Genetic Algorithm For Task Scheduling Research Articles

Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm

Cloud computing is now dominant in high-performance distributed computing, offering resource polling and ondemand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.

An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

Hybrid approach based on cuckoo optimization algorithm and genetic algorithm for task scheduling

One of the most important issues in designing efficient scheduling algorithms in heterogeneous distribution systems is the reduction of execution time. In the proposed algorithm, the modified operators of the cuckoo optimization algorithm and the genetic algorithm are used to achieve a relatively optimal solution with fewer repetitions of the genetic algorithm and less execution time than the cuckoo optimization algorithm. The most important innovation in the proposed algorithm is the introduction of a new operator called spiral search, which increases the variety among the samples produced in each generation. The main idea of this operator is to replace linear search with the spiral search, which allows local search between similar schedules and accelerates the achievement of a relatively optimal answer. Also the multi objective function in the proposed algorithm is used to minimize makespan and maximize parallelization. The results obtained from the proposed algorithm on a large number of standard graphs with a various range of attributes show that it is superior to the other task scheduling algorithms.

Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO.

DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

Hybrid genetic algorithm for task scheduling in distributed real-time system

A distributed real-time system consists of a set of heterogeneous processors located at possibly different sites and connected by a communication link. Task scheduling in distributed real-time system attracts the attention of the researcher in many disciplines. Many researchers have been developed the solution of task scheduling problem by using different types of technique. In this paper, a hybrid genetic algorithm (HGA) is developed which is a combination of k-means and genetic algorithm to form the clusters of tasks in an effort to minimise the communication costs. A genetic algorithm is also developed to schedule the formed clusters of tasks onto set of processors to minimise the execution costs. The results of the algorithm have been compared with various existing techniques. Experiment results shows that the proposed algorithm achieves better efficiency than other existing techniques.

Hybrid genetic algorithm for task scheduling in distributed real-time system

Hybrid genetic algorithm for task scheduling in distributed real-time system

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.

Energy aware multi objective genetic algorithm for task scheduling in cloud computing

In recent years, the utilisation of cloud is expanded to all the fields. Cloud computing has the advantage to provide anything as a service to the clients. One of the major issues in cloud computing is energy consumption at the time of task allocation to the virtual machines. This paper address the problem of task scheduling by considering the energy consumption of tasks in the cloud computing. Existing research works only focused on optimising the time and cost of the resource without paying attention to the energy consumption. This paper proposes a new algorithm for multi objective task scheduling in clouds, and develop a genetic algorithm to optimise the energy consumption. Simulation results prove the efficiency of the proposed method.

Energy aware multi objective genetic algorithm for task scheduling in cloud computing

In recent years, the utilisation of cloud is expanded to all the fields. Cloud computing has the advantage to provide anything as a service to the clients. One of the major issues in cloud computing is energy consumption at the time of task allocation to the virtual machines. This paper address the problem of task scheduling by considering the energy consumption of tasks in the cloud computing. Existing research works only focused on optimising the time and cost of the resource without paying attention to the energy consumption. This paper proposes a new algorithm for multi objective task scheduling in clouds, and develop a genetic algorithm to optimise the energy consumption. Simulation results prove the efficiency of the proposed method.

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

SummaryWith the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.

An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing

Abstract Cloud computing is a new platform to manage and provide services on the internet. Lately, researchers have paid attention a lot to this new subject. One of the reasons to have high performance in a cloud environment is the task scheduling. Since the task scheduling is an NP-Complete problem, in many cases, meta-heuristics scheduling algorithms are used. In this paper to optimize the task scheduling solutions, a powerful and improved genetic algorithm is proposed. The proposed algorithm uses the advantages of evolutionary genetic algorithm along with heuristic approaches. For analyzing the correctness of the proposed algorithm, we have presented a behavioral modeling approach based on model checking techniques. Then, the expected specifications of the proposed algorithm is extracted in the form of Linear Temporal Logic (LTL) formulas. To achieve the best performance in verification of the proposed algorithm, we use the Labeled Transition System (LTS) method. Also, the proposed behavioral models are verified using NuSMV and PAT model checkers. Then, the correctness of the proposed algorithm is analyzed according to the verification results in terms of some expected specifications, reachability, fairness, and deadlock-free. The simulation and statistical results revealed that the proposed algorithm outperformed the makespans of the three well-known heuristic algorithms and also the execution time of our recently meta-heuristics algorithm.

