Abstract:The trade-off between Energy consumption and SLA violation presents a serious challenge in cloud computing environments. A non-aggressive virtual machine consolidation algorithm is a good approach to reduce the consumed energy as well as SLA violation. A well-known strategy to deal with the virtual machine consolidation problem consists of four steps: host overloading detection, host under-loading detection, virtual machine selection and virtual machine placement. In this paper, the previous strategy is modifi… Show more
“…e proposed method chooses an action from available acceptable actions and executes it on a cloud environment. It receives a reinforcement signal conforming to the suitability of the virtual machine placement solution by using that action [16]. e authors proposed a hybrid approach based on Naive Bayesian Classifier and Random Key Cuckoo Search for VM consolidation problem to minimize energy consumption.…”
In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.
“…e proposed method chooses an action from available acceptable actions and executes it on a cloud environment. It receives a reinforcement signal conforming to the suitability of the virtual machine placement solution by using that action [16]. e authors proposed a hybrid approach based on Naive Bayesian Classifier and Random Key Cuckoo Search for VM consolidation problem to minimize energy consumption.…”
In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.
In recent years, companies and researchers have hosted and rented computer resources over the internet due to cloud computing, which led to an increase in the energy consumed by data centers. This consumption is considered one of the world's highest, which pushed many researchers to propose several techniques such as server consolidation (SC) to solve the trade-off between energy saving and quality of service (QoS). SC requires maintaining service level agreements (SLA) violations and minimizing the number of active physical machines (PMs). Furthermore, to achieve this balance and avoid increasing hardware costs, the SC challenge targets placing new virtual machines (VMs) in suitable PMs. This work explored the existing SC algorithms that include CloudSim as a simulator environment and PlanetLab as a dataset. The authors compared the well-known optimization methods and extracted the weaknesses of the main three deployed approaches involved in the consolidation process: bin-packing model, metaheuristics, and machine learning-based solutions.
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