Task Scheduling In Cloud Environment Research Articles

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

AbstractTask scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. GIJA integrates the principles of the Geyser‐inspired algorithm with the Jaya algorithm, augmented by a Levy Flight mechanism, to address the complexities of task scheduling optimization. The motivation for this research stems from the increasing demand for efficient resource utilization and task management in cloud computing, driven by the proliferation of Internet of Things (IoT) devices and the growing reliance on cloud‐based services. Traditional task scheduling algorithms often face challenges in handling dynamic workloads, heterogeneous resources, and varying performance objectives, necessitating innovative optimization techniques. GIJA leverages the eruptive dynamics of geysers, inspired by nature's efficiency in channeling resources, to guide task scheduling decisions. By combining this Geyser‐inspired approach with the simplicity and effectiveness of the Jaya algorithm, GIJA offers a robust optimization framework capable of adapting to diverse cloud computing environments. Additionally, the integration of the Levy Flight mechanism introduces stochasticity into the optimization process, enabling the exploration of solution spaces and accelerating convergence. To evaluate the efficacy of GIJA, extensive experiments are conducted using synthetic and real‐world datasets representative of cloud computing workloads. Comparative analyses against existing task scheduling algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, and GIAA, demonstrate the superior performance of GIJA in terms of solution quality, convergence rate, diversity, and robustness. The findings of GIJA provide a promising solution quality for addressing the complexities of task scheduling in cloud environments (95%), with implications for enhancing system performance, scalability, and resource utilization.

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.

Task Scheduling in a Cloud Environment: A comparative Study

In this study, we explore the application of Monarch Butterfly Optimization (MBO) algorithms for task scheduling in cloud computing, comparing its performance against widely used optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).Task scheduling in the cloud is a critical aspect influencing resource utilization, turnaround time, and overall system efficiency. MBO, known for its effective exploration- exploitation balance, is examined for its suitability in addressing the complexities of cloud computing environments. The study investigates MBO's advantages, such as enhanced adaptability to dynamic conditions, effective handling of multi-objective optimization, and its consideration of bandwidth as a critical resource. Comparative analyses with ACO and PSO highlight MBO's superior performance in achieving near-optimal task schedules, emphasizing its potential to offer innovative solutions to the challenges posed by task scheduling in dynamic and resource-constrained cloud environments. This research contributes valuable insights into the strengths of MBO, paving the way for advancements in optimization methodologies tailored for cloud computing systems.

Optimizing Task Scheduling in Cloud Computing: An Enhanced Shortest Job First Algorithm

In the dynamic landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing overall system performance. This research introduces a groundbreaking approach to task scheduling in cloud environments through the implementation of a novel Modified Shortest Job First (SJF) algorithm within the CloudSim simulation framework. The primary objectives of this study are to address existing challenges in traditional scheduling algorithms, mitigate resource bottlenecks, reduce task completion times, and improve overall system efficiency.

Bivariate Correlative Modest Adaptive Boost Resource Aware Task Scheduling in Cloud Environment

Cloud computing is a rapidly evolving paradigm that provides accessible and virtualized resources through Internet technology. In this model, Cloud Service Providers (CSPs) offer online access to computing resources for users to execute their application tasks. Task scheduling in cloud computing involves the allocation of computational tasks to available resources within the cloud environment. The primary objectives of task scheduling are to optimize resource utilization, minimize task completion time, and enhance overall system performance. Task scheduling plays a vital role in cloud resource management as it directly impacts the efficiency of cloud data centers. With the increasing number of cloud computing users, task scheduling has become more challenging, requiring the use of suitable scheduling algorithms. To improve task scheduling efficiency, a novel method called Bivariate Correlative Modest Adaptive Boost Resource Optimized Task Scheduling (BCMABROTS) is developed for efficient service provisioning in the cloud environment. The BCMABROTS method begins by collecting the number of incoming user-requested tasks. After that, the resources of virtual machines, such as energy, bandwidth, memory, and CPU, are measured. The ensemble classifier constructs the set of weak learners as the nearest prototype centroid classifier. The weak classifier uses Bivariate Correlation to classify the resource-optimal virtual machine based on the available resources. The results of the weak learners are combined to provide strong classification results. Once the optimal virtual machine is categorized, the task is scheduled for that particular virtual machine by the task assigner. This approach ensures efficient cloud service provisioning with minimal time consumption. Experimental evaluation is carried out to assess factors such as task scheduling efficiency, makespan, throughput and average response time in relation to the number of cloud-requested tasks. The observed performance results confirm that the BCMABROTS method improves the task scheduling efficiency, throughput and minimizes the makespan as well as the average response time than the conventional machine learning methods.

Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.

A Novel Hybrid MSA-CSA Algorithm for Cloud Computing Task Scheduling Problems

Recently, the dynamic distribution of resources and task scheduling has played a critical role in cloud computing to achieve maximum storage and performance. The allocation of computational tasks in the cloud is a complicated process that can be affected by some factors, such as available network bandwidth, makespan, and cost considerations. However, these allocations are always non-symmetric. Therefore, it is crucial to optimize available bandwidth for efficient cloud computing task scheduling. In this research, a novel swarm-based task scheduling with a security approach is proposed to optimize the distribution of tasks using available resources and encode cloud information during task scheduling. It can combine the Moth Swarm Algorithm (MSA) with the Chameleon Swarm Algorithm (CSA) for the task scheduling process and utilizes the Polymorphic Advanced Encryption Standard (P-AES) for information security of cloud scheduled tasks. The approach offers a new perspective for utilizing swarm intelligence algorithms to optimize cloud task scheduling. The integration of MSA and CSA with P-AES enables the approach to provide efficient and secure task scheduling by exploiting the strengths of used algorithms. The study evaluates the performance of the proposed approach in terms of the degree of imbalance, makespan, resource utilization, cost, average waiting time, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using a wide range of tasks from 1000 to 5000. The results show that the approach provides an innovative solution to the challenges of task scheduling in cloud environments and improves the performance of cloud services in terms of effectiveness and security measures.

Research on Sparrow Search Optimization Algorithm for multi-objective task scheduling in cloud computing environment

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Improved Task Scheduling for Cloud Computing Using Combination of Genetic Algorithm and Tabu Search Algorithm

Abstract: Cloud computing has revolutionized the way computational resources are provisioned and utilized. However, effective task scheduling in cloud environments remains a critical challenge due to the complexity of managing resources and meeting quality of service (QoS) requirements. In this paper, we address the multi-objective task scheduling problem by proposing a novel scheme that combines a hybrid algorithm, HGTSA, to achieve improved convergence rates and overall efficiency. Our main contributions include the development of a fitness function that considers makespan, system capacity, and resource utilization, as well as the implementation of the HGTSA algorithm. Through extensive simulations, we demonstrate the superiority of our proposed scheme, outperforming conventional genetic algorithms and achieving better makespan times. Furthermore, we highlight the future scope of research in leveraging machine learning techniques to further enhance task scheduling processes and optimize decision-making based on workload predictions and historical data.

Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment

Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment

Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes

Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

Adaptive DRL-Based Task Scheduling for Energy-Efficient Cloud Computing

Intelligent task scheduling solutions are highly demanded in the operation of complex cloud data centers so that resources can be utilized in an energy-efficient way while still ensuring various requirements of users. However, the energy problem of task scheduling in cloud environment becomes more challenging with the ever-increasing number of users as well as the constant and unpredictable change of workloads. In this research, we propose an Adaptive Deep Reinforcement Learning-based (ADRL) task scheduling framework for energy-efficient cloud computing. We first present a Change Detection algorithm to detect whether the workload has changed greatly. On this basis, we built an Automatic Generation network to adjust the discount factor of Deep Reinforcement Learning (DRL) dynamically according to the changing workload, which enables faster and more accurate learning. We finally introduce the adaptive DRL to learn the optimal policy of dispatching arriving user requests with the reward aiming to minimize task response time and maximize resource utilization. Simulated experiments have confirmed that the proposed scheduling scheme performs well on accelerating learning convergence and promoting allocation accuracy, thus it is very effective in reducing the average response time of tasks and increasing the CPU utilization rate of resources, which eventually makes the cloud system more energy efficient.

Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments

For cloud environments, task scheduling focusing on the optimal completion time (makespan) is vital. Metaheuristic approaches can be used to produce efficient solutions that will provide important cost savings to both the cloud service provider and the clients. On the other hand, since there is a high probability of getting stuck in local minima in metaheuristic solutions due to the type of problem, it may not always be possible to quickly reach the optimal solution. This study, using a metaheuristic approach, proposes a solution based on the Cloneable Jellyfish Algorithm for optimal task distribution in cloud environments. The unique feature of the proposed algorithm is that it allows dynamic population growth to be carried out in a controlled manner in order not to get stuck in local minima during the exploration phase. In addition, this algorithm, which uses a different cloning mechanism so that similar candidates are not generated in the population growth, has made it possible to achieve the optimal solution in a shorter time. To observe the solution performance, cloud environment simulations created in the Cloudsim simulator have been used. In experiments, the success of the proposed solution compared to classical scheduling algorithms has been proven.

Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines.

Improved red fox optimizer with fuzzy theory and game theory for task scheduling in cloud environment

The tasks scheduling problem is about mapping tasks to resources so that the objectives related to both users and service providers are satisfied. In this paper, three improvements are presented to enhance the ability of the Red Fox Optimization (RFO) algorithm. Firstly, a Quasi-Opposition Based Learning method is employed for generating the initial population and a Levy flight method is used to enhance the exploration ability of newly generated foxes. Secondly, two fuzzy control systems are used to provide a balance between exploration and exploitation activities. Thirdly, a cornu-spiral movement is considered to enhance the local search capability. In addition, an efficient task scheduling using the Fuzzy Improved RFO (FIRFO) algorithm and game theory named EGFIRFO is presented considering four conflicting objectives (i.e., resource utilization, load balancing, makespan, and execution time). As a global optimization, the proposed method is compared with Bat algorithm (BA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Antlion Optimizer (ALO), and Red Fox Optimizer (RFO) in terms of a set of qualitative parameters (i.e., convergence curve, trajectory, average fitness, and search history). The experimental results indicated that the proposed algorithm could improve the fitness value by 24.18%, 12.81%, 15.65%, 33.49%, and 14.73% compared with the BA, GWO, PSO, ALO, and RFO, respectively. As a scheduler, EGFIRFO is compared to three scheduling algorithms (i.e., MSDE, EMVO, and HGSWC).

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

A New Black Widow Algorithm for Discontinuous Optimization in Cloud Task Environment

Task scheduling in cloud environment has received extensive attention due to its complex characteristics, and most of the previous methods ignore the discontinuity problem of workflow scheduling. Therefore, a new black widow optimization algorithm (NBWO) is proposed. NBWO proposed a new reproductive strategy to select excellent male individuals and female individuals to obtain better offspring. In NBWO, the number of female individuals is fixed, and males obtain mating rights through competition; NBWO improves the mutation strategy, which makes the exploration ability and development ability of the algorithm more balanced. Applying NBWO to cloud computing task scheduling, the experimental results show that NBWO has excellent performance in dealing with discontinuous optimization problems.

Efficient task scheduling in cloud environment

SummaryCloud computing is the provision of on‐demand computing resources over the internet and on a pay‐as‐you‐go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p‐EDF) and Non‐Preemptive EDF (np‐EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de‐merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time.

Fisher linear discriminant and discrete global swarm based task scheduling in cloud environment

Fisher linear discriminant and discrete global swarm based task scheduling in cloud environment

A Novel Hybrid Optimization Model for Task Scheduling in Cloud Environment

A Novel Hybrid Optimization Model for Task Scheduling in Cloud Environment