Pub Date : 2023-03-08DOI: 10.1109/TSUSC.2023.3273852
Mattia Tibaldi;Christian Pilato
This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGAs energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency . The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.
{"title":"A Survey of FPGA Optimization Methods for Data Center Energy Efficiency","authors":"Mattia Tibaldi;Christian Pilato","doi":"10.1109/TSUSC.2023.3273852","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3273852","url":null,"abstract":"This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGAs energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the \u0000<italic>European Code of Conduct for Data Center Energy Efficiency</i>\u0000. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"343-362"},"PeriodicalIF":3.9,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50280163","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 : 2023-03-08DOI: 10.1109/TSUSC.2023.3252039
Maryleen Uluaku Amaizu;Muhammad K. Ali;Ashiq Anjum;Lu Liu;Antonio Liotta;Omer Rana
Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. However, Machine Learning (ML) algorithms typically require significant computational resources, hence cannot be directly deployed on resource-constrained edge devices for learning and analytics. Edge-enhanced compressive offloading becomes a sustainable solution that allows data to be compressed at the edge and offloaded to the cloud for further analysis, reducing bandwidth consumption and communication latency. The design and implementation of a learning method for discovering compression techniques that offer the best QoS for an application is described. The approach uses a novel modularisation approach that maps features to models and classifies them for a range of Quality of Service (QoS) features. An automated QoS-aware orchestrator has been designed to select the best autoencoder model in real-time for compressive offloading in edge-enhanced clouds based on changing QoS requirements. The orchestrator has been designed to have diagnostic capabilities to search appropriate parameters that give the best compression. A key novelty of this work is harnessing the capabilities of autoencoders for edge-enhanced compressive offloading based on portable encodings, latent space splitting and fine-tuning network weights. Considering how the combination of features lead to different QoS models, the system is capable of processing a large number of user requests in a given time. The proposed hyperparameter search strategy (over the neural architectural space) reduces the computational cost of search through the entire space by up to 89%. When deployed on an edge-enhanced cloud using an Azure IoT testbed, the approach saves up to 70% data transfer costs and takes 32% less time for job completion. It eliminates the additional computational cost of decompression, thereby reducing the processing cost by up to 30%.
{"title":"Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics","authors":"Maryleen Uluaku Amaizu;Muhammad K. Ali;Ashiq Anjum;Lu Liu;Antonio Liotta;Omer Rana","doi":"10.1109/TSUSC.2023.3252039","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3252039","url":null,"abstract":"Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. However, Machine Learning (ML) algorithms typically require significant computational resources, hence cannot be directly deployed on resource-constrained edge devices for learning and analytics. Edge-enhanced compressive offloading becomes a sustainable solution that allows data to be compressed at the edge and offloaded to the cloud for further analysis, reducing bandwidth consumption and communication latency. The design and implementation of a learning method for discovering compression techniques that offer the best QoS for an application is described. The approach uses a novel modularisation approach that maps features to models and classifies them for a range of Quality of Service (QoS) features. An automated QoS-aware orchestrator has been designed to select the best autoencoder model in real-time for compressive offloading in edge-enhanced clouds based on changing QoS requirements. The orchestrator has been designed to have diagnostic capabilities to search appropriate parameters that give the best compression. A key novelty of this work is harnessing the capabilities of autoencoders for edge-enhanced compressive offloading based on portable encodings, latent space splitting and fine-tuning network weights. Considering how the combination of features lead to different QoS models, the system is capable of processing a large number of user requests in a given time. The proposed hyperparameter search strategy (over the neural architectural space) reduces the computational cost of search through the entire space by up to 89%. When deployed on an edge-enhanced cloud using an Azure IoT testbed, the approach saves up to 70% data transfer costs and takes 32% less time for job completion. It eliminates the additional computational cost of decompression, thereby reducing the processing cost by up to 30%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"448-464"},"PeriodicalIF":3.9,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50400593","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 : 2023-03-01DOI: 10.1109/TSUSC.2023.3271789
Adil Israr;Qiang Yang;Ali Israr
A massive number of small cell base stations are expected to be deployed in the 5G and beyond 5G mobile communication networks due to the exponential increase in mobile traffic. This will directly lead to not only a significant increase in energy consumption but also the overall operational cost and carbon footprint. An energy provision based on renewable energy generation to power these small cell base stations is considered a sustainable and promising solution to address this challenge. This paper exploits the cost-effective and low-carbon energy provision solution for individual small-cell mobile networks and presents two different potential frameworks, i.e., centralized and distributed energy provision, respectively. The former supplies nearby small cell base stations through a centralized renewable energy source with energy storage facilities. For the latter, small cell base stations can be supplied by utilizing local renewable energy and storage facilities. These two frameworks are assessed and compared in terms of renewable energy utilization and carbon emission reduction in the presence of time-varying traffic loads, small cell locations and renewable energy availabilities. In addition, we devise energy management for these configurations by incorporating a resource-on-demand strategy in the proposed framework. The numerical simulation results demonstrate that the proposed centralized renewable energy generation strategy for nearby small cells maximizes the cost and energy efficiencies of the network.
