Energy Aware Grid Scheduling for Dependent Task Using Genetic Algorithm

2016 ◽  
Vol 7 (2) ◽  
pp. 18-36
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Job Scheduling ◽  
Computing System ◽  
Computational Grid ◽  
Task Execution ◽  
Energy Aware ◽  
Optimal Energy ◽  
Proposed Model ◽  
Dynamic Voltage

Consumption of energy in the large computing system is an important issue not only because energy sources are depleting fast but also due to the deteriorating environmental conditions. A computational grid is a large heterogeneous distributed computing platform which consumes enormous energy in the task execution. Energy-aware job scheduling, in the computational grid, is an important issue that has been addressed in this work. If the tasks are properly scheduled, keeping the optimal energy concern, it is possible to save the energy consumed by the system in the task execution. The prime objective, in this work, is to schedule the dependent tasks of a job, on the grid nodes with optimal energy consumption. Energy consumption is estimated with the help of Dynamic Voltage Frequency Scaling (DVFS). Makespan, while optimizing the energy consumption, is also taken care of in the proposed model. GA is applied for the purpose and therefore the model is named as Energy Aware Genetic Algorithm (EAGA). Performance evaluation of the proposed model is done using GridSim simulator. A comparative study with other existing models viz. min-min and max-min proves the efficacy of the proposed model.

A Genetic Algorithm for Energy Aware Task Scheduling in Heterogeneous Systems

2005 ◽  
Vol 15 (04) ◽  
pp. 439-449 ◽  
Author(s):  
Man Lin ◽  
Sai Man Ng
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Heterogeneous Systems ◽  
Task Mapping ◽  
Energy Aware ◽  
Heterogeneous Processors ◽  
Comparison Results ◽  
Dynamic Voltage ◽  
System Configurations ◽  
Dependent Tasks

In distributed systems, an application can be decomposed to tasks which can be executed on different processors in parallel. Modern processors allow variable supply voltages and dynamic voltage scaling (DVS) provides the possibility to reduce the power consumption. In this paper, we present a static scheduling approach to integrate task mapping, scheduling and voltage selection to minimize energy consumption of real-time dependent tasks executing on a number of heterogeneous processors. The approach is based on Genetic Algorithms. The simulation results show that the proposed algorithm is very effective and reduces the energy consumption ranging from 20% to 90% under different system configurations. We also compare the proposed genetic-algorithm-based energy aware algorithm with other three algorithms, namely earliest-deadline-first-based, longest-time-first-based and simulated-annealing-based energy aware algorithms. The comparison results demonstrate that the genetic-algorithm-based energy aware algorithm outperforms other three algorithms.

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

Energy Optimization Heuristics for Budget-Constrained Workflow in Heterogeneous Computing System

2019 ◽  
Vol 28 (09) ◽  
pp. 1950159 ◽  
Author(s):  
Junqiang Jiang ◽  
Wenbin Li ◽  
Li Pan ◽  
Bo Yang ◽  
Xin Peng
Keyword(s):  
Energy Consumption ◽  
Heterogeneous Computing ◽  
Rapid Development ◽  
Energy Optimization ◽  
Minimum Cost ◽  
Computing System ◽  
Directed Acyclic Graphs ◽  
Optimization Heuristics ◽  
Dynamic Voltage ◽  
Heterogeneous Computing System

With the rapid development of commercialized computation, the heterogeneous computing system (HCS) has evolved into a new method of service provisioning based on utility computing models, in which the users consume services and resources based on their quality of service requirements. In certain models using the pay-as-you-go concept, the users are charged for accessed services based on their usage. In addition, the commercialized HCS provider also assumes the responsibility to reduce the energy consumption to protect the environment. This paper considers a basic model known as directed acyclic graphs (DAG), which is designed for workflow applications, and investigates heuristics that allows the scheduling of various tasks of a workflow into the dynamic voltage and frequency scaling enabled HCS. The proposed approaches, which are Minimum-Cost-Up-to-Budget (MCUB) and Maximum-Cost-Down-to-Budget (MCDB), could not only satisfy budget constrains but could also optimize overall energy consumption. The approaches along with their variants are implemented and evaluated using four types of basic DAGs. From the experimental results, we conclude that MCDB outperforms MCUB in energy optimization and makespan criterion while meeting budget constraints faced by users.

