Data centers consume an enormous amount of energy to meet the ever-increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal-aware consolidation methods. We propose an Energy and Thermal-Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade-off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real-world traces with precise power and thermal models.The experimental results and empirical studies demonstrate that ETAS outperforms other state-of-the-art algorithms by reducing overall energy without any hotspot creation. KEYWORDScloud computing, data center cooling, energy efficiency in a data center, hotspots VM consolidation INTRODUCTIONCloud computing is a massive paradigm shift from how the computing capabilities are acquired in past from traditional ownership model to current subscription model. 1 Cloud offers on-demand access to elastic resources as services with pay as you go model based on the actual usage of resources. Cloud data centers are the backbone infrastructure to cloud services. To adapt to the increasing demand for massive scale cloud services, data centers house thousands of servers to fulfill their computing needs. However, they are power hungry and consume a huge amount of energy to provide cloud services in a reliable manner. According to the USA energy department report, 2 data centers in the USA itself consume about 2% (70 billion kWh) of the total energy production. Not only do data centers consume huge power; they significantly contribute to the greenhouse gas emissions resulting in high carbon footprints. To be precise, they generate 43 million tons of CO 2 per year and continues to grow at an annual rate of 11%. 3 If the necessary steps are taken, data center power consumption can be reduced from the predicted worst case of 8000 TWh to 1200 TWh by the year 2030. 4 Therefore, improving the energy efficiency of the cloud data center is quintessential for sustainable and cost-effective cloud computing.A significant part of cloud data centers' energy consumption emanates from computing and cooling systems. In particular, the contribution of cooling system power is almost equal to the computing system. 5 In this context, a data center resource management system should holistically contemplate computing and cooling power together to achieve overall energy efficiency.In pursuance of reducing the computing energy, workloads are consolidated on the fewest hosts as ...
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38°C, which is 6°C less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5°C and consumes 34.5% less energy as compared to the baseline algorithm.
The emerging trend towards moving from monolithic applications to microservices has raised new performance challenges in cloud computing environments. Compared with traditional monolithic applications, the microservices are lightweight, fine-grained, and must be executed in a shorter time. Efficient scaling approaches are required to ensure microservices' system performance under diverse workloads with strict Quality of Service (QoS) requirements and optimize resource provisioning. To solve this problem, we investigate the trade-offs between the dominant scaling techniques, including horizontal scaling, vertical scaling, and brownout in terms of execution cost and response time. We first present a prediction algorithm based on gradient recurrent units to accurately predict workloads assisting in scaling to achieve efficient scaling. Further, we propose a multi-faceted scaling approach using reinforcement learning called CoScal to learn the scaling techniques efficiently. The proposed CoScal approach takes full advantage of datadriven decisions and improves the system performance in terms of high communication cost and delay. We validate our proposed solution by implementing a containerized microservice prototype system and evaluated with two microservice applications. The extensive experiments demonstrate that CoScal reduces response time by 19% to 29% and decreases the connection time of services by 16% when compared with the state-of-the-art scaling techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.