Thermal Prediction for Efficient Energy Management of Clouds Using Machine Learning

Ilager, Shashikant; Ramamohanarao, Kotagiri; Buyya, Rajkumar

doi:10.1109/tpds.2020.3040800

Cited by 62 publications

(27 citation statements)

References 31 publications

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“…They then propose enhancements to server architectures to help mitigate such efects. At a system level, Ilager et al in [86] explore using ML techniques for thermal prediction for energy eicient management of cloud computing systems.…”

Section: Pue = Total_power_consumption It_power_consumptionmentioning

confidence: 99%

Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards

2022

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Computing systems have undergone a tremendous change in the last few decades with several inflexion points. While Moore’s law guided the semiconductor industry to cram more and more transistors and logic into the same volume, the limits of instruction-level parallelism (ILP) and the end of Dennard’s scaling drove the industry towards multi-core chips. More recently, we have entered the era of domain-specific architectures and chips for new workloads like artificial intelligence (AI) and machine learning (ML). These trends continue, arguably with other limits, along with challenges imposed by tighter integration, extreme form factors and increasingly diverse workloads, making systems more complex to architect, design, implement and optimize from an energy efficiency perspective. Energy efficiency has now become a first order design parameter and constraint across the entire spectrum of computing devices. Many research surveys have gone into different aspects of energy efficiency techniques implemented in hardware and microarchitecture across devices, servers, HPC/cloud, data center systems along with improved software, algorithms, frameworks, and modeling energy/thermals. Somewhat in parallel, the semiconductor industry has developed techniques and standards around specification, modeling/simulation, benchmarking and verification of complex chips; these areas have not been addressed in detail by previous research surveys. This survey aims to bring these domains holistically together, present the latest in each of these areas, highlight potential gaps and challenges, and discuss opportunities for the next generation of energy efficient systems. The survey is composed of a systematic categorization of key aspects of building energy efficient systems - (1) specification - the ability to precisely specify the power intent, attributes or properties at different layers (2) modeling and simulation of the entire system or subsystem (hardware or software or both) so as to be able to experiment with possible options and perform what-if analysis, (3) techniques used for implementing energy efficiency at different levels of the stack, (4) verification techniques used to provide guarantees that the functionality of complex designs are preserved, and (5) energy efficiency benchmarks, standards and consortiums that aim to standardize different aspects of energy efficiency, including cross-layer optimizations.

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Section: Pue = Total_power_consumption It_power_consumptionmentioning

confidence: 99%

Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards

2022

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“…As a consequence, estimating host temperature ahead of time can help with thermal management decisions like VM migration to reduce host temperature, i.e., CPU temperature. [78], on the other hand, took into account the ambient temperature for prediction, which is a combination of CPU and inlet temperature. This could result in an increase in algorithm overhead.…”

Section: Host Temperaturementioning

confidence: 99%

Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions

Khan¹,

Tian²,

Buyya³

2021

Preprint

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Cloud computing has rapidly emerged as model for delivering Internet-based utility computing services. In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields. Cloud providers provide users/machines resources such as virtual machines, raw (block) storage, firewalls, load balancers, and network devices in this service model. One of the most important aspects of cloud computing for IaaS is resource management. Scalability, quality of service, optimum utility, reduced overheads, increased throughput, reduced latency, specialised environment, cost effectiveness, and a streamlined interface are some of the advantages of resource management for IaaS in cloud computing. Traditionally, resource management has been done through static policies, which impose certain limitations in various dynamic scenarios, prompting cloud service providers to adopt data-driven, machine-learning-based approaches. Machine learning is being used to handle a variety of resource management tasks, including workload estimation, task scheduling, VM consolidation, resource optimization, and energy optimization, among others. This paper provides a detailed review of challenges in ML-based resource management in current research, as well as current approaches to resolve these challenges, as well as their advantages and limitations. Finally, we propose potential future research directions based on identified challenges and limitations in current research.

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“…Some models use a set of parameters from the server room that are relevant for the thermodynamics processes and use machine learning to predict their evolution. Gradient boosting decision trees, artificial neural networks, or deep learning models are used to predict the server room temperature [ 42 , 43 ]. Finally, Grammatical Evolution techniques [ 44 ] and Environmentally Opportunistic Computing [ 45 ] are used for analyzing server and inlet air temperatures and predicting the temperatures, in conjunction with thermal models of DCs.…”

Section: Related Workmentioning

confidence: 99%

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

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Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

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Thermal Prediction for Efficient Energy Management of Clouds Using Machine Learning

Cited by 62 publications

References 31 publications

Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards

Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards

Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

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