Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.
{"title":"A Dynamic Opportunistic Routing Protocol for Asynchronous Duty-Cycled WSNs","authors":"Xingfu Wang;Wenkang Zhou;Ammar Hawbani;Ping Liu;Liang Zhao;Saeed Hamood Alsamhi","doi":"10.1109/TSUSC.2023.3237220","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3237220","url":null,"abstract":"Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"314-327"},"PeriodicalIF":3.9,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50400490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-13DOI: 10.1109/TSUSC.2023.3236598
Tuhin Chakraborty;Adel N. Toosi;Carlo Kopp
Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called Elastic Power Utilization ( EPU ), to serve this purpose. EPU utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the EPU framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for Google , Wikipedia , and Nectar workloads, respectively.
{"title":"Elastic Power Utilization in Sustainable Micro Cloud Data Centers","authors":"Tuhin Chakraborty;Adel N. Toosi;Carlo Kopp","doi":"10.1109/TSUSC.2023.3236598","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3236598","url":null,"abstract":"Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called \u0000<italic>Elastic Power Utilization</i>\u0000 (\u0000<italic>EPU</i>\u0000), to serve this purpose. \u0000<italic>EPU</i>\u0000 utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the \u0000<italic>EPU</i>\u0000 framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for \u0000<italic>Google</i>\u0000, \u0000<italic>Wikipedia</i>\u0000, and \u0000<italic>Nectar</i>\u0000 workloads, respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"465-478"},"PeriodicalIF":3.9,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50400594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Residential water meters accommodate various methods of power provisioning. Electromagnetic and ultrasonic meters, for example, often rely on a battery-like external power source, whereas mechanical meters harvest energy from water flow through an impeller. Although energy harvesting (EH) minimizes maintenance needs driven by battery depletion/replenishment, placing a physical element into the flow adversely affects water pressure. This intrusive EH/sensing technique is not user-friendly either since the meters with impellers need to be embedded into pipes by skilled personnel. Hence, this paper proposes a non-intrusive sensor system powered by thermoelectric generators (TEGs) for plug-and-play water flow rate measurement. This system, equipped with a custom-made energy management unit (EMU), adopts ultrasonic sensors, a task-based computing scheme, and a LoRa module for autonomous sensing and reporting of the flow rate. After summarizing thermoelectricity and delta time-of-flight ( $Delta$ ToF)-based ultrasonic sensing theory, we provide the system model and design details with a particular focus on the EMU. Then, we experimentally evaluate the system under varying conditions, demonstrating their impact on average sensing and transmission periods. The results unveil that our proposal can achieve high measurement precision ( $pm 1.4%$ ), comparable to its intrusive and battery-powered counterparts, and thus has the potential of replacing the residential water meters.
{"title":"A TEG-Based Non-Intrusive Ultrasonic System for Autonomous Water Flow Rate Measurement","authors":"Sergey Mileiko;Oktay Cetinkaya;Darren Mackie;Rishad Shafik;Domenico Balsamo","doi":"10.1109/TSUSC.2023.3236524","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3236524","url":null,"abstract":"Residential water meters accommodate various methods of power provisioning. Electromagnetic and ultrasonic meters, for example, often rely on a battery-like external power source, whereas mechanical meters harvest energy from water flow through an impeller. Although energy harvesting (EH) minimizes maintenance needs driven by battery depletion/replenishment, placing a physical element into the flow adversely affects water pressure. This intrusive EH/sensing technique is not user-friendly either since the meters with impellers need to be embedded into pipes by skilled personnel. Hence, this paper proposes a non-intrusive sensor system powered by thermoelectric generators (TEGs) for \u0000<i>plug-and-play</i>\u0000 water flow rate measurement. This system, equipped with a custom-made energy management unit (EMU), adopts ultrasonic sensors, a task-based computing scheme, and a LoRa module for autonomous sensing and reporting of the flow rate. After summarizing thermoelectricity and delta time-of-flight (\u0000<inline-formula><tex-math>$Delta$</tex-math></inline-formula>\u0000ToF)-based ultrasonic sensing theory, we provide the system model and design details with a particular focus on the EMU. Then, we experimentally evaluate the system under varying conditions, demonstrating their impact on average sensing and transmission periods. The results unveil that our proposal can achieve high measurement precision (\u0000<inline-formula><tex-math>$pm 1.4%$</tex-math></inline-formula>\u0000), comparable to its intrusive and battery-powered counterparts, and thus has the potential of replacing the residential water meters.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"363-374"},"PeriodicalIF":3.9,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50280165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.1109/TSUSC.2022.3229310