{"title":"Emission-Aware Sustainable Energy Provision for 5G and B5G Mobile Networks","authors":"Adil Israr;Qiang Yang;Ali Israr","doi":"10.1109/TSUSC.2023.3271789","DOIUrl":"10.1109/TSUSC.2023.3271789","url":null,"abstract":"A massive number of small cell base stations are expected to be deployed in the 5G and beyond 5G mobile communication networks due to the exponential increase in mobile traffic. This will directly lead to not only a significant increase in energy consumption but also the overall operational cost and carbon footprint. An energy provision based on renewable energy generation to power these small cell base stations is considered a sustainable and promising solution to address this challenge. This paper exploits the cost-effective and low-carbon energy provision solution for individual small-cell mobile networks and presents two different potential frameworks, i.e., centralized and distributed energy provision, respectively. The former supplies nearby small cell base stations through a centralized renewable energy source with energy storage facilities. For the latter, small cell base stations can be supplied by utilizing local renewable energy and storage facilities. These two frameworks are assessed and compared in terms of renewable energy utilization and carbon emission reduction in the presence of time-varying traffic loads, small cell locations and renewable energy availabilities. In addition, we devise energy management for these configurations by incorporating a resource-on-demand strategy in the proposed framework. The numerical simulation results demonstrate that the proposed centralized renewable energy generation strategy for nearby small cells maximizes the cost and energy efficiencies of the network.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"670-681"},"PeriodicalIF":3.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79428951","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 : 2023-03-01DOI: 10.1109/TSUSC.2023.3251302
Liangliang Chen;Fei Meng;Ying Zhang
Reinforcement learning (RL) methods can be used to develop a controller for the heating, ventilation, and air conditioning (HVAC) systems that both saves energy and ensures high occupants’ thermal comfort levels. However, the existing works typically require on-policy data to train an RL agent, and the occupants’ personalized thermal preferences are not considered, which is limited in the real-world scenarios. This paper designs a high-performance model-based offline RL algorithm for personalized HVAC systems. The proposed algorithm can quickly adapt to different occupants’ thermal preferences with a few thermal feedbacks, guaranteeing the high occupants’ personalized thermal comfort levels efficiently. First, we use a meta-supervised learning algorithm to train an occupant's thermal preference model. Then, we train an ensemble neural network to predict the thermal states of the considered zone. In addition, the obtained ensemble networks can indicate the regions in the state and action spaces covered by the offline dataset. With the personalized thermal preference model updated via meta-testing, model-based RL is used to derive the optimal HVAC controller. Since the proposed algorithm only requires offline datasets and a few online thermal feedbacks for training, it contributes to a more practical deployment of the RL algorithm to HVAC systems. We use the ASHRAE database II to verify the effectiveness and advantage of the meta-learning algorithm for modeling different occupants’ thermal preferences. Numerical simulations on the EnergyPlus environment demonstrate that the proposed algorithm can guarantee personalized thermal preferences with a slight increase of power consumption of 1.91% compared with the model-based RL algorithm with on-policy data aggregation.
