Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Kantale, Vidya

doi:10.30534/ijatcse/2020/88922020

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…They checked all the algorithms in various tasks and under various patterns of the virtual machine ( VM) in the study. According to the report, machine learning-based algorithms work better than others in terms of general load balancing performance [36].…”

Section: Related Workmentioning

confidence: 99%

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

2020

IJATCSE

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Many commercial facilities and academic institutions now use cloud computing in an attempt of adopting new digital innovations. Cloud Computing Providers might be constrained to some services, lacking several resources their customers requested, meaning that distinct cloud services need to come together in collaboration to interoperate and exchange resources among themselves. In different characteristics and structures, clouds may be interlinked, and the interconnected systems may be prone to instability or intrusion. Although digital transformation and cloud services are also making progress in meeting the influencing policies internationally, terrorists use virtual space to commit cyberattacks. Adoption of cloud services has become highly sensitive to attacks and intrusions. Security breach or corruption of data gives organizations or agencies significant catastrophic losses. Divine influence and physical devices really aren't sufficient to protect services provided by clouds; thus, there is a need for an effective cyber protection model that is implementable, versatile, reliable and capable of detecting hazardous cyber-attacks on joined heterogeneous cloud providers, making it essential to decrease in real-time. This paper focuses on developing a Statistical Model for cybercrime detection in a heterogeneous cloud systems that are joined. When defining the implementation of the proposed model, we used an architecture and design modelling system. We used statistical notations and diagrams to contain the complexities and variability of the joining cloud data centres when adopting shared resources and possible cybercrime detection. The proposed model was experimented using WEKA tool to test Anomaly and Normal Accuracy performance of three (3) Classification existing algorithms: C4.5 and Random Decision tree and NaïveBayes. The dataset used was CICDDOS2019, the dataset was generated observing abstract behaviour of 25 users based on heterogeneous HTTP, HTTPS, FTP, SSH, and email protocols, Operating Systems and initiating different DDOS attacks. After a comparison of Classification model results, yielding in the highest accuracy and detection rates as well as the lowest false rates. The results specify that the classification capability of the C4.5 is inherently superior to the classification of Random Tree and the NaïveBayes.

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Section: Related Workmentioning

confidence: 99%

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

2020

IJATCSE

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“…Recently, studies have found evidence that inefficient task scheduling in cloud computing can negatively impact performance efficiency and drastically increase resource use [5]. The suboptimal allocation of tasks using traditional scheduling strategies such as First Come First Served (FCFS), Round Robin (RR) and Shortest Job First (SJF) has proven insufficient for dynamic cloud environments [6].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Task Scheduling in Cloud Computing Using Discrete Tuna Swarm Optimization

Ledmi,

Souidi

et al. 2024

ISI

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Task scheduling in cloud computing represents a pivotal challenge, necessitating the efficient allocation of computing tasks to available resources. This challenge is crucial in diverse sectors such as e-commerce, e-learning, and e-health, and is compounded by the heterogeneity of tasks and resources, fluctuating demands, and the need to optimize multiple objectives like Makespan, resource utilization, and throughput. In the quest to resolve these complexities, meta-heuristic algorithms inspired by natural phenomena have gained prominence. Among them, the Tuna Swarm Optimization (TSO) algorithm stands out for its proficient ability to navigate and exploit the search space effectively. This paper introduces a novel algorithm, the Discrete Tuna Swarm Optimization for Task Scheduling (DTSO-TS), derived from the TSO algorithm. DTSO-TS algorithm's goal is to efficiently distribute tasks among virtual machines, balance workloads and improve resource utilization to minimize Makespan while increasing throughput. A fitness function provides optimal solutions to this goal. Creates a swarm before evaluating and refining solutions which have proven their worth. By contrasting it with well-known scheduling algorithms such as Ant-Colony-Based, Particle Swarm Optimisation, Genetic Algorithm, First Come First Serve, Round Robin, and Shortest Job First, we may evaluate DTSO-TS's effectiveness. According to the comparison results, DTSO-TS is the best option for scheduling tasks in cloud computing contexts.

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“…As well, research works are widespread, where the formation of schedules is carried out on the basis of graph theory [9], genetic [10] and heuristic algorithms [11] with their subsequent implementation in software. However, existing computer scheduling programs based on existing models, focused on the conditions of specific production or have excessive versatility of the implementation of common functions [8], [12]- [15] and high market value and cannot be adapted to solve the problem of forces coordination during the operation.…”

Section: Introductionmentioning

confidence: 99%

Method of the Effort Coordination Chart Creation

2020

IJATCSE

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The search for new approaches and the development of tools based on them to support the commander decision to organize the interaction of forces in the course of modern combat operations, which are characterized by rapid changes in the situation, has recently been the subject of intensive research. One of the promising areas of such research is the development of special software used to form a chart of the forces' efforts coordination during the joint implementation of their combat missions.Research into the development of such programs has led to new requirements for the formation of these charts. First, the chart should take into account not only data of the operational and tactical situation, but also its changes in the real time mode. Secondly, during the formation of the efforts coordination chart in the software there should be a knowledge base about its organization, which is formed and constantly updated on the basis of previous experience of conducting operations. For the software development that will be able to form a highquality form and content of the coordination chart a formalized mathematical apparatus is required. The article describes one of the approaches to formalizing the process of forming a chart of Ground Based Air Defense efforts with Air Defense Aircraft coordination, which is based on the theory of schedules and the method of a multicriteria problem decomposition.

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Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Cited by 3 publications

References 8 publications

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

Optimizing Task Scheduling in Cloud Computing Using Discrete Tuna Swarm Optimization

Method of the Effort Coordination Chart Creation

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