DiAndra Phillip, Jin Chen, F. Maksakuli, Arber Ruci, E'edresha Sturdivant, Zhigang Zhu
For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future prediction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores.
{"title":"Improving Building Energy Efficiency through Data Analysis","authors":"DiAndra Phillip, Jin Chen, F. Maksakuli, Arber Ruci, E'edresha Sturdivant, Zhigang Zhu","doi":"10.1145/3599733.3600244","DOIUrl":"https://doi.org/10.1145/3599733.3600244","url":null,"abstract":"For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future prediction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134328550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents an argument for the use of specific documentation for the ethical development, use, and sharing of energy datasets, and an evaluation of current practice in the energy AI community. Drawing on a recently developed resource from the broader machine learning community and applying it to the specific context of energy AI research, opportunities for more transparent collection and distribution of energy datasets are revealed. To help elucidate the utility of the datasheets and the energy community’s current level of documentation, two publicly available energy datasets are chosen for analysis. One has published documentation covering 66% of the datasheet questionnaire, while the second covers 42% of the suggested information. Two additional questions are recommended for energy-relevant datasheets that will promote ethical AI practices in the energy domain. A new resource for exploring and aligning energy datasets with demographic data is provided.
{"title":"Datasheets for Energy Datasets: An Ethically-Minded Approach to Documentation","authors":"Ilana Heintz","doi":"10.1145/3599733.3600249","DOIUrl":"https://doi.org/10.1145/3599733.3600249","url":null,"abstract":"This work presents an argument for the use of specific documentation for the ethical development, use, and sharing of energy datasets, and an evaluation of current practice in the energy AI community. Drawing on a recently developed resource from the broader machine learning community and applying it to the specific context of energy AI research, opportunities for more transparent collection and distribution of energy datasets are revealed. To help elucidate the utility of the datasheets and the energy community’s current level of documentation, two publicly available energy datasets are chosen for analysis. One has published documentation covering 66% of the datasheet questionnaire, while the second covers 42% of the suggested information. Two additional questions are recommended for energy-relevant datasheets that will promote ethical AI practices in the energy domain. A new resource for exploring and aligning energy datasets with demographic data is provided.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123877491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanathorn Sukprasert, Abel Souza, Noman Bashir, David E. Irwin, P. Shenoy
As the demand for computing continues to grow exponentially and datacenters are already highly optimized, many have suggested leveraging computing workload's spatiotemporal flexibility. However, different workloads may have different degrees of flexibility, including execution deadlines, data protection laws, or latency requirements. These constraints, along with many others, limit the potential benefits of carbon-aware spatiotemporal workload shifting; the achievable benefits of these approaches are unclear-an aspect not addressed by prior research. Accurately quantifying the achievable benefits of carbon-aware spatiotemporal workload scheduling is critically important, as many in research and industry are already devoting significant time and resources to realize these benefits. To address the problem, we conduct a large-scale longitudinal analysis of carbon-aware spatiotemporal workload shifting to answer the following research question: What are the maximum carbon emission reductions that can be achieved due to temporal and spatial workload shifting for different types of cloud workloads and in different parts of the world?
{"title":"Spatiotemporal Carbon-aware Scheduling in the Cloud: Limits and Benefits","authors":"Thanathorn Sukprasert, Abel Souza, Noman Bashir, David E. Irwin, P. Shenoy","doi":"10.1145/3599733.3606301","DOIUrl":"https://doi.org/10.1145/3599733.3606301","url":null,"abstract":"As the demand for computing continues to grow exponentially and datacenters are already highly optimized, many have suggested leveraging computing workload's spatiotemporal flexibility. However, different workloads may have different degrees of flexibility, including execution deadlines, data protection laws, or latency requirements. These constraints, along with many others, limit the potential benefits of carbon-aware spatiotemporal workload shifting; the achievable benefits of these approaches are unclear-an aspect not addressed by prior research. Accurately quantifying the achievable benefits of carbon-aware spatiotemporal workload scheduling is critically important, as many in research and industry are already devoting significant time and resources to realize these benefits. To address the problem, we conduct a large-scale longitudinal analysis of carbon-aware spatiotemporal workload shifting to answer the following research question: What are the maximum carbon emission reductions that can be achieved due to temporal and spatial workload shifting for different types of cloud workloads and in different parts of the world?","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122318899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Higher penetration of renewable generation has led to a large increase in 'curtailment', i.e. periods in which generation from renewable resources is lost as a result of insufficient demand or lacking grid transmission capacity. Electricity consumers could help avoid curtailment - and reduce emissions - by shifting their consumption to time periods with curtailment. However, their ability to do so is severely limited by a lack of real-time curtailment information. To address this issue, we present a classification model based on gradient-boosted learning which identifies solar curtailment in real time for the California grid. The model relies only on publicly-available, real-time grid information, and is tuned specifically to capture time periods with high curtailment. Our analysis shows that the proposed classifier can precisely and reliably identify solar curtailments.
