Network slicing is considered one of the key technologies in future telecommunication networks as it can split the physical network into a number of logical networks tailored to diverse purposes that allow users to access various services speedily. The fifth-generation (5G) mobile network can support a variety of applications by using network slicing. However, security (especially authentication) is a significant issue when users access the network slice-based services. Various authentication schemes are designed to secure access, and only a few offer cross-network slice authentication. The security analysis of existing cross-network authentication schemes shows they are vulnerable to several attacks such as device stolen, ephemeral secret leakage, violation of perfect forward secrecy, identity theft. Therefore, we propose an authentication mechanism that offers cross-network slice authentication and prevents all the aforementioned vulnerabilities. The security verification of the authentication mechanism is carried out informally and formally (ROR logic and Scyther tool) to ensure that it handles all the vulnerabilities. The comparison of empirical evaluation shows that the proposed scheme is least costly than its competitors. Java-based implementations of the proposed protocols imitate a real environment, showing that our proposed protocol maintains almost the same performance as state-of-the-art solutions while providing additional security features.
{"title":"An Enhanced Cross-Network-Slice Authentication Protocol for 5G","authors":"Awaneesh Kumar Yadav;Shalitha Wijethilaka;An Braeken;Manoj Misra;Madhusanka Liyanage","doi":"10.1109/TSUSC.2023.3283615","DOIUrl":"10.1109/TSUSC.2023.3283615","url":null,"abstract":"Network slicing is considered one of the key technologies in future telecommunication networks as it can split the physical network into a number of logical networks tailored to diverse purposes that allow users to access various services speedily. The fifth-generation (5G) mobile network can support a variety of applications by using network slicing. However, security (especially authentication) is a significant issue when users access the network slice-based services. Various authentication schemes are designed to secure access, and only a few offer cross-network slice authentication. The security analysis of existing cross-network authentication schemes shows they are vulnerable to several attacks such as device stolen, ephemeral secret leakage, violation of perfect forward secrecy, identity theft. Therefore, we propose an authentication mechanism that offers cross-network slice authentication and prevents all the aforementioned vulnerabilities. The security verification of the authentication mechanism is carried out informally and formally (ROR logic and Scyther tool) to ensure that it handles all the vulnerabilities. The comparison of empirical evaluation shows that the proposed scheme is least costly than its competitors. Java-based implementations of the proposed protocols imitate a real environment, showing that our proposed protocol maintains almost the same performance as state-of-the-art solutions while providing additional security features.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"555-573"},"PeriodicalIF":3.9,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80912702","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}
Blockchain is an important supporting technology for various sustainable systems. It relies on a number of distributed nodes running blockchain client software, which is responsible for some critical tasks, such as communicating with other nodes and generating new blocks. However, the quick evolution of blockchain technology brings crucial challenges to blockchain client design. After carefully examining existing blockchain client software, we have identified a critical weakness: Blockchain clients are weak in supporting live upgrades, resulting in a blockchain fork that incurs security concerns and risks. In this article, we propose Phoenix, a novel blockchain client design that is live upgradable. Phoenix uses blockchain service encapsulation to decouple blockchain services. Based on service encapsulation, we propose a live upgrade scheme that packs upgrade codes into blockchain transactions and uses a Just-In-Time engine to avoid service interruption. A parallel execution engine is developed to increase service efficiency. We evaluated Phoenix on a 51-node blockchain, and experimental results show that Phoenix outperforms existing solutions in overhead and upgrade latency.
