Job Scheduling In Cloud Research Articles

A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling

A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling

Energy-aware multi-objective job scheduling in cloud computing environment with water wave optimisation

Energy-aware multi-objective job scheduling in cloud computing environment with water wave optimisation

Energy-aware multi-objective job scheduling in cloud computing environment with water wave optimisation

Energy-aware multi-objective job scheduling in cloud computing environment with water wave optimisation

Optimizing Cloud-Fog-Edge Job Scheduling Using Catastrophic Genetic Algorithm and Block Chain-Based Trust: A Collaborative Approach

Collaborative edge-cloud features improve job scheduling. Cloud job scheduling is crucial. Pending delay completion. A cloud-edge mixed system replaced centralized cloud computing. Combining resource levels reduces terminal user service call latency. Decentralization, regionalization, and node dispersal autonomy increase ambiguity, unreliability, and instability. This paper will plan cloud-migrating tasks on edge devices or the cloud to achieve a global optimum. The objective of this research is to enhance the efficiency of job scheduling in cloud-fog edge environments through the integration of the Catastrophic Genetic Algorithm (CGA), a genetic algorithm inspired by natural evolution. Additionally, Berger's theory will be employed to develop a trust-enabled interaction framework based on blockchain technology. The CGA fitness function incorporates load balancing and reasonability in the coordination of services and scheduling of tasks, with the goal of maximizing performance. This article presents proposed improvements to the CGA, which involve the incorporation of mutation and crossover operators, roulette selection, and cataclysm. These changes aim to expand the search area and potentially discover schedules that are more optimal. The approach also effectively deals with the problem of premature convergence, guaranteeing ample time for the algorithm to comprehensively explore the solution space prior to reaching a final solution. The experimental findings indicate that the strategy put forward in this study yields a substantial reduction in task completion time, surpassing 97%. Furthermore, it effectively addresses the best local problem, hence showcasing competing options.

Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing

A growing number of services, accessible and usable by individuals and businesses on a pay-as-you-go basis, are being made available via cloud computing platforms. The business services paradigm in cloud computing encounters several quality of service (QoS) challenges, such as flow time, makespan time, reliability, and delay. To overcome these obstacles, we first designed a resource management framework for cloud computing systems. This framework elucidates the methodology of resource management in the context of cloud job scheduling. Then, we study the impact of a Virtual Machine’s (VM’s) physical resources on the consistency with which cloud services are executed. After that, we developed a priority-based fair scheduling (PBFS) algorithm to schedule jobs so that they have access to the required resources at optimal times. The algorithm has been devised utilizing three key characteristics, namely CPU time, arrival time, and job length. For optimal scheduling of cloud jobs, we also devised a backfilling technique called Earliest Gap Shortest Job First (EG-SJF), which prioritizes filling in schedule gaps in a specific order. The simulation was carried out with the help of the CloudSim framework. Finally, we compare our proposed PBFS algorithm to LJF, FCFS, and MAX–MIN and find that it achieves better results in terms of overall delay, makespan time, and flow time.

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

A theoretical and empirical study of job scheduling in cloud computing environments: the weighted completion time minimization problem with capacitated parallel machines

A theoretical and empirical study of job scheduling in cloud computing environments: the weighted completion time minimization problem with capacitated parallel machines

SG-PBFS: Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment

Job scheduling in cloud computing plays a crucial role in optimizing resource utilization and ensuring efficient job allocation. But cloud resources may be wasted, or service performance may suffer because of under-utilization or over-utilization because of poor scheduling. Existing approaches often overlook the dynamic nature of cloud environments, resulting in issues like missed deadlines, prolonged flowtime, extended makespan, and unacceptable total tardiness. To address this issue, the main objective of this research is to improve the existing Priority Rules (PR) cloud schedulers by developing a new dynamic scheduling algorithm by manipulating the gaps in the cloud job schedule. Firstly, a Priority-Based Fair Scheduling (PBFS) algorithm has been introduced to schedule jobs so that jobs can access the required resources at optimal times. Then, a backfilling strategy called Shortest Gap - Priority-Based Fair Scheduling (SG-PBFS) is developed that attempts to manipulate the gaps in the schedule of cloud jobs. Finally, the performance evaluation demonstrates that the proposed SG-PBFS algorithm outperforms SG-SJF, SG-LJF, SG-FCFS, SG-EDF, and SG-(MAX-MIN) regarding flow time, makespan time, and total tardiness, which conclusively demonstrates its effectiveness. To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language.

