efficiency, Scheduling Length Ratio, Load Balancing, and Cost. [ABSTRACT FROM AUTHOR]

Task scheduling in parallel computing is represented by a directed acyclic graph, and it is called multiprocessor task scheduling. The primary objective of task scheduling algorithm is to minimize the total execution time i.e. scheduling length. There are various types of task scheduling algorithms: Bound Number of Processor Scheduling, and Arbitrary Processor Network Scheduling Algorithms. Bound Number of Processor Scheduling algorithms are used for limited number of processors; since it does not use task duplication, link contention, and routing strategy for communication. But Arbitrary Processor Network scheduling algorithms are considered for link contention routing strategy in communication. It uses arbitrary network topology. In this paper, we have studied different Arbitrary Processor Network scheduling algorithms: Mapping Heuristic, Dynamic Level Scheduling, Bottom Up, and Bubble Scheduling and Allocation. The primary objective of this survey paper is to explain various Arbitrary Processor Network scheduling algorithms and their functionality, priority attributes, and time complexity. The performance metrics can also be used for comparison of different Arbitrary Processor Network scheduling algorithms. [ABSTRACT FROM AUTHOR]

Task scheduling in the multiprocessor environment is considered to be a NP-complete problem. It is also called a multiprocessor scheduling. Here, a parallel program is divided into a number of subtasks and it is mapped on the processors for concurrent execution. The objective of task scheduling is to reduce scheduling length of a given application program. Considering that task scheduling is represented by Directed Acyclic Grape (DAG). There are two types of task scheduling algorithms: static task scheduling and dynamic task scheduling algorithms. In this paper, we propose a new task scheduling algorithm based on minimum distance and an entry task duplicated on the processors. The minimum distance computes between an entry task and its successors. It includes, minimum distance of parent task, execution time and communication time Here , also considering a priority list (PL) attribute that contains tasks information of a given application program whose distance is minimum from an entry task. Finally, we will do comparative study of the proposed algorithm with Bounded Number of Processors (BNP) class of scheduling algorithms. It has done based on the following matrices: Scheduling Length, Speedup, Efficiency, Load Balancing and Normalized Scheduling Length (NSL). It showed proposed algorithm gives better result than BNP class of scheduling algorithms. [ABSTRACT FROM AUTHOR]