{"title":"Phoenix: A Live Upgradable Blockchain Client","authors":"Chenmin Wang;Peng Li;Xuepeng Fan;Zaiyang Tang;Yulong Zeng;Kouichi Sakurai","doi":"10.1109/TSUSC.2023.3282586","DOIUrl":"10.1109/TSUSC.2023.3282586","url":null,"abstract":"Blockchain is an important supporting technology for various sustainable systems. It relies on a number of distributed nodes running blockchain client software, which is responsible for some critical tasks, such as communicating with other nodes and generating new blocks. However, the quick evolution of blockchain technology brings crucial challenges to blockchain client design. After carefully examining existing blockchain client software, we have identified a critical weakness: Blockchain clients are weak in supporting live upgrades, resulting in a blockchain fork that incurs security concerns and risks. In this article, we propose Phoenix, a novel blockchain client design that is live upgradable. Phoenix uses blockchain service encapsulation to decouple blockchain services. Based on service encapsulation, we propose a live upgrade scheme that packs upgrade codes into blockchain transactions and uses a Just-In-Time engine to avoid service interruption. A parallel execution engine is developed to increase service efficiency. We evaluated Phoenix on a 51-node blockchain, and experimental results show that Phoenix outperforms existing solutions in overhead and upgrade latency.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"703-714"},"PeriodicalIF":3.9,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74606354","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 : 2023-06-05DOI: 10.1109/TSUSC.2023.3279382
Gao Jintao;Li Zhanhuai;Sun Jian
The improvement of robustness and efficiency for multi-way equijoin query is challenging, no-matter for centralized database systems or distributed database systems. Due to lots of unnecessary data existing during query processing, these two metrics will be seriously reduced. If we can thoroughly prune unnecessary data in advance, the robustness and efficiency will be highly improved. However, the pruning power of current strategies, such as predicate push-down and algebraic equivalence, is limited. We present deepDP, a powerful, generalized, and efficient strategy for data pruning. deepDP builds multiple independent pruning spaces by generating longest transitive closures and applies appropriate data pruning strategy for each pruning space. For thoroughly pruning unnecessary data, deepDP employs $alpha cdot beta$ pruning strategy to clean each pruning space based on a newly designed statistic information-Hollow Range and re-shuffles the elements in all pruned spaces for maximizing robustness and efficiency benefits meanwhile minimizing the invasion. We implement deepDP in PostgreSQL but are not limited to it, and evaluate deepDP on TPC-H, JOB, and our synthesis benchmark–DHR. The experiment results show that compared to traditional data pruning strategy, deepDP can improve multi-way equijoin query on efficiency by 3.5x.
{"title":"Thorough Data Pruning for Join Query in Database System","authors":"Gao Jintao;Li Zhanhuai;Sun Jian","doi":"10.1109/TSUSC.2023.3279382","DOIUrl":"10.1109/TSUSC.2023.3279382","url":null,"abstract":"The improvement of robustness and efficiency for multi-way equijoin query is challenging, no-matter for centralized database systems or distributed database systems. Due to lots of unnecessary data existing during query processing, these two metrics will be seriously reduced. If we can thoroughly prune unnecessary data in advance, the robustness and efficiency will be highly improved. However, the pruning power of current strategies, such as predicate push-down and algebraic equivalence, is limited. We present deepDP, a powerful, generalized, and efficient strategy for data pruning. deepDP builds multiple independent pruning spaces by generating longest transitive closures and applies appropriate data pruning strategy for each pruning space. For thoroughly pruning unnecessary data, deepDP employs \u0000<inline-formula><tex-math>$alpha cdot beta$</tex-math></inline-formula>\u0000 pruning strategy to clean each pruning space based on a newly designed statistic information-Hollow Range and re-shuffles the elements in all pruned spaces for maximizing robustness and efficiency benefits meanwhile minimizing the invasion. We implement deepDP in PostgreSQL but are not limited to it, and evaluate deepDP on TPC-H, JOB, and our synthesis benchmark–DHR. The experiment results show that compared to traditional data pruning strategy, deepDP can improve multi-way equijoin query on efficiency by 3.5x.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"409-421"},"PeriodicalIF":3.9,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84036343","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 : 2023-06-01DOI: 10.1109/TSUSC.2023.3281886
Raj Anwit;Prasanta K. Jana;Mohammad S. Obaidat
In the edge-based Internet of Things (IoT) era, wireless sensor networks (WSNs) are the prime source for data collection. In such WSNs, mobile edge nodes such as mobile sinks (MSs) are the superior means to collect sensed data by visiting rendezvous points (RPs). However, WSNs are often obstacle-ridden, which creates hurdles to the movement of the MSs. Most of the existing path planning works dealing with obstacles do not address optimal and smooth path construction. In other words, they have not considered a) optimizing the number of RPs and constructing a feasible path and b) smoothing the constructed path by considering sharp edges and convexity of the obstacle perimeter. In this paper, we address all such issues and develop an efficient scheme for determining an optimal number of RPs using a greedy approach to the set-cover problem and optimized path construction, both in polynomial time. Then, we apply the modified BUG2 algorithm to construct an obstacle-free path, which is then smoothed using the concept of the Bezier curve. Extensive simulations show the superiority of our proposed scheme over the existing algorithms in terms of energy consumption, latency, and so on.