{"title":"A Classification Model for Real-time Identification of Solar Curtailment in the California Grid","authors":"J. Gorka, Line A. Roald","doi":"10.1145/3599733.3606303","DOIUrl":"https://doi.org/10.1145/3599733.3606303","url":null,"abstract":"Higher penetration of renewable generation has led to a large increase in 'curtailment', i.e. periods in which generation from renewable resources is lost as a result of insufficient demand or lacking grid transmission capacity. Electricity consumers could help avoid curtailment - and reduce emissions - by shifting their consumption to time periods with curtailment. However, their ability to do so is severely limited by a lack of real-time curtailment information. To address this issue, we present a classification model based on gradient-boosted learning which identifies solar curtailment in real time for the California grid. The model relies only on publicly-available, real-time grid information, and is tuned specifically to capture time periods with high curtailment. Our analysis shows that the proposed classifier can precisely and reliably identify solar curtailments.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Adamski, Radosław Górzeński, Turhan Can Kargin, Łukasz Malewski, Ariel Oleksiak, Franciszek Sidorski
Data centers are huge energy consumers but also a source of significant amounts of waste heat. Within this paper we present approach to re-use the data center waste heat both locally and in connection to a district heating network. The heat re-use design and analysis process is supported by the methodology and software tools developed by the RENergetic project. The analysis is performed based on a case study of the university campus and a data center located in a close neighbourhood.
{"title":"Planning data center waste heat re-use in a university campus - a case study and software tools","authors":"J. Adamski, Radosław Górzeński, Turhan Can Kargin, Łukasz Malewski, Ariel Oleksiak, Franciszek Sidorski","doi":"10.1145/3599733.3600256","DOIUrl":"https://doi.org/10.1145/3599733.3600256","url":null,"abstract":"Data centers are huge energy consumers but also a source of significant amounts of waste heat. Within this paper we present approach to re-use the data center waste heat both locally and in connection to a district heating network. The heat re-use design and analysis process is supported by the methodology and software tools developed by the RENergetic project. The analysis is performed based on a case study of the university campus and a data center located in a close neighbourhood.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Energy efficiency is a critical concern for machine learning (ML) algorithms deployed in data centers. Recently, many works in the literature have focused on running ML algorithms on energy-efficient and constrained hardware, such as mobile phones, to reduce the energy footprint of training ML models. This paper introduces the Power Profiler, an open-source monitoring platform that provides valuable insights into the energy consumption of ML algorithms on Android mobile devices. By capturing key performance indicators (KPIs) such as voltage, current, and CPU usage, the Power Profiler enables real-time monitoring of their energy usage. It eliminates the need for custom hardware installations and facilitates the development of energy-efficient ML models. The Power Profiler can empower researchers to understand and optimize the energy consumption patterns of ML algorithms, facilitating the creation of sustainable ML models for energy-efficient mobile deployments.
