Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with En cur = 1.2En min (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.
Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN) that is based on gravitational search algorithm (GSA). In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.
The Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of
Energy management is considered the major concern in cloud computing, which supports the rapid growth of data centers and computing centers; therefore, energy and load balancing have become crucial issues in cloud data centers. To address this issue, the present paper proposed a two-phase energyaware load balancing (EALB) scheduling algorithm using the virtual machine migration through the Particle Swarm Optimization (PSO) algorithm to be applicable to dynamic voltage frequency scaling-enabled cloud data centers, which is called EALBPSO. In the first phase, an objective function was employed to deactivate a large number of physical machines in order to reduce energy consumption. The main idea of the algorithm was to maximize load balancing in the second phase, in which the remaining virtual and physical machines were used as the PSO inputs, and an objective function was also defined to distribute the load appropriately among the physical machines. In addition, a dataset was developed to test different parameters and scenarios with the aim of assessing the effectiveness of the proposed EALBPSO algorithm in comparison with other algorithms already proposed in the literature for similar purposes. The experimental results demonstrated that the proposed algorithm was capable of saving up to 0.896%, 9.716%, and 10.8% energy compared with the MDPSO algorithm, Kumar et al.'s algorithm, and Dahsti and Rahmani algorithm, respectively, and also it showed 5.91%, 16%, and 16.267% improvements for the number of virtual machines migrations, and 3.867%, 8.623%, and 6.953% improvements for the deviation of processors, all compared with their competitors stated above, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.