RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Gupta, Abhishek; Bhadauria, H.S.; Singh, Annapurna

doi:10.1007/s12652-020-02458-1

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…Gupta et al [22] presented a scheme for load balancing based on risk management. This scheme utilized the advanced scheduling algorithm for resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers

et al. 2022

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Cloud computing is a global storage framework whereby users use the tools, including the computation, storage, network, etc., that are provided automatically. The computational has led the cloud data centers to be stored in digital services that many consumers distribute. The biggest problem with cloud data centers is handling the millions of users' continuous proposals. Therefore, in this paper, Adaptive Scheduling Algorithm Based Task Loading (ASA-TL) has been proposed to manage cloud data centers' task to be stored in digital devices. ASA is implemented in which the task is shared between all the current virtual servers,and the cloud data are protected from overloading. The data assignment is made by taking account of the importance and status of the digital device that helps to assign task fairly and use them efficiently. TL manages these requests efficiently, and the task input must be equally and reliably allocated between various processors. The experimental result suggests that ASA-TL achieves the highest performance assessment measurements, including response time, processing time in the data center and overall expense.INDEX TERMS Data center, cloud computing, response time, task management.

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“…Gupta et al [22] presented a scheme for load balancing based on risk management. This scheme utilized the advanced scheduling algorithm for resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers

et al. 2022

View full text Add to dashboard Cite

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“…These cloud computing services are made available via cloud data centers. To meet day-to-day needs such as bulk amounts of data, cloud-related environments must provide high-performance servers and fast storage devices [19], [20], [21], [22], [23], [24]. The resources are considered a major source of power consumption, like air conditioning and cooling down various equipment [25].…”

Section: Introductionmentioning

confidence: 99%

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Algarni,

Shah,

Jehangiri

et al. 2024

IEEE Access

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Cloud computing infrastructure is designed to deploy and assess service-oriented applications, primarily via cloud datacenters. These datacenters are integral to energy utilization in cloud environments, with energy consumption closely tied to resource utilization. It is important to monitor and predict power consumption in these datacenters, especially for high-demand services. Container-based virtualization, particularly using Docker containers, has gained significant attention due to its lightweight nature. However, predicting energy usage at a fine-grained level for container-based applications is a challenging task. In this study, we employ three time series analysis algorithms-AR, ARIMA, and ETS-to predict the energy usage of Docker containers over the next hour. Utilizing collected time-series power consumption data, our study contributes to enhancing power predictions for Docker containers within cloud infrastructures. Our prediction results focus on four Docker containers, each running multiple applications as Docker subprocesses. Power data for individual applications was aggregated to determine total container power consumption. Comparing the performance of ARIMA, ETS, and AR algorithms in predicting Docker container instance power, we found varying outcomes across containers. Through assessing MAPE across different time series model window lengths, we identified superior performance among the models. Specifically, ETS consistently demonstrated the lowest MAPE values for containers like 'polinx-container' and 'alpines-container', indicating higher prediction accuracy compared to ARIMA and AR models. The ARIMA model outperformed the ETS and AR models for the 'progrium container'. These findings underscore the necessity of selecting appropriate time series models tailored to specific Docker container configurations and workload scenarios for precise energy consumption forecasts.INDEX TERMS Docker container; timer series analysis, energy consumption; cloud computing; monitoring.

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