{"title":"Fast Human-in-the-Loop Control for HVAC Systems via Meta-Learning and Model-Based Offline Reinforcement Learning","authors":"Liangliang Chen;Fei Meng;Ying Zhang","doi":"10.1109/TSUSC.2023.3251302","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3251302","url":null,"abstract":"Reinforcement learning (RL) methods can be used to develop a controller for the heating, ventilation, and air conditioning (HVAC) systems that both saves energy and ensures high occupants’ thermal comfort levels. However, the existing works typically require on-policy data to train an RL agent, and the occupants’ personalized thermal preferences are not considered, which is limited in the real-world scenarios. This paper designs a high-performance model-based offline RL algorithm for personalized HVAC systems. The proposed algorithm can quickly adapt to different occupants’ thermal preferences with a few thermal feedbacks, guaranteeing the high occupants’ personalized thermal comfort levels efficiently. First, we use a meta-supervised learning algorithm to train an occupant's thermal preference model. Then, we train an ensemble neural network to predict the thermal states of the considered zone. In addition, the obtained ensemble networks can indicate the regions in the state and action spaces covered by the offline dataset. With the personalized thermal preference model updated via meta-testing, model-based RL is used to derive the optimal HVAC controller. Since the proposed algorithm only requires offline datasets and a few online thermal feedbacks for training, it contributes to a more practical deployment of the RL algorithm to HVAC systems. We use the ASHRAE database II to verify the effectiveness and advantage of the meta-learning algorithm for modeling different occupants’ thermal preferences. Numerical simulations on the EnergyPlus environment demonstrate that the proposed algorithm can guarantee personalized thermal preferences with a slight increase of power consumption of 1.91% compared with the model-based RL algorithm with on-policy data aggregation.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"504-521"},"PeriodicalIF":3.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50280316","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}
It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.
{"title":"Fine-Grained Online Energy Management of Edge Data Centers Using Per-Core Power Gating and Dynamic Voltage and Frequency Scaling","authors":"Shoulu Hou;Wei Ni;Kailan Zhao;Bo Cheng;Shuai Zhao;Zhiguo Wan;Xiulei Liu;Shiping Chen","doi":"10.1109/TSUSC.2023.3250487","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3250487","url":null,"abstract":"It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"522-536"},"PeriodicalIF":3.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50400592","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}
Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes’ abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.
{"title":"Defect Prediction via Tree-Based Encoding with Hybrid Granularity for Software Sustainability","authors":"Shaojian Qiu;Huihao Huang;Wenchao Jiang;Fanlong Zhang;Weilin Zhou","doi":"10.1109/TSUSC.2023.3248965","DOIUrl":"10.1109/TSUSC.2023.3248965","url":null,"abstract":"Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes’ abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"249-260"},"PeriodicalIF":3.9,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74652697","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 : 2023-02-17DOI: 10.1109/TSUSC.2023.3246087
Kamal Taha
For sustainable cyber-physical systems (CPS) security, proactive measures to cybersecurity need to be implemented instead of reactive measures. Towards this, we introduce in this paper a proactive methodology implemented in a system called IDI_CPS. It is based on the observation that CPS devices that have LAN-based network sharing (e.g., via Wi-Fi connections) need first to be clustered using some clustering criterion. Then, the influential and central devices in these clusters need to be identified to pay more attention to their file sharing. These influential devices may have network sharing with devices at the WAN level. Therefore, the influential devices at the WAN level that have network sharing with the influential devices in the clusters need also to be identified to pay more attention to their file sharing. We propose novel techniques for: (1) clustering the devices that have LAN-based network sharing using k-clique modeling, (2) employing clustering coefficient-based techniques for identifying the most influential device in each cluster, and (3) employing Independent Cascades model-based techniques for identifying the influential devices at the WAN level that have network sharing with the influential devices in the clusters. We experimentally evaluated our proposed system IDI_CPS and compared it with four comparable methods. Results showed marked improvement.
{"title":"Identifying and Protecting Cyber-Physical Systems’ Influential Devices for Sustainable Cybersecurity","authors":"Kamal Taha","doi":"10.1109/TSUSC.2023.3246087","DOIUrl":"10.1109/TSUSC.2023.3246087","url":null,"abstract":"For sustainable cyber-physical systems (CPS) security, proactive measures to cybersecurity need to be implemented instead of reactive measures. Towards this, we introduce in this paper a proactive methodology implemented in a system called IDI_CPS. It is based on the observation that CPS devices that have LAN-based network sharing (e.g., via Wi-Fi connections) need first to be clustered using some clustering criterion. Then, the influential and central devices in these clusters need to be identified to pay more attention to their file sharing. These influential devices may have network sharing with devices at the WAN level. Therefore, the influential devices at the WAN level that have network sharing with the influential devices in the clusters need also to be identified to pay more attention to their file sharing. We propose novel techniques for: (1) clustering the devices that have LAN-based network sharing using k-clique modeling, (2) employing clustering coefficient-based techniques for identifying the most influential device in each cluster, and (3) employing Independent Cascades model-based techniques for identifying the influential devices at the WAN level that have network sharing with the influential devices in the clusters. We experimentally evaluated our proposed system IDI_CPS and compared it with four comparable methods. Results showed marked improvement.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"614-626"},"PeriodicalIF":3.9,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87573728","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 : 2023-02-10DOI: 10.1109/TSUSC.2023.3244081
Athena Abdi;Armin Salimi-Badr
In this paper, an evolutionary-neuro-fuzzy-based task scheduling approach (ENF-S) to jointly optimize the main critical parameters of heterogeneous multi-core systems is proposed. This approach has two phases: first, the fuzzy neural network (FNN) is trained using a non-dominated sorting genetic algorithm (NSGA-II), considering the critical parameters of heterogeneous multi-core systems on a training data set consisting of different application graphs. These critical parameters are execution time, temperature, failure rate, and power consumption. The output of the trained FNN determines the criticality degree for various processing cores based on the system's current state. Next, the trained FNN is employed as an online scheduler to jointly optimize the critical objectives of multi-core systems at runtime. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous. The efficiency of ENF-S is investigated in various aspects including its joint optimization capability, appropriateness of generated fuzzy rules, comparison with related research, and its overhead analysis through several experiments on real-world and synthetic application graphs. Based on these experiments, our ENF-S outperforms the related studies in optimizing all design criteria. Its improvements over related methods are estimated ${19.21%}$ in execution time, ${13.07%}$ in temperature, ${25.09%}$ in failure rate, and ${13.16%}$ in power consumption, averagely.
{"title":"ENF-S: An Evolutionary-Neuro-Fuzzy Multi-Objective Task Scheduler for Heterogeneous Multi-Core Processors","authors":"Athena Abdi;Armin Salimi-Badr","doi":"10.1109/TSUSC.2023.3244081","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3244081","url":null,"abstract":"In this paper, an evolutionary-neuro-fuzzy-based task scheduling approach (ENF-S) to jointly optimize the main critical parameters of heterogeneous multi-core systems is proposed. This approach has two phases: first, the fuzzy neural network (FNN) is trained using a non-dominated sorting genetic algorithm (NSGA-II), considering the critical parameters of heterogeneous multi-core systems on a training data set consisting of different application graphs. These critical parameters are execution time, temperature, failure rate, and power consumption. The output of the trained FNN determines the \u0000<i>criticality degree</i>\u0000 for various processing cores based on the system's current state. Next, the trained FNN is employed as an online scheduler to jointly optimize the critical objectives of multi-core systems at runtime. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous. The efficiency of ENF-S is investigated in various aspects including its joint optimization capability, appropriateness of generated fuzzy rules, comparison with related research, and its overhead analysis through several experiments on real-world and synthetic application graphs. Based on these experiments, our ENF-S outperforms the related studies in optimizing all design criteria. Its improvements over related methods are estimated \u0000<inline-formula><tex-math>${19.21%}$</tex-math></inline-formula>\u0000 in execution time, \u0000<inline-formula><tex-math>${13.07%}$</tex-math></inline-formula>\u0000 in temperature, \u0000<inline-formula><tex-math>${25.09%}$</tex-math></inline-formula>\u0000 in failure rate, and \u0000<inline-formula><tex-math>${13.16%}$</tex-math></inline-formula>\u0000 in power consumption, averagely.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"479-491"},"PeriodicalIF":3.9,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50280162","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 : 2023-01-30DOI: 10.1109/TSUSC.2023.3240457
Yanqi Gong;Fei Hao;Liang Wang;Liang Zhao;Geyong Min
With the advent of 5G, Mobile Edge Computing (MEC), a promising computing paradigm sits closer to users than cloud computing, is being broadly used in various Internet of Things (IoT) applications, and achieve high-quality user experience. Task offloading, as a critical research issue in MEC, is playing an important role in optimizing computational resources and management. However, many tasks are executed dependent on the computational results of other tasks. Moreover, in the case of offloading tasks with other devices, it is often required to consider the success rate of offloading, since not all users are willing to lend their mobile devices to others for task execution. To address this challenge, by taking social relationships between users into account, this paper intends to combine computational resources of local devices and edge clouds and provide more flexible offloading and execution solutions, for achieving the efficient offloading of dependent tasks with the joint consideration of network latency and energy consumption. This paper develops a dependent task offloading strategy based on Bipartite Graph Matching. Extensive simulations are conducted for validating the effectiveness of our proposed strategy. Experimental results demonstrate that our proposed strategy can significantly minimize the overhead compared with other baseline strategies. In particular, the overhead is reduced 8.2%, compared with the strategy which consider the Device-to-Device (D2D) offloading only.