Peijin Cong;Junlong Zhou;Weiming Jiang;Mingsong Chen;Shiyan Hu;Keqin Li
The network system deployed in hazardous environments is a key component of hazard-aware cyber-physical systems (CPSs) and its performance highly depends on surrounding environments. Due to the mobility of network nodes (e.g., portable IoT devices), frequently changeable network topology and links, as well as other external interferences such as electromagnetic interference, ensuring adaptivity and reliability of hazard-aware CPSs is of utmost importance. Meanwhile, the timeliness of message transmission is stringent in hazardous environments because the violation of timing requirements may lead to serious consequences. Last but not least, portable IoT devices are typically energy limited, thus ensuring a sustainable message transmission is highly necessary. In this paper, we aim at optimizing the reliability of hazard-aware CPSs while meeting the timing and energy constraints. To this end, we develop the first hazard-aware CPS model and study the impacts of surrounding environments (i.e., physical side) to the network infrastructure of a hazard-aware CPS (i.e., cyber side) with respect to reliability. We also propose a new scheme that adaptively tunes the fault tolerance strategies and admission strategies for real-time messages, to increase the reliability of hazard-aware CPSs under the energy constraint. Extensive simulation results demonstrate that our proposed scheme is capable of increasing system reliability by up to 4.21× with a lower deadline miss rate and runtime overhead compared with the state-of-the-art approaches.
{"title":"Improving Reliability and Sustainability of Hazard-Aware Cyber-Physical Systems","authors":"Peijin Cong;Junlong Zhou;Weiming Jiang;Mingsong Chen;Shiyan Hu;Keqin Li","doi":"10.1109/TSUSC.2022.3229310","DOIUrl":"10.1109/TSUSC.2022.3229310","url":null,"abstract":"The network system deployed in hazardous environments is a key component of hazard-aware cyber-physical systems (CPSs) and its performance highly depends on surrounding environments. Due to the mobility of network nodes (e.g., portable IoT devices), frequently changeable network topology and links, as well as other external interferences such as electromagnetic interference, ensuring adaptivity and reliability of hazard-aware CPSs is of utmost importance. Meanwhile, the timeliness of message transmission is stringent in hazardous environments because the violation of timing requirements may lead to serious consequences. Last but not least, portable IoT devices are typically energy limited, thus ensuring a sustainable message transmission is highly necessary. In this paper, we aim at optimizing the reliability of hazard-aware CPSs while meeting the timing and energy constraints. To this end, we develop the first hazard-aware CPS model and study the impacts of surrounding environments (i.e., physical side) to the network infrastructure of a hazard-aware CPS (i.e., cyber side) with respect to reliability. We also propose a new scheme that adaptively tunes the fault tolerance strategies and admission strategies for real-time messages, to increase the reliability of hazard-aware CPSs under the energy constraint. Extensive simulation results demonstrate that our proposed scheme is capable of increasing system reliability by up to 4.21× with a lower deadline miss rate and runtime overhead compared with the state-of-the-art approaches.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"271-282"},"PeriodicalIF":3.9,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86204905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/TSUSC.2022.3227749
Saifullah Khalid;Ishfaq Ahmad
Natural hazards and technical malfunctions can cause widespread outages of power networks, adversely affecting communities and infrastructures. Microgrids with distributed generation and storage can help mitigate some of these devastating effects. However, not many communities and infrastructures have alternative power mechanisms. When needed, microgrids may help needy neighbors or critical communities, such as hospitals, by donating or trading surplus capacity. Energy donation in a smart grid is a viable and highly effective restoration option to mitigate the effects of a disaster. However, because of the limited capacity of microgrids, service restoration requires prioritizing critical load and optimality of operations for rendering relief to those in dire need. In this paper, we propose a new framework for enabling energy donation in a smart grid during a crisis when the main supply is cut off. The proposed technique allocates energy using weighted and optimized rationing approaches. It does so by catering to the load critically and users’ historical contribution during restoration. The proposed method is based on an algorithm which employs evolutionary optimization technique that maximizes social welfare and minimizes losses while satisfying the resource and network constraints. Extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.