{"title":"Power Profiler: Monitoring Energy Consumption of ML Algorithms on Android Mobile Devices","authors":"Karim Boubouh, Robert Basmadjian","doi":"10.1145/3599733.3606304","DOIUrl":"https://doi.org/10.1145/3599733.3606304","url":null,"abstract":"Energy efficiency is a critical concern for machine learning (ML) algorithms deployed in data centers. Recently, many works in the literature have focused on running ML algorithms on energy-efficient and constrained hardware, such as mobile phones, to reduce the energy footprint of training ML models. This paper introduces the Power Profiler, an open-source monitoring platform that provides valuable insights into the energy consumption of ML algorithms on Android mobile devices. By capturing key performance indicators (KPIs) such as voltage, current, and CPU usage, the Power Profiler enables real-time monitoring of their energy usage. It eliminates the need for custom hardware installations and facilitates the development of energy-efficient ML models. The Power Profiler can empower researchers to understand and optimize the energy consumption patterns of ML algorithms, facilitating the creation of sustainable ML models for energy-efficient mobile deployments.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132506195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johannes Galenzowski, Simon Waczowicz, V. Hagenmeyer
In order to achieve the worldwide set ambitious climate goals, the identification and characterization of flexibility in city districts can reduce grid loads and avoid grid congestion. Unlike other flexibility indicators in the literature, the present paper introduces a new flexibility indicator that uses a data-driven approach to determine flexibility from actual measured load profiles. We present this new indicator by considering flexibility in the context of planning charging infrastructure with a valley filling approach. For this use case, we introduce a data-analysis workflow to apply the presented flexibility indicator. The described data-analysis workflow is applied to data from a real-world city district. Based on the results from the real-world data, we show that the highest peak load and the least flexible peak are not always identical. Therefore, it is not sufficient to consider only the highest peak loads to adequately describe flexibility. Furthermore, we discuss that additional flexibility can be used as another degree of freedom to optimize the charging power or the charging duration. In the presented real-world data, we show that the maximum required charging power is determined by the most inflexible peak and can be the same or smaller for all peaks with a higher flexibility. Moreover, we highlight the difference between considering buildings individually and combining them as a district.
{"title":"A new Data-Driven Approach for Comparative Assessment of Baseline Load Profiles Supporting the Planning of Future Charging Infrastructure","authors":"Johannes Galenzowski, Simon Waczowicz, V. Hagenmeyer","doi":"10.1145/3599733.3600245","DOIUrl":"https://doi.org/10.1145/3599733.3600245","url":null,"abstract":"In order to achieve the worldwide set ambitious climate goals, the identification and characterization of flexibility in city districts can reduce grid loads and avoid grid congestion. Unlike other flexibility indicators in the literature, the present paper introduces a new flexibility indicator that uses a data-driven approach to determine flexibility from actual measured load profiles. We present this new indicator by considering flexibility in the context of planning charging infrastructure with a valley filling approach. For this use case, we introduce a data-analysis workflow to apply the presented flexibility indicator. The described data-analysis workflow is applied to data from a real-world city district. Based on the results from the real-world data, we show that the highest peak load and the least flexible peak are not always identical. Therefore, it is not sufficient to consider only the highest peak loads to adequately describe flexibility. Furthermore, we discuss that additional flexibility can be used as another degree of freedom to optimize the charging power or the charging duration. In the presented real-world data, we show that the maximum required charging power is determined by the most inflexible peak and can be the same or smaller for all peaks with a higher flexibility. Moreover, we highlight the difference between considering buildings individually and combining them as a district.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125169976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Worship Facilities in India: A Large Unexplored Goldmine for Decarbonization","authors":"Balaji Kalluri, P. Arjunan","doi":"10.1145/3599733.3606297","DOIUrl":"https://doi.org/10.1145/3599733.3606297","url":null,"abstract":"","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126403513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahsa Sahebdel, A. Zeynali, Noman Bashir, M. Hajiesmaili, Jimi B. Oke
Urban mobility contributes 40% of CO2 emissions from road transport, which is projected to double by 2050 [6]. Ride-sharing services like Uber and Lyft have transformed urban mobility by providing convenient and on-demand personal transportation through smartphone applications. However, their success has resulted in an increase in traffic and congestion on roads?a type of rebound effect. For example, in New York City, ride-sharing accounts for over 50% of road traffic. Recent studies estimate that a typical ride-sharing trip is less efficient than a personal car trip, mainly due to "deadhead" miles traveled by a ride-share vehicle between consecutive hired rides, resulting in 36-45% higher distance travelled and upto 47% higher CO2 emissions compared to a private car ride [3]. As a result, there is a need to develop emission-aware ride-assignment algorithms that reduce emissions from deadhead miles. Recent work has used theoretical as well as data-driven and machine learning (ML) approaches to improve the performance of ride-sharing platforms. For example, Abkarian et al. [1] present a model that aims to balance the tradeoff between waiting times and deadhead mileage driven by the vehicles in the fleet. Ke et al. [4] propose a novel spatio-temporal deep learning approach that uses a convolutional neural network (CNN) to model the spatial distribution of demand and a long short-term memory (LSTM) network to model the temporal patterns in ride demand. While these studies focus on improving the performance of ride-sharing services, they do not explicitly target reducing deadhead miles. The most relevant work to ours targets reducing deadhead miles for individual trips [5]. Authors combine demand predictions with a heuristic approach to driver assignment to demonstrate up to 82% reduction in trip-level deadhead miles. However, their approach may not effectively reduce system-wide deadhead miles and emissions, which depend on factors like fuel efficiency and traffic conditions. Furthermore, they neither consider EVs nor do they take equity into account. Our work takes a holistic approach toward designing multi-objective ride assignment optimizations, aiming to reduce emissions from deadhead miles, incorporate equity considerations, and account for EVs in ride-sharing fleets. In this paper, we present a preliminary study illustrating the benefits of emission-aware ride assignment and propose combining data-driven algorithms and machine learning to enhance online decision-making processes.
