Task Scheduling Algorithms in Cloud Computing: A Review

Ibrahim, Ibrahim Mahmood

doi:10.17762/turcomat.v12i4.612

Cited by 99 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In cloud computing, the data processed by users is stored on the network rather than on the local computer. Cloud service providers should effectively operate the data center so that the scheduling of resource tasks can meet the needs of customers [12]. Through the network, users can quickly and conveniently access data and services at any location in an on-demand and easy-to-expand way, so as to achieve the goal of enjoying high-performance computing and application services with low-configuration devices.…”

Section: Figure 2 Cloud Computing Technologymentioning

confidence: 99%

Departure Audit of Natural Ecological Environment Resource Assets Based on Cloud Computing

2023

NEP

View full text Add to dashboard Cite

With the promotion of the strategy of ecological civilization, the off-duty audit system of natural ecological environment resources assets has achieved further development. However, under the influence of information technology, the complexity of ecological environment resources in the audit process has been increasing, which has not only hindered the processing and completion of data acquisition, resource asset measurement and other work, but also had a negative impact on the audit quality and efficiency. In order to solve this dilemma and realize the efficient and orderly implementation of audit work, based on the analysis of the definition, content and development status of the audit of natural ecological environment resources assets, this paper carried out an effective research on the optimization of audit work in combination with cloud computing technology. In order to verify the effectiveness of cloud computing technology, this paper took an audit case of an audit office as an empirical analysis object to study its audit efficiency. The analysis results showed that under the specified work cycle, the average completion rate of audit work after the application of cloud computing technology was about 93.57%, while the average completion rate of audit work before the application of cloud computing technology was 74.85%. From the concrete results of the empirical analysis, it is feasible to audit the natural ecological environment resource assets leaving office based on cloud computing.

show abstract

Section: Figure 2 Cloud Computing Technologymentioning

confidence: 99%

Departure Audit of Natural Ecological Environment Resource Assets Based on Cloud Computing

2023

NEP

View full text Add to dashboard Cite

show abstract

“…In fact, by utilizing technological developments such as scheduling management applications, the process of mapping class schedules can be carried out effectively and efficiently. Many methods have been used in scheduling management applications for course scheduling [3][4][5][6][7][8][9][10][11][12][13] , job shop scheduling [14,15] , cloud environments scheduling [16,17] , and healthcare scheduling [18] . Scheduling management applications have become increasingly important in our fast-paced world.…”

Section: Introductionmentioning

confidence: 99%

User-Centered Design-Based Approach in Scheduling Management Application Design and Development

Herumurti

Bimantara

Supriana

2023

JTS

View full text Add to dashboard Cite

The process of manually making and setting course schedules using Microsoft Excel is ineffective, time-consuming, and still prone to errors. This research develops a website-based scheduling management application with a case study at SMK Pariwisata Margarana so that it can solve scheduling problems manually. The User-Centered Design (UCD) method is applied in the application prototype design stage.Open interviews, field observations, simulations, and questionnaires were used as research data collection methods. Three iterations were carried out at the prototype design stage to fulfill all user needs. The high-fidelity prototype in the last iteration is then implemented into an application. Application quality is measured using ISO/IEC 25010 with five characteristics. The test results on usability characteristics show that the scheduling management application obtains an average usability score of 91.2%. The appropriateness recognizability sub-characteristic obtained the highest usability score of 93.53%. UCD can help produce an application that can meet all the user's needs when implemented in the application design phase.

show abstract

“…Multi-objective optimization of cloud scheduling is a process of mapping tasks to appropriate resources of cloud computing considering multiple scheduling metrics.The multi-objective cloud task scheduling optimization in cloud environment needs to select the appropriate scheduling metrics from the actual requirements,and the scheduling metrics can be selected one or more,and then each metric is weighted to constitute the scheduling objective function. Research on cloud task scheduling methods [1] first emerged with traditional scheduling methods such as minimum completion time based,first come first served,polling scheduling,greedy policy scheduling,Min-Min scheduling,and Max-Min scheduling.However,traditional algorithms can only optimize a single objective and ignore other objectives,leading to increased scheduling cost on other objectives.With the rise of heuristic algorithms [2],researchers have introduced biologically inspired genetic algorithms [3],lion algorithms [4],imperial competition algorithms [5],etc.and population intelligence-based ant colony algorithms [6],particle swarm algorithms [7],cat swarm algorithms [8],fish swarm algorithms [9],etc.into cloud computing scheduling,and the idea of such algorithms is to solve multi-objective optimization problems by giving an optimal approximate solution based on multi-objective optimization under satisfying the constraints.However,in the face of increasingly complex cloud computing environments that require dynamic adjustments,they lack the flexibility to make timely adjustments to the current state of the cloud environment,and are prone to fall into local optimal solutions in the process of multi-objective solving,thus failing to obtain the global optimal solution.To address these problems,this paper applies deep reinforcement learning algorithms to cloud computing scheduling to explore the optimal scheduling strategy.The powerful feature extraction capability of deep learning combined with reinforcement learning is used to find the optimal strategy for multiobjective optimization of cloud task scheduling through the mechanism of reward feedback and continuous trial and error.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of cloud task scheduling based on improved deep Q-learning algorithm

Guan

Dong

et al. 2023

Preprint

View full text Add to dashboard Cite

With the rapid development of cloud computing technology, although cloud computing can provide flexible resources and pay on demand mode, the problem of high energy consumption is still an urgent problem for cloud service providers to optimize. The cloud task scheduling problem of how to reduce operational costs while improving the quality of cloud services has become a research focus of cloud computing technology. In general, researchers will reduce power consumption, optimize load balancing,improve response speed and other aspects of cloud task scheduling algorithm for multi-objective optimization. At present, there are many optimization algorithms, such as traditional scheduling algorithms and heuristic algorithms, but these algorithms are unable to cope with the dynamic online complex cloud environment, resulting in the failure of multi-objective optimization or the high cost and poor effect of multi-objective optimization. However,deep reinforcement learning shows a better effect on multi-objective optimization of cloud task scheduling. Deep reinforcement learning offers new research ideas for cloud task scheduling. In this paper, based on the D3QN algorithm and noise network, NoisyD3QN algorithm is improved,which increases the exploration ability of reinforcement learning model and increases the upper limit of performance. Meanwhile, the NoisyD3QN algorithm-based cloud task scheduling model is constructed using the power consumption model proposed by FAN for simulation experiments. Comparing the performance of the eight algorithms on different optimization targets, the experiments show that the proposed algorithm effectively reduces the cluster power consumption, improves response speed, reduces user waiting time, and optimizes load balancing, Significantly reduces the average standard deviation of cluster CPU utilization.

show abstract

Task Scheduling Algorithms in Cloud Computing: A Review

Cited by 99 publications

References 38 publications

Departure Audit of Natural Ecological Environment Resource Assets Based on Cloud Computing

Departure Audit of Natural Ecological Environment Resource Assets Based on Cloud Computing

User-Centered Design-Based Approach in Scheduling Management Application Design and Development

Multi-objective optimization of cloud task scheduling based on improved deep Q-learning algorithm

Contact Info

Product

Resources

About