{"title":"Optimizing Energy Donation for Service Restoration in a Power Distribution System","authors":"Saifullah Khalid;Ishfaq Ahmad","doi":"10.1109/TSUSC.2022.3227749","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3227749","url":null,"abstract":"Natural hazards and technical malfunctions can cause widespread outages of power networks, adversely affecting communities and infrastructures. Microgrids with distributed generation and storage can help mitigate some of these devastating effects. However, not many communities and infrastructures have alternative power mechanisms. When needed, microgrids may help needy neighbors or critical communities, such as hospitals, by donating or trading surplus capacity. Energy donation in a smart grid is a viable and highly effective restoration option to mitigate the effects of a disaster. However, because of the limited capacity of microgrids, service restoration requires prioritizing critical load and optimality of operations for rendering relief to those in dire need. In this paper, we propose a new framework for enabling energy donation in a smart grid during a crisis when the main supply is cut off. The proposed technique allocates energy using weighted and optimized rationing approaches. It does so by catering to the load critically and users’ historical contribution during restoration. The proposed method is based on an algorithm which employs evolutionary optimization technique that maximizes social welfare and minimizes losses while satisfying the resource and network constraints. Extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"268-279"},"PeriodicalIF":3.9,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/TSUSC.2022.3224668
Weiqi Zhang;Line Roald;Victor Zavala
Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.
{"title":"Exploring the Impacts of Power Grid Signals on Data Center Operations Using a Receding-Horizon Scheduling Model","authors":"Weiqi Zhang;Line Roald;Victor Zavala","doi":"10.1109/TSUSC.2022.3224668","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3224668","url":null,"abstract":"Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"245-256"},"PeriodicalIF":3.9,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.
{"title":"UAV Dispatch Planning for a Wireless Rechargeable Sensor Network for Bridge Monitoring","authors":"Chuanxin Zhao;Yang Wang;Xin Zhang;Siguang Chen;Changzhi Wu;Kok Lay Teo","doi":"10.1109/TSUSC.2022.3224442","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3224442","url":null,"abstract":"Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"293-309"},"PeriodicalIF":3.9,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rise of the energy impact of software systems requires the need to optimize and reduce their energy consumption. One area often neglected is the important role played by users to drive energy reductions. In this paper, we aim to reduce the energy impact of software by pushing end users to change their software usage behavior, through raising awareness and providing software green feedback. We present a comprehensive and detailed field study of the impact of green feedback on software usage by end users, and the efficiency of green feedback on software behavioral change, using a distributed architecture aimed at providing accurate green feedback in real time. We find that green feedback helps in raising awareness about software energy, and on the willingness of users to apply energy-efficient changes. However, we also find that users lack the knowledge and tools to properly adopt lasting and energy-effective behavioral changes.
{"title":"The Impact of Green Feedback on Users’ Software Usage","authors":"Adel Noureddine;Martín Diéguez Lodeiro;Noëlle Bru;Richard Chbeir","doi":"10.1109/TSUSC.2022.3222631","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3222631","url":null,"abstract":"The rise of the energy impact of software systems requires the need to optimize and reduce their energy consumption. One area often neglected is the important role played by users to drive energy reductions. In this paper, we aim to reduce the energy impact of software by pushing end users to change their software usage behavior, through raising awareness and providing software green feedback. We present a comprehensive and detailed field study of the impact of green feedback on software usage by end users, and the efficiency of green feedback on software behavioral change, using a distributed architecture aimed at providing accurate green feedback in real time. We find that green feedback helps in raising awareness about software energy, and on the willingness of users to apply energy-efficient changes. However, we also find that users lack the knowledge and tools to properly adopt lasting and energy-effective behavioral changes.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"280-292"},"PeriodicalIF":3.9,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.1109/TSUSC.2022.3222409
Charalampos Marantos;Lazaros Papadopoulos;Christos P. Lamprakos;Konstantinos Salapas;Dimitrios Soudris
Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.
