Optimal resource scheduling for SMEs using cloud in manufacturing sectors

2021 ◽  
Author(s):  
N.C. Brintha ◽  
J.T. Winowlin Jappes ◽  
M. Adam Khan ◽  
A. Ajithram
Keyword(s):  
Resource Scheduling ◽  
Manufacturing Sectors ◽  
Optimal Resource

scholarly journals Delay-Optimal Resource Scheduling of Energy Harvesting-Based Devices

2019 ◽  
Vol 3 (4) ◽  
pp. 1023-1034
Author(s):  
Ibrahim Fawaz ◽  
Mireille Sarkiss ◽  
Philippe Ciblat
Keyword(s):  
Energy Harvesting ◽  
Resource Scheduling ◽  
Optimal Resource

scholarly journals Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

2020 ◽  
Vol 7 (3) ◽  
pp. 1572-1581 ◽  
Author(s):  
Libo Jiao ◽  
Yulei Wu ◽  
Jiaqing Dong ◽  
Zexun Jiang
Keyword(s):  
Internet Of Things ◽  
Resource Scheduling ◽  
Imperfect Csi ◽  
Optimal Resource

scholarly journals Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

2017 ◽  
Vol 9 (1-3) ◽  
Author(s):  
Syed Hamid Hussain Madni ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi’i Muhammad Abdulhamid
Keyword(s):  
Cloud Computing ◽  
Search Algorithm ◽  
Resource Scheduling ◽  
Cuckoo Search ◽  
Computing System ◽  
Cuckoo Search Algorithm ◽  
Cloud Computing System ◽  
Optimal Resource ◽  
System Resource ◽  
Np Hard Problem

Effective resource scheduling is essential for the overall performance of cloud computing system. Resource scheduling problem in IaaS cloud computing is investigated in this paper. It is established to be an NP-hard problem. A recently developed Cuckoo Search (CS) meta-heuristic algorithm is proposed in this paper, to minimize the response time, makespan and throughput for the resource scheduling in IaaS cloud computing. Simulation results show that CS algorithm outperforms that of Ant Colony Optimization (ACO) algorithm based on the considered parameters. 

Resource Scheduling Techniques in Utility Computing

2016 ◽  
pp. 1159-1179
Author(s):  
Inderveer Chana ◽  
Tarandeep Kaur
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Resource Scheduling ◽  
Utility Computing ◽  
On Demand ◽  
Geographically Dispersed ◽  
Optimal Resource ◽  
Dynamic Resource ◽  
Characteristic Features

Utility Computing offers on-demand services from a shared pool of resources and can be envisaged to be a benchmark in the IT development. The capability to provide on-demand services involves management of large number of resources that are geographically dispersed and thus poses a number of resource management and scheduling challenges in the domain of resource heterogeneity, dynamic resource locations and load balancing. Proficient resource allocations and efficient scheduling helps in achieving optimal resource utilization and hence enhances the performance of the system. This paper evaluates existing resource management systems, listing their key characteristic features and highlighting the factors that make the existing systems excel upon each other. It also discusses various resource scheduling techniques currently available and characterizes the techniques based on Quality of Service (QoS) parameters supported by them along with the classification on basis of their operating environment and further extends towards load balancing and energy efficiency support if available.

Best Scheduling Plan Choice Based on Mathematical Model

2013 ◽  
Vol 457-458 ◽  
pp. 1550-1553
Author(s):  
Yan Min Zheng ◽  
Jin Feng Zhang ◽  
Qing Zhou Liu
Keyword(s):  
Mathematical Model ◽  
Resource Scheduling ◽  
Decision Makers ◽  
Information Collection ◽  
Scheduling Scheme ◽  
Plan Choice ◽  
Optimal Resource ◽  
Scheduling Plan ◽  
Emergency Decision ◽  
Future Demand

As information collection change with the events development in the situations of the emergency, a mathematical model is established on the basis of it, the emergency decision-makers take different measures at different times of emergencies is analyzed in detail to get the future demand for emergency resources information , then the optimal resource scheduling scheme was arrived. Finally, the method of how to use the model to generate the best scheduling plans is given by a specific example.

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