{"title":"A Socially-Aware Dependent Tasks Offloading Strategy in Mobile Edge Computing","authors":"Yanqi Gong;Fei Hao;Liang Wang;Liang Zhao;Geyong Min","doi":"10.1109/TSUSC.2023.3240457","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3240457","url":null,"abstract":"With the advent of 5G, Mobile Edge Computing (MEC), a promising computing paradigm sits closer to users than cloud computing, is being broadly used in various Internet of Things (IoT) applications, and achieve high-quality user experience. Task offloading, as a critical research issue in MEC, is playing an important role in optimizing computational resources and management. However, many tasks are executed dependent on the computational results of other tasks. Moreover, in the case of offloading tasks with other devices, it is often required to consider the success rate of offloading, since not all users are willing to lend their mobile devices to others for task execution. To address this challenge, by taking social relationships between users into account, this paper intends to combine computational resources of local devices and edge clouds and provide more flexible offloading and execution solutions, for achieving the efficient offloading of dependent tasks with the joint consideration of network latency and energy consumption. This paper develops a dependent task offloading strategy based on Bipartite Graph Matching. Extensive simulations are conducted for validating the effectiveness of our proposed strategy. Experimental results demonstrate that our proposed strategy can significantly minimize the overhead compared with other baseline strategies. In particular, the overhead is reduced 8.2%, compared with the strategy which consider the Device-to-Device (D2D) offloading only.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"328-342"},"PeriodicalIF":3.9,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50400591","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}
Cyber Threat Intelligence (CTI), which is knowledge of cyberspace threats gathered from security data, is critical in defending against cyberattacks.However, there is no open-source CTI dataset for security researchers to effectively apply enormous CTI information for security analysis in the field of threat intelligence, particularly in the field of Chinese threat intelligence. As a result, for network security research and development, this article constructed a Chinese CTI entity relationship dataset–CDTier, which includes: 1) A threat entity extraction dataset composed of 100 CTI reports, 3744 threat sentences and 4259 threat knowledge objects; 2) A dataset for entity relation extraction including 100 CTI reports, 2598 threat sentences and 2562 knowledge object relations. CDTier is, as far as we know, the first CTI dataset. On the CDTier, we trained 4 models for threat entity extraction and relation extraction using well-established and widely used deep learning methods in the NLP. The results showed that the model trained on CDTier extracts knowledge objects and their relationships described in threat intelligence more accurately. This significantly minimizes threat intelligence analysts’ work while assessing threat intelligence.
{"title":"CDTier: A Chinese Dataset of Threat Intelligence Entity Relationships","authors":"Yinghai Zhou;Yitong Ren;Ming Yi;Yanjun Xiao;Zhiyuan Tan;Nour Moustafa;Zhihong Tian","doi":"10.1109/TSUSC.2023.3240411","DOIUrl":"10.1109/TSUSC.2023.3240411","url":null,"abstract":"Cyber Threat Intelligence (CTI), which is knowledge of cyberspace threats gathered from security data, is critical in defending against cyberattacks.However, there is no open-source CTI dataset for security researchers to effectively apply enormous CTI information for security analysis in the field of threat intelligence, particularly in the field of Chinese threat intelligence. As a result, for network security research and development, this article constructed a Chinese CTI entity relationship dataset–CDTier, which includes: 1) A threat entity extraction dataset composed of 100 CTI reports, 3744 threat sentences and 4259 threat knowledge objects; 2) A dataset for entity relation extraction including 100 CTI reports, 2598 threat sentences and 2562 knowledge object relations. CDTier is, as far as we know, the first CTI dataset. On the CDTier, we trained 4 models for threat entity extraction and relation extraction using well-established and widely used deep learning methods in the NLP. The results showed that the model trained on CDTier extracts knowledge objects and their relationships described in threat intelligence more accurately. This significantly minimizes threat intelligence analysts’ work while assessing threat intelligence.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"627-638"},"PeriodicalIF":3.9,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78890157","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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