scholarly journals Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

2021 ◽  
Author(s):  
Poria Pirozmand ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Maedeh Farrokhzad ◽  
Mehdi Sadeghilalimi ◽  
Seyedsaeid Mirkamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Scheduling Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Computing System ◽  
Computing Systems ◽  
Cloud Computing System ◽  
Scheduling Heuristic ◽  
The Cost

AbstractThe cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

scholarly journals Analysis of finite-source cluster networks

2016 ◽  
Vol 25 (2) ◽  
pp. 223-235
Author(s):  
ADAM TOTH ◽  
◽  
TAMAS BERCZES ◽  
ATTILA KUKI ◽  
BELA ALMASI ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Job Scheduling ◽  
Computational Grid ◽  
Main Interest ◽  
Finite Source ◽  
Cluster Networks ◽  
Computational Cluster ◽  
Commercial Off The Shelf ◽  
Infinite Model

Nowadays the distributed heterogeneous resources of networks, like the computational grid, start to have a greater part of interest so, the investigations of such systems are vital. Because of the more efficient utilisation of the resources, the job scheduling becomes more challenging for the system administrators. The allocation of the arriving jobs has a great impact on the efficiency and the energy consumption of the system. In this paper, we present a finite source generalized model for the performance evaluation of scheduling compute-intensive jobs based on the infinite model of Tien Van Do. The available computers are classified into three groups. This classification is based on two aspects: high performance priority (HP) and energy efficiency priority (EE). We investigate three schemes (separate queue, class queue and common queue) for buffering the jobs in a computational cluster that is built from Commercial Off-The-Shelf (COTS) servers. Our main interest is to calculate performance measures and energy consumption of the system using the different buffering schemes and classifications.

GA Based Scheduling Model for Computational Grid to Minimize Turnaround Time

2009 ◽  
Vol 1 (4) ◽  
pp. 70-90 ◽  
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Hard Problem ◽  
Simulation Studies ◽  
Scheduling Model ◽  
Np Hard Problem

Scheduling on distributed systems is an NP hard problem and grid being a wide heterogeneous expandable system makes scheduling even a tougher job. Genetic algorithm, based on natural selection and evolution has gained popularity in recent times because of its effectiveness in handling optimization problems. In this article, a job-scheduling model for a computational grid with the objective of minimizing the turnaround time using genetic algorithm is proposed. The model evaluates various clusters in the grid to find the most suitable one with minimum turnaround time for the job-scheduling. Simulation studies compare the performance of this model with other similar models.

Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters

2021 ◽  
pp. 1-22
Author(s):  
Golnaz Berenjian ◽  
Homayun Motameni ◽  
Mehdi Golsorkhtabaramiri ◽  
Ali Ebrahimnejad
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Service Providers ◽  
Service Level Agreement ◽  
Main Idea ◽  
Service Level ◽  
Second Phase ◽  
Energy Aware ◽  
Slack Time ◽  
Dynamic Voltage

Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 1-24
Author(s):  
Kuljeet Kaur ◽  
Sahil Garg ◽  
Georges Kaddoum ◽  
Neeraj Kumar
Keyword(s):  
Energy Consumption ◽  
Job Scheduling ◽  
Service Level Agreement ◽  
Cyber Physical Systems ◽  
Time Data ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Physical Systems ◽  
Cloud Data ◽  
Energy Aware Scheduling

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

scholarly journals Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Sonia Yassa ◽  
Rachid Chelouah ◽  
Hubert Kadima ◽  
Bertrand Granado
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Heterogeneous Computing ◽  
Pso Algorithm ◽  
Workflow Scheduling ◽  
Energy Aware ◽  
Multi Objective ◽  
Traditional Research ◽  
Dynamic Voltage

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

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