{"title":"Bringing Energy Efficiency Closer to Application Developers: An Extensible Software Analysis Framework","authors":"Charalampos Marantos;Lazaros Papadopoulos;Christos P. Lamprakos;Konstantinos Salapas;Dimitrios Soudris","doi":"10.1109/TSUSC.2022.3222409","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3222409","url":null,"abstract":"Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"180-193"},"PeriodicalIF":3.9,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many applications require simple controllers that continuously run the same application. These applications are often found in battery operated embedded systems that require to be ultra-low power (ULP) and are very price sensitive. Some examples include IoT devices of different nature and medical devices. Currently, these systems rely on off-the-shelf general-purpose microprocessors. One of the problems of using these processors, is that not all of the resources are needed for a specific application. Furthermore, because of the regularity of the workloads running on these systems there is a large opportunity to optimize the processor by pruning those unused resources to achieve lower area (cost) and power. Moreover, these processors can be specified at the behavioral level and use High-Level Synthesis (HLS) to generate an efficient Register Transfer Level (RTL) description. This opens a window to additional optimizations as the processor implementation is fully re-optimized during the HLS process. Also, many applications running on these embedded systems tolerate imprecise outputs. These include image processing and digital signal processing (DSP) applications. This opens the door to further optimizations in the context of approximate computing. To address these issues, this work presents a methodology to customize a behavioral RISC processor automatically for a given workload such that its area and power are significantly reduced as compared to the original, general-purpose processor. First, generating a bespoke processor that leads to the exact output as compared to the original general-purpose one and then by approximating it allowing a certain level of error at the output. Compared to previous work that customizes a given processor at the gate netlist only, our proposed method shows significant benefits. In particular, this work shows that raising the level of abstraction reduces the area and power by 78.3% and 70.1% for the exact solution on average, and further reduces the area by an additional 10.0% and 16.5% for the approximate version tolerating a maximum of 10% and 20% output errors respectively.
{"title":"Application Specific Approximate Behavioral Processor","authors":"Qilin Si;Prattay Chowdhury;Rohit Sreekumar;Benjamin Carrion Schafer","doi":"10.1109/TSUSC.2022.3222117","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3222117","url":null,"abstract":"Many applications require simple controllers that continuously run the same application. These applications are often found in battery operated embedded systems that require to be ultra-low power (ULP) and are very price sensitive. Some examples include IoT devices of different nature and medical devices. Currently, these systems rely on off-the-shelf general-purpose microprocessors. One of the problems of using these processors, is that not all of the resources are needed for a specific application. Furthermore, because of the regularity of the workloads running on these systems there is a large opportunity to optimize the processor by pruning those unused resources to achieve lower area (cost) and power. Moreover, these processors can be specified at the behavioral level and use High-Level Synthesis (HLS) to generate an efficient Register Transfer Level (RTL) description. This opens a window to additional optimizations as the processor implementation is fully re-optimized during the HLS process. Also, many applications running on these embedded systems tolerate imprecise outputs. These include image processing and digital signal processing (DSP) applications. This opens the door to further optimizations in the context of approximate computing. To address these issues, this work presents a methodology to customize a behavioral RISC processor automatically for a given workload such that its area and power are significantly reduced as compared to the original, general-purpose processor. First, generating a bespoke processor that leads to the exact output as compared to the original general-purpose one and then by approximating it allowing a certain level of error at the output. Compared to previous work that customizes a given processor at the gate netlist only, our proposed method shows significant benefits. In particular, this work shows that raising the level of abstraction reduces the area and power by 78.3% and 70.1% for the exact solution on average, and further reduces the area by an additional 10.0% and 16.5% for the approximate version tolerating a maximum of 10% and 20% output errors respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 2","pages":"165-179"},"PeriodicalIF":3.9,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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