Comparative Analysis of Various Task Scheduling Algorithms in Cloud Environment

Mallik, Puspanjali; Nayak, Ajit Kumar; Dalei, Rabindra Kumar

doi:10.1109/ocit53463.2021.00019

Cited by 1 publication

(4 citation statements)

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“…The task scheduling process in cloud computing is depicted in Figure 5 [19]. The data center broker, who serves as an intermediary between the user and the data center, determines how the requested users submit their tasks to the data center.…”

Section: Task Scheduling Architecturementioning

confidence: 99%

“…The various task scheduling algorithms used in cloud computing are depicted in Figure 6 [19]. In cloud computing, different task scheduling methods, including Round Robin (RR), First Come First Serve (FCFS), Max-Min, Min-Min, Particle Swam Optimization (PSO), Ant Colony Optimization (ACO), and Intelligent Water Drop Cloud Scheduling (IWDC) are used.…”

Section: Classification Of Task Scheduling Algorithmsmentioning

confidence: 99%

“…PSO [19] PSO is a meta-heuristic algorithm that considers both computation task and data transmission time. It is used for workflow applications by varying its computation and communication costs.…”

Section: Rr [19]mentioning

confidence: 99%

“…ACO [19] The ant foraging behavior is replicated in ACO. When ants are looking for food, they leave pheromones along the way so that other ants can easily follow their trail or locate the quickest path.…”

Section: Rr [19]mentioning

confidence: 99%

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Cloud-based Cost Effective IIoT Model Towards Industry 5.0

Shinge

Shrawankar

2023

Research Reports on Computer Science

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Companies across industries increasingly depend upon cloud computing to manage their Industrial Internet of Things (IIoT) technology. Machines are connected over a network in the IIoT. Cloud computing plays an essential role by connecting people, devices, work processes, and buildings to deliver cloud services in industries. But cloud computing faces a problem with task scheduling, high latency delay, and memory management, affecting the overall cost of industries using cloud services. A major concern in the cloud computing field is task scheduling which is essential for achieving cost-effective execution and improving resource usage. It refers to assigning available resources to user tasks. This problem can be solved effectively by improving task execution and increasing the use of resources. The waiting time between a client’s sent request and a cloud service provider to give a response, known as latency, is another issue in cloud environments. In cloud computing, this delay can be significantly higher. As a result, users of various cloud services may incur increased expenses due to this delay. Finally, among the most significant topics in cloud computing is efficient memory management, which handles integrated data and optimizes memory management algorithms. This paper proposes a cloud model for IIoT, which provides task scheduling, helps reduce latency, and optimizes memory management. This proposed model helps to reduce the cost of using cloud computing in IIoT.

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Section: Task Scheduling Architecturementioning

confidence: 99%

Section: Classification Of Task Scheduling Algorithmsmentioning

confidence: 99%

Section: Rr [19]mentioning

confidence: 99%

Section: Rr [19]mentioning

confidence: 99%

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