The High Performance of a Task Scheduling Algorithm Using Reference Queues for Cloud- Computing Data Centers

Chen, Chin-Ling; Chiang, Mao-Lun; Lin, Chuan-Bi

doi:10.3390/electronics9020371

Cited by 7 publications

(4 citation statements)

References 21 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cloud node contains massive storage and processors with high-speed network connectivity and various application services [18][19][20]. For assigning services to suitable service nodes with appropriate distribution of workload in every node, Chen et al [21] proposed RQCSA and FSQSM, which improved the efficiency and minimized queue waiting time and makespan. Behzad et al [22] proposed the queue based hybrid scheduling algorithm for storing jobs in the queue according to the order of priority.…”

Section: Related Workmentioning

confidence: 99%

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

et al. 2022

View full text Add to dashboard Cite

Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

et al. 2022

View full text Add to dashboard Cite

show abstract

“…According to NIST, cloud-computing suppliers offer their resources according to three standard models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) (Kofahi & Alsmadi, 2018), (Pandey & Farik, 2015). Figure 1 (Wikipedia, n.d.), (Chen, et al, 2020), portrayed these models. These models need not be related.…”

Section: Cloud Computing Service Modelsmentioning

confidence: 99%

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

2022

IKM

View full text Add to dashboard Cite

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.

show abstract

“…The extensive results were compared between different workflow scheduling methods. Chen et al [7] resolved the unbalancing problems in cloud based multi-object tasks functionalities with resource constrain domains. The authors developed Reference Queue based Cloud Service Architecture (RQCSA) for proving an effective solution for resource allocations in the dynamic cloud resource environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Tharani,

Kalpana

2021

View full text Add to dashboard Cite

Swarm Optimization (PSO), Genetic Algorithm (GA) for resolving the problems during the workflow scheduling process. The makespan time of the workflow scheduling process of these methods is high and hence the accuracy level of the scheduling functions will not be optimum. Hence, this research work develops the hybrid classification approach which combines both Machine Learning (ML) and Deep Learning (DL) methods for the effective workflow scheduling process in different cloud environments. This proposed method can function in two modes as user end and remote end. In user end, ML algorithms are used, and the DL algorithms are used in remote end at cloud system.The main research goals of this work are: • To develop a cloud-based system used for both workflow computing and scheduling to minimize the Makespan (MS) and Execution Cost (EC) in cloud environments for healthcare applications using hybrid ML and DL approach. • To identify the cancer region in the classified abnormal images using threshold method.

show abstract

The High Performance of a Task Scheduling Algorithm Using Reference Queues for Cloud- Computing Data Centers

Cited by 7 publications

References 21 publications

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Bulletin of the Polish Academy of Sciences: Technical Sciences

Contact Info

Product

Resources

About