Task Scheduling In Cloud Computing Research Articles

Crow Search Algorithm (CSA) for Task-Scheduling in Cloud Computing

Crow Search Algorithm (CSA) for Task-Scheduling in Cloud Computing

Multiobjective Hybrid Al-Biruni Earth Namib Beetle Optimization and Deep Learning based Task Scheduling in Cloud Computing

With the rapid development of computing networks, cloud computing (CC) enables the deployment of large-scale applications and meets the increased rate of computational demands. Moreover, task scheduling is an essential process in CC. The tasks must be effectually scheduled across the Virtual Machines (VMs) to increase resource usage and diminish the makespan. In this paper, the multi-objective optimization called Al-Biruni Earth Namib Beetle Optimization (BENBO) with the Bidirectional-Long Short-Term Memory (Bi-LSTM) named as BENBO+ Bi-LSTM is developed for Task scheduling. The user task is subjected to the multi-objective BENBO, in which parameters like makespan, computational cost, reliability, and predicted energy are used to schedule the task. Simultaneously, the user task is fed to Bi-LSTM-based task scheduling, in which the VM parameters like average computation cost, Earliest Starting Time (EST), task priority, and Earliest Finishing Time (EFT) as well as the task parameters like bandwidth and memory capacity are utilized to schedule the task. Moreover, the task scheduling outcomes from the multi-objective BENBO and Bi-LSTM are fused for obtaining the final scheduling with less makespan and resource usage. Moreover, the predicted energy, resource utilization and makespan are considered to validate the BENBO+ Bi-LSTM-based task scheduling, which offered the optimal values of 0.669J, 0.535 and 0.381.

Task scheduling in cloud computing based on grey wolf optimization with a new encoding mechanism

Task scheduling in the cloud computing still remains challenging in terms of performance. Several evolutionary-derived algorithms have been proposed to solve or alleviate this problem. However, evolutionary algorithms have good exploration ability, but the performance drops significantly in high dimensions. To address this issue, considering the characteristic of task scheduling in cloud computing (i.e. all task-VM mappings are 1-dimensional and have the same search range), we propose a task scheduling algorithm based on grey wolf optimization using a new encoding mechanism (GWOEM) in this work. Through this new encoding mechanism, greedy and evolutionary algorithms are rationally integrated in GWOEM. Besides, based on the new mechanism, the dimension of search space is reduced to 1 and the key parameter (i.e., the population size) is eliminated. We apply the proposed GWOEM to the Google Cloud Jobs dataset (GoCJ) and demonstrate better performance than the prior state of the art in terms of makespan.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimise resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimise the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimizaion(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

Integral-Valued Pythagorean Fuzzy-Set-Based Dyna Q+ Framework for Task Scheduling in Cloud Computing.

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. Task scheduling is a nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task scheduling decisions. The primary rationale for selecting these uncertainty parameters lies in the challenge of accurately measuring their values, as empirical estimations often diverge from the actual values. The integral-valued Pythagorean fuzzy set (IVPFS) is a promising mathematical framework to deal with parametric uncertainties. The Dyna Q+ algorithm is the updated form of the Dyna Q agent designed specifically for dynamic computing environments by providing bonus rewards to non-exploited states. In this paper, the Dyna Q+ agent is enriched with the IVPFS mathematical framework to make intelligent task scheduling decisions. The performance of the proposed IVPFS Dyna Q+ task scheduler is tested using the CloudSim 3.3 simulator. The execution time is reduced by 90%, the makespan time is also reduced by 90%, the operation cost is below 50%, and the resource utilization rate is improved by 95%, all of these parameters meeting the desired standards or expectations. The results are also further validated using an expected value analysis methodology that confirms the good performance of the task scheduler. A better balance between exploration and exploitation through rigorous action-based learning is achieved by the Dyna Q+ agent.

Optimization of Task Scheduling in Cloud Computing Using the Sine Cosine Algorithm

Optimization of Task Scheduling in Cloud Computing Using the Sine Cosine Algorithm

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: a hybrid artificial intelligence approach

Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach

Enhancement in performance of cloud computing task scheduling using optimization strategies

Enhancement in performance of cloud computing task scheduling using optimization strategies

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the performance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the other two algorithms

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Enhanced Harris Hawks Optimization Algorithm for SLA-Aware Task Scheduling in Cloud Computing

Enhanced Harris Hawks Optimization Algorithm for SLA-Aware Task Scheduling in Cloud Computing

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.

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

Cloud Computing: Optimization using Particle Swarm Optimization to Improve Performance of Cloud

INTRODUCTION: In the contemporary world cloud computing is acknowledged as advanced technology to manage and store huge amount of data over the network. To handle the network traffic and effective task scheduling some efficient load balancing algorithm should be implemented. This can reduce the network traffic and overcome the problem of limited bandwidth. The various research articles represents ample amount of optimization techniques to overcome the transfer of data with limited bandwidth. Among all, few solutions has been chosen for current research article such as – optimization of load distribution of various resources provided by cloud.
 OBJECTIVES: In this paper, Comparative analysis of various task scheduling algorithms such as (FCFS, SJF, Round Robin & PSO) have been proposed in current research article to accumulate the outcome and evaluate the overall performance of cloud at different number of processing elements (pesNumber) .
 METHODS: Overall performance of task scheduling is significantly enhanced by PSO Algorithm implemented on cloud in comparison of FCFS, SJF and Round Robin. Outcomes of optimization technique has been implemented and tested over the CloudSim simulator.
 RESULTS: The comparative analysis conducted based on scalability for increasing the number of processing elements over the cloud. The major insight of proposed algorithm has shows that results are still better when number of VMs is increased and it successfully minimizes waiting time and turnaround time and completion time by 43% which is significantly high than outcomes of existing research articles.
 CONCLUSION: To optimize the task scheduling in cloud computing, comparative analysis of various task scheduling algorithms has been proposed, including Particle Swarm Optimization algorithm.

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Optimized Deep Learning Schemes for Secured Resource Allocation and Task Scheduling in Cloud Computing - A Survey

Scheduling involves allocating shared resources gradually so that tasks can be completed within a predetermined time frame. In Task Scheduling (TS) and Resource Allocation (RA), the phrase is used independently for tasks and resources. Scheduling is widely used for Cloud Computing (CC), computer science, and operational management. Effective scheduling ensures that systems operate efficiently, decisions are made effectively, resources are used efficiently, costs are kept to a minimum, and productivity is increased. High energy consumption, lower CPU utilization, time consumption, and low robustness are the most frequent problems in TS and RA in CC. In this survey, RA and TS based on deep learning (DL) and machine learning (ML) were discussed. Additionally, look into the methods employed by DL-based RA and TS-based CC. Additionally, the benefits, drawbacks, advantages, disadvantages, and merits are explored. The work's primary contribution is an analysis and assessment of DL-based RA and TS methodologies that pinpoint problems with cloud computing.