Optimization of Database Resources Assignment in Cloud Computing Using Gravity Search Algorithm

2018 ◽  
Author(s):  
Mahmoudreza Tahmassebpour
Keyword(s):  
Cloud Computing ◽  
Search Algorithm

scholarly journals Enhanced Crow Search Algorithm for Task Scheduling in Cloud Computing

2021 ◽  
Vol 1055 (1) ◽  
pp. 012102
Author(s):  
K R Prasanna Kumar ◽  
K Kousalya ◽  
S Vishnuppriya ◽  
S Ponni ◽  
K Logeswaran
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Search Algorithm

Context-Awareness in Opportunistic Mobile Cloud Computing

2015 ◽  
pp. 203-237
Author(s):  
Radu-Corneliu Marin ◽  
Radu-Ioan Ciobanu ◽  
Radu Pasea ◽  
Vlad Barosan ◽  
Mihail Costea ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Context Awareness ◽  
Mobile Cloud Computing ◽  
Search Algorithm ◽  
Power Saving ◽  
Mobile Network ◽  
Handheld Devices ◽  
Mobile Cloud ◽  
Mobile Code ◽  
Battery Technology

Smartphones have shaped the mobile computing community. Unfortunately, their power consumption overreaches the limits of current battery technology. Most solutions for energy efficiency turn towards offloading code from the mobile device into the cloud. Although mobile cloud computing inherits all the Cloud Computing advantages, it does not treat user mobility, the lack of connectivity, or the high cost of mobile network traffic. In this chapter, the authors introduce mobile-to-mobile contextual offloading, a novel collaboration concept for handheld devices that takes advantage of an adaptive contextual search algorithm for scheduling mobile code execution over smartphone communities, based on predicting the availability and mobility of nearby devices. They present the HYCCUPS framework, which implements the contextual offloading model in an on-the-fly opportunistic hybrid computing cloud. The authors emulate HYCCUPS based on real user traces and prove that it maximizes power saving, minimizes overall execution time, and preserves user experience.

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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. 

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