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.

A Novel Optimization Approach for Energy-Efficient Multiple Workflow Scheduling in Cloud Environment

A Novel Optimization Approach for Energy-Efficient Multiple Workflow Scheduling in Cloud Environment

An power and bound-aware optimised scheduler for virtualized cloud computing

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task’s quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm’s exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance.

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.

Hybrid Simulated Annealing and Spotted Hyena Optimization Algorithm-Based Resource Management and Scheduling in Cloud Environment

Hybrid Simulated Annealing and Spotted Hyena Optimization Algorithm-Based Resource Management and Scheduling in Cloud Environment

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.

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.

A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud

Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm that optimizes the makespan, service cost, and load balance in the cloud. The proposed HGWOCPSO hybrid algorithm employs GWO and Constriction factor based PSO (CPSO) for the workflow optimization. The algorithm is simulated on Workflowsim, where a set of scientific workflows with varying task sizes and inter-task communication requirements are executed on a cloud platform. The simulation results show that the proposed algorithm outperforms existing algorithms in terms of makespan, service cost, and load balance. The employed GWO algorithm mitigates the problem of local optima that is inherent in PSO algorithm.

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

An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

SummaryEfficient utilization of resources is a challenging task in cloud computing due to heterogeneity of resources and fluctuating resource demands of multifarious applications. This article presents an autonomic resource allocation approach for energy efficient utilization of resources while satisfying QoS needs of the end‐users. The proposed approach is based on antlion optimization and makes use of resource utilization history for autonomic allocation and efficient use of resources. It can operate in one of the three different modes namely, energy‐saving, performance, and balanced mode depending on the projected workload of an application. The performance analysis of the proposed approach is carried out in CloudSim simulator under different workload conditions.

HGPSO: An efficient scientific workflow scheduling in cloud environment using a hybrid optimization algorithm

Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups.

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.

A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm

In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment.