In this paper, several works has been presented related to the clustering parallel computing for distributed system. The trend of the paper is to focus on the strength points of previous works in this field towards enhancing performance of the distributed systems. This concentration conducted via presenting several techniques where each of them has the weak and strong features. The most challenging points for all techniques vary from increasing the performance of the system to time responding to overcome overhead running of the system. For more specific addressing concurrent computation besides parallel computing classifications for distributed systems, this paper depended comprehensive features study and comparison between SYNC and ASYNC Modes.

/pdf/a-state-of-art-survey-for-concurrent-computation-and-3zrs48vlvs.pdf

A State of Art Survey for Concurrent Computation and Clustering of Parallel Computing for Distributed Systems

The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloud-fog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption. 

/pdf/multi-objective-grey-wolf-optimizer-algorithm-for-task-3ppq5pax.pdf

Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

Presently, cloud computing has become a very popular platform in the computing world. It provides users with high efficient resources on demand. Nevertheless, due to the strong turnout to use the cloud in everything in the Information Technology (IT) field, cloud platforms have become very crowded with heavy loads. Therefore, providers need to use new techniques to manage resources allocation to users. One of the most important techniques, in managing cloud resources, is scheduling technique. Recently, many heuristic and meta-heuristic techniques are developed to solve the scheduling problem. However, each technique is efficient to solve a part of the problem but unable to solve the overall problem. This paper presents a new technique called Water Pressure Change Optimization (WPCO) to solve the scheduling problem in cloud computing. The new WPCO technique is inspired from the phenomenon of water density changing when increasing the pressure due to the changing in the physical characteristics of the water. The new technique is evaluated and compared with the most recent existing techniques. The results indicate that the WPCO can distribute any number of tasks on the available resources in low time complexity. In addition, it improves schedule length, load balancing, resources utilization, memory usage and throughput of the cloud system.

A novel water pressure change optimization technique for solving scheduling problem in cloud computing

Article history: Received January 25 2021 Received in Revised Format March 17 2021 Accepted March 21 2021 Available online March, 21 2021 Optimizing the trade-off between crucial decisions has been a prominent issue to help decisionmakers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a nondominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria. © 2021 by the authors; licensee Growing Science, Canada

/pdf/two-meta-heuristic-algorithms-for-optimizing-a-multi-5cef6p0mzl.pdf

Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds

Task scheduling plays a vital role in distributed computing. It enhances the performance of the system as it minimizes the overall execution time and reduces overhead problems like communication delay by allocating suitable task to appropriate processor. Different scheduling techniques are discussed in this paper which are employed for task scheduling. Taxonomy of hierarchical classification is discussed for concurrent system and further several task scheduling algorithms are described on the basis of dependency and approach used such as static or dynamic for low throughput and efficient performance.

A Survey on Scheduling Algorithms for Parallel and Distributed Systems

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A Survey on Scheduling Algorithms for Parallel and Distributed Systems