{"title":"Data-driven Algorithms for Reducing the Carbon Footprint of Ride-sharing Ecosystems","authors":"Mahsa Sahebdel, A. Zeynali, Noman Bashir, M. Hajiesmaili, Jimi B. Oke","doi":"10.1145/3599733.3606300","DOIUrl":"https://doi.org/10.1145/3599733.3606300","url":null,"abstract":"Urban mobility contributes 40% of CO2 emissions from road transport, which is projected to double by 2050 [6]. Ride-sharing services like Uber and Lyft have transformed urban mobility by providing convenient and on-demand personal transportation through smartphone applications. However, their success has resulted in an increase in traffic and congestion on roads?a type of rebound effect. For example, in New York City, ride-sharing accounts for over 50% of road traffic. Recent studies estimate that a typical ride-sharing trip is less efficient than a personal car trip, mainly due to \"deadhead\" miles traveled by a ride-share vehicle between consecutive hired rides, resulting in 36-45% higher distance travelled and upto 47% higher CO2 emissions compared to a private car ride [3]. As a result, there is a need to develop emission-aware ride-assignment algorithms that reduce emissions from deadhead miles. Recent work has used theoretical as well as data-driven and machine learning (ML) approaches to improve the performance of ride-sharing platforms. For example, Abkarian et al. [1] present a model that aims to balance the tradeoff between waiting times and deadhead mileage driven by the vehicles in the fleet. Ke et al. [4] propose a novel spatio-temporal deep learning approach that uses a convolutional neural network (CNN) to model the spatial distribution of demand and a long short-term memory (LSTM) network to model the temporal patterns in ride demand. While these studies focus on improving the performance of ride-sharing services, they do not explicitly target reducing deadhead miles. The most relevant work to ours targets reducing deadhead miles for individual trips [5]. Authors combine demand predictions with a heuristic approach to driver assignment to demonstrate up to 82% reduction in trip-level deadhead miles. However, their approach may not effectively reduce system-wide deadhead miles and emissions, which depend on factors like fuel efficiency and traffic conditions. Furthermore, they neither consider EVs nor do they take equity into account. Our work takes a holistic approach toward designing multi-objective ride assignment optimizations, aiming to reduce emissions from deadhead miles, incorporate equity considerations, and account for EVs in ride-sharing fleets. In this paper, we present a preliminary study illustrating the benefits of emission-aware ride assignment and propose combining data-driven algorithms and machine learning to enhance online decision-making processes.","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130366931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Henrik Forsgren, Rickard Brännvall, Mattias Vesterlund, T. Minde
The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?
{"title":"Homomorphic Encryption Enables Data and Algorithm Confidentiality for Remote Monitoring and Control: An Application to Data Center Systems","authors":"Henrik Forsgren, Rickard Brännvall, Mattias Vesterlund, T. Minde","doi":"10.1145/3599733.3600254","DOIUrl":"https://doi.org/10.1145/3599733.3600254","url":null,"abstract":"The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?","PeriodicalId":114998,"journal":{"name":"Companion Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130556654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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