Genetic Algorithm for Task Scheduling on Distributed Heterogeneous Computing System

Distributed heterogeneous computing system are increasingly being employed for critical applications, such as aircraft control, industrial process control, and banking systems. Efficient application scheduling is a key issue for achieving high performance in this system. The problem is generally addressed in terms of task scheduling, where the tasks are the schedulable units of a program. The task scheduling problem has been extensively studied and a large number of scheduling heuristics have been presented in the literature. In this paper we propose a new task-scheduling algorithm namely, Genetic Algorithm for Task Scheduling (GATS) on heterogeneous computing system, which provides optimal results for applications represented by directed acyclic graph. The performance of the algorithm is illustrated by comparing the schedule length, speedup, and efficiency with existing algorithms such as CPOP, HEFT and PSGA. The comparison study based on randomly generated graphs and graphs of three real world applications such as Gaussian Elimination Algorithm, Fast Fourier Transformation, and Gauss Jordan algorithm shows that GATS algorithm substantially outperforms existing algorithms.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues

On parallel and distributed heterogeneous computing systems, a heuristic-based task scheduling algorithm typically consists of two phases: task prioritization and processor selection. In a heuristic based task scheduling algorithm, different prioritization will produce different makespan on a heterogeneous computing system. Therefore, a good scheduling algorithm should be able to efficiently assign a priority to each subtask depending on the resources needed to minimize makespan. In this paper, a task scheduling scheme on heterogeneous computing systems using a multiple priority queues genetic algorithm (MPQGA) is proposed. The basic idea of our approach is to exploit the advantages of both evolutionary-based and heuristic-based algorithms while avoiding their drawbacks. The proposedalgorithm incorporates a genetic algorithm (GA) approach to assign a priority to each subtask while using a heuristic-based earliest finish time (EFT) approach to search for a solution for the task-to-processor mapping. The MPQGA method also designs crossover, mutation, and fitness function suitable for the scenario of directed acyclic graph (DAG) scheduling. The experimental results for large-sized problems from a large set of randomly generated graphs as well as graphs of real-world problems with various characteristics show that the proposed MPQGA algorithm outperforms two non-evolutionary heuristics and a random search method in terms of schedule quality.

A Genetic Algorithm for Task Scheduling on NoC Using FDH Cross Efficiency

A CrosFDH-GA algorithm is proposed for the task scheduling problem on the NoC-based MPSoC regarding the multicriterion optimization. First of all, four common criterions, namely, makespan, data routing energy, average link load, and workload balance, are extracted from the task scheduling problem on NoC and are used to construct the DEA DMU model. Then the FDH analysis is applied to the problem, and a FDH cross efficiency formulation is derived for evaluating the relative advantage among schedule solutions. Finally, we introduce the DEA approach to the genetic algorithm and propose a CrosFDH-GA scheduling algorithm to find the most efficient schedule solution for a given scheduling problem. The simulation results show that our FDH cross efficiency formulation effectively evaluates the performance of schedule solutions. By conducting comparative simulations, our CrosFDH-GA proposal produces more metrics-balanced schedule solution than other multicriterion algorithms.

An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment

Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

Modified Genetic Algorithm for Task Scheduling in Homogeneous Parallel System Using Heuristics

Modified Genetic Algorithm for Task Scheduling in Homogeneous Parallel System Using Heuristics