Deep Adversarial Imitation Reinforcement Learning for QoS-Aware Cloud Job Scheduling

Although cloud computing is one of the promising technologies for online business services, how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging current techniques. With the advancing of machine learning, deep reinforcement learning (DRL) has demonstrated its outstanding capability in dispatching time-sensitive tasks. However, the reinforced rewards in DRL are typically unavailable until the completion of the scheduling for all the jobs. Considering the fact that the trajectory of jobs in cloud is always long, current DRL-based solutions will meet challenges in finding the trajectories with high rewards, and thus would have issues such that the finally trained scheduling policy is suboptimal. To improve the problem, in this article, we propose a more advanced approach called a deep adversarial imitation reinforcement learning (AIRL) framework for scheduling time-sensitive cloud jobs. Specifically, we focus on scheduling user requests in a way to maximize job successful rate along with a significant reduction on job response time. We present the detailed design of our method, and our experimental results demonstrate that AIRL can generally outperform the existing cloud job scheduling approaches, including the DRL-based method, in the presence of different real-time workload and computing resource configurations.

AI assisted multi-objective cloud computing model for optimized task scheduling and enhanced QOS

In recent decades, cloud computing has gained popularity due to the extensive collection of autonomous systems with a flexible framework and diversified features. Various communities require a cloud computing paradigm to maximize their revenue due to its commercial reality. Scheduling of resources to the cloud consumers dramatically influences the cost-benefit of the service providers. Several kinds of research have already been made, focusing on task scheduling and resource utilization. Job shop scheduling is one of the strong NP-hard problem for the production of optimal scheduling strategies. Evolutionary algorithms such as genetic algorithm and tabu search have been emerged to perform optimal job scheduling in cloud computing environments, but that are confined to perform a single objective. Hence to meet the multiple objectives in cloud computing platforms, we proposed a novel artificial intelligence-based task scheduling strategy to facilitate minimum makespan, energy efficiency, reduced computational cost, and reliability. The proposed modified sheep flock heredity algorithm (MSFHA) facilitates the optimal task scheduling strategy by selecting the job schedules with the Longest job to the High-speed processor (LJHP), Smallest job to the High-speed processor (SJHP), and high-affinity values.

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

Chemical reaction optimization (CRO) for cloud job scheduling

Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy ( $$PE$$ ) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms.

An Adaptive Computational Model for Threshold Based VM Migration and Job Scheduling in Cloud

An Adaptive Computational Model for Threshold Based VM Migration and Job Scheduling in Cloud

Efficient job scheduling in cloud computing based on genetic algorithm

Scheduling in cloud is one of the challenging issues in resource management topic where the main question is how to manage time and cost in an optimised way. This study tackles the mentioned proble...

Efficient job scheduling in cloud computing based on genetic algorithm

Scheduling in cloud is one of the challenging issues in resource management topic where the main question is how to manage time and cost in an optimised way. This study tackles the mentioned problem by managing time and cost through a genetic-based algorithm. The primary goal of this study is to manage jobs in a shorter time with lower cost and higher utilisation. Toward that end, we leverage the genetic algorithm solutions and a new model is proposed where jobs are created in genetic format. In the evaluation part of the model, different scenarios based on taking different fitness functions and format of the population are considered. We have analysed makespan, cost and utilisation in comparison to other two existing scheduling models (MAX-MIN and MIN-MIN). The results show considerable improvement in the cost, makespan and utilisation.

Cloud computing and big data: Technologies and applications

Cloud computing and big data: Technologies and applications

A hybrid framework for Job Scheduling on Cloud using Firefly and BAT algorithm

Nowadays cloud computing is an emerging field, requires more algorithm and techniques for the various process of cloud computing. Here, we have considered the job scheduling process in cloud computing platform that needs a good algorithm to schedule the jobs requested from various users of cloud computing environment. Here, the request can be from any platform so scheduling is indispensable one when a number of users need the particular jobs. In this research, we have intended to develop a hybrid algorithm for job scheduling in cloud computing environment. Accordingly, multiple criteria will be taken for scheduling various jobs located in various servers. Then, the job scheduling will be done based on a hybrid optimisation algorithm. Additionally, different jobs with different constraints will be considered and the cloud computing environment is simulated with the help of cloudsim tool.

A fuzzy energy and security aware scheduling in cloud

The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

A new approach for scheduling jobs in cloud computing environment

Abstract. Job dscheduling in cloud computing environment is one of the most important issues that must be considered by cloud computing service providers. Optimal job scheduling enables more efficient utilization of resources, which in turn leads to more customers satisfaction. Solution procedures to the problem of job scheduling in cloud computing environment have mainly focused on optimizing one quality criterion. In this paper, we propose a static solution procedure for the scheduling of jobs in cloud computing environment which is based on particle swarm optimization technique (PSO). Considering the virtual machine capabilities and having secured an appropriate method for request assignments, this solution procedure not only reduces the amount of memory needed, but minimizes the maximum job's makespan. The simulation results show that our proposed method reduces the maximum job makespan by a larger amount when compared to the other PSO based methods.

Erratum to: FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Erratum to: FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method