Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

Mahmoud, Hadeer; Thabet, Mostafa; Khafagy, Mohamed Helmy; Omara, Fatma A.

doi:10.1109/access.2022.3163273

Cited by 38 publications

(42 citation statements)

References 36 publications

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“…The transactive operational framework in optimizing IDC operations and energy utilization in geo-distributed markets. [48] Multiobjective task scheduling approach in the cloud environment using the Decision Tree algorithm. It optimizes task allocation.…”

Section: Ref No Methodology Novelty Resultsmentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the Load Balancing and Energy-Efficient Migration Model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a Deep Reinforcement Learning-based Iterative-learning Contextual Side chaining Model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means Clustering and Fuzzy Logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ Bidirectional Long Short-Term Memory networks with Recurrent Graph Neural Network, for accurate workload prediction and informed migration decision Making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This Load Balancing and Energy-Efficient Migration Model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

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Section: Ref No Methodology Novelty Resultsmentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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“…In [10], a novel Task Scheduling-Decision Tree (TS-DT) algorithm for assigning and implementing application's task was presented. Also an elaborate comparative study was provided and validation results found improvement not only in terms of makespan but also resulted in the improvement of resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

George

Anoop

2023

IJRITCC

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Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.

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“…Before responding to the early warning signal, it is necessary to first transmit the parameters, positions and other data in the detection area to the microcontroller, and then send an alarm by the buzzer after A/D conversion. In practice, the short-time geological radar velocimeter we designed is developed to measure the velocity, thickness and other information of the surrounding environment at different times near some unknown strata [7][8].…”

Section: Demand For Short-time Approaching Forecast and Early Warning...mentioning

confidence: 99%

Lightning Warning Methods Based on Machine Learning and Single Station Ground Meteorological Elements

2021

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Lightning is a natural phenomenon in nature, and its occurrence rules and effects are very complex. The analysis of meteorological elements plays an extremely important guiding role in lightning prediction. Therefore, this paper uses the method of machine learning to carry out early warning analysis on lightning approaching, in order to obtain weather information in advance and take action to avoid danger. In this paper, the analytic hierarchy process (AHP) and the survey method are mainly used to carry out the correlation analysis on the lightning approaching warning. The survey data shows that in this system, the accuracy rate of lightning early warning using decision tree algorithm can reach about 90%, indicating that machine learning has a good effect in lightning approaching early warning.

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Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

Cited by 38 publications

References 36 publications

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Lightning Warning Methods Based on Machine Learning and Single Station Ground Meteorological Elements

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