Scheduling in distributed systems: A cloud computing perspective

Bittencourt, Luiz F.; Goldman, Alfredo; Madeira, Edmundo Roberto Mauro; Fonseca, Nelson L. S. da; Sakellariou, Rizos

doi:10.1016/j.cosrev.2018.08.002

Cited by 81 publications

(48 citation statements)

References 165 publications

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“…e IaaS cloud is a very common model from the perspective of resource management; thus, scheduling in such systems has gained great attention especially from the research community [67]. e IaaS cloud model provides computing resources as virtual resources that are made accessible to consumers through the Internet [68,69].…”

Section: Model and Problem Formulationmentioning

confidence: 99%

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

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

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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.

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Section: Model and Problem Formulationmentioning

confidence: 99%

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

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

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“…This decision is based on the objective function. One of the most employed objective functions in modern cloud systems is task completion time, or makespan [97] that should be minimized. Some of the other objective functions that are usually taken for research, as well as for the practical purposes include: minimization of the transfer and communication time, maximization of the throughput, maximization of the load balancing and resource utilization, and minimization of the computing costs and energy consumption.…”

Section: Cloud Task Scheduling Problem Formulationmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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“…One of the software and methodological implementations used for the processes of spatial and geospatial modeling of structure, interconnections and dynamics in hydro and oceanology are geographic information systems (GIS) [5]. Using the model of distributed or cloud computing using an agent-based approach used to process Big Data will allow you to make the transition to a qualitatively new knowledge, optimize the processes occurring in large-scale scientific research, and solve the problem of analysis and representative visualization of measurement slices in multidimensional databases [6][7][8], to analyze the cyclical nature of changes in ecological and geological changes, while using statistics for more than 100 years of environmental monitoring.…”

Section: Review Of Literary Sourcesmentioning

confidence: 99%

Intellectual technology of detection of anomalies in the aquatoria ecosystems of the Sevastopol on the basis of data clustering

2019

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The main features associated with the development of intelligent technology for detecting anomalies of ecosystems in the waters of the city of Sevastopol are considered. An approach is proposed, the feature of which is to ensure continuous monitoring of key environmental indicators presented in the form of heterogeneous information flows: hydrometeorological information, data on the level of pollution and air composition, soil, environmental monitoring, monitoring of maximum permissible emissions of harmfulsubstances in order to detect changes in the state of data flow monitoring. The proposed method for thedetection of anomalies of ecosystems of the water area is based on data clustering. We consider typicaloperations on clusters and main metrics based on the Kullback information measure.

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Scheduling in distributed systems: A cloud computing perspective

Cited by 81 publications

References 165 publications

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

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

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Intellectual technology of detection of anomalies in the aquatoria ecosystems of the Sevastopol on the basis of data clustering

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