在基于边缘的物联网（IoT）时代，无线传感器网络（WSN）是数据收集的主要来源。在这种 WSN 中，移动汇（MS）等移动边缘节点是通过访问会合点（RP）收集传感数据的最佳手段。然而，WSN 通常障碍重重，这给 MS 的移动造成了障碍。现有的大多数路径规划工作都在处理障碍物，但并没有解决最佳和平滑路径的构建问题。换句话说，它们没有考虑 a) 优化 RP 的数量并构建可行路径，以及 b) 通过考虑障碍物周边的锐边和凸度来平滑所构建的路径。在本文中，我们解决了所有这些问题，并开发了一种高效的方案，利用贪婪法确定最佳 RP 数，从而在多项式时间内解决集合覆盖问题和优化路径构建问题。然后，我们采用改进的 BUG2 算法来构建无障碍路径，再利用贝塞尔曲线的概念对其进行平滑处理。大量仿真表明，我们提出的方案在能耗、延迟等方面都优于现有算法。
{"title":"Obstacle Adaptive Smooth Path Planning for Mobile Data Collector in the Internet of Things","authors":"Raj Anwit;Prasanta K. Jana;Mohammad S. Obaidat","doi":"10.1109/TSUSC.2023.3281886","DOIUrl":"10.1109/TSUSC.2023.3281886","url":null,"abstract":"In the edge-based Internet of Things (IoT) era, wireless sensor networks (WSNs) are the prime source for data collection. In such WSNs, mobile edge nodes such as mobile sinks (MSs) are the superior means to collect sensed data by visiting rendezvous points (RPs). However, WSNs are often obstacle-ridden, which creates hurdles to the movement of the MSs. Most of the existing path planning works dealing with obstacles do not address optimal and smooth path construction. In other words, they have not considered a) optimizing the number of RPs and constructing a feasible path and b) smoothing the constructed path by considering sharp edges and convexity of the obstacle perimeter. In this paper, we address all such issues and develop an efficient scheme for determining an optimal number of RPs using a greedy approach to the set-cover problem and optimized path construction, both in polynomial time. Then, we apply the modified BUG2 algorithm to construct an obstacle-free path, which is then smoothed using the concept of the Bezier curve. Extensive simulations show the superiority of our proposed scheme over the existing algorithms in terms of energy consumption, latency, and so on.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"727-738"},"PeriodicalIF":3.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75654418","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 : 2023-06-01DOI: 10.1109/TSUSC.2023.3282164
Mohsen Ansari;Sepideh Safari;Nezam Rohbani;Alireza Ejlali;Bashir M. Al-Hashimi
One of the essential requirements of embedded systems is a guaranteed level of reliability. In this regard, fault-tolerance techniques are broadly applied to these systems to enhance reliability. However, fault-tolerance techniques may increase power consumption due to their inherent redundancy. For this purpose, power management techniques are applied, along with fault-tolerance techniques, which generally prolong the system lifespan by decreasing the temperature and leading to an aging rate reduction. Yet, some power management techniques, such as Dynamic voltage and frequency scaling (DVFS), increase the transient fault rate and timing error. For this reason, heterogeneous multicore platforms have received much attention due to their ability to make a trade-off between power consumption and performance. Still, it is more complicated to map and schedule tasks in a heterogeneous multicore system. In this paper, for the first time, we propose a power management method for a heterogeneous multicore system that reduces power consumption and tolerates both transient and permanent faults through primary/backup technique while considering core-level power constraint, real-time constraint, and aging effect. Experimental evaluations demonstrate the efficiency of our proposed method in terms of reducing power consumption compared to the state-of-the-art schemes, together with guaranteeing reliability and considering the aging effect.
{"title":"Power-Efficient and Aging-Aware Primary/Backup Technique for Heterogeneous Embedded Systems","authors":"Mohsen Ansari;Sepideh Safari;Nezam Rohbani;Alireza Ejlali;Bashir M. Al-Hashimi","doi":"10.1109/TSUSC.2023.3282164","DOIUrl":"10.1109/TSUSC.2023.3282164","url":null,"abstract":"One of the essential requirements of embedded systems is a guaranteed level of reliability. In this regard, fault-tolerance techniques are broadly applied to these systems to enhance reliability. However, fault-tolerance techniques may increase power consumption due to their inherent redundancy. For this purpose, power management techniques are applied, along with fault-tolerance techniques, which generally prolong the system lifespan by decreasing the temperature and leading to an aging rate reduction. Yet, some power management techniques, such as Dynamic voltage and frequency scaling (DVFS), increase the transient fault rate and timing error. For this reason, heterogeneous multicore platforms have received much attention due to their ability to make a trade-off between power consumption and performance. Still, it is more complicated to map and schedule tasks in a heterogeneous multicore system. In this paper, for the first time, we propose a power management method for a heterogeneous multicore system that reduces power consumption and tolerates both transient and permanent faults through primary/backup technique while considering core-level power constraint, real-time constraint, and aging effect. Experimental evaluations demonstrate the efficiency of our proposed method in terms of reducing power consumption compared to the state-of-the-art schemes, together with guaranteeing reliability and considering the aging effect.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"715-726"},"PeriodicalIF":3.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72713993","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 : 2023-04-28DOI: 10.1109/TSUSC.2023.3271220
Song Wang;Tangyuan Zou;Weixin Zhao;Liang Liu
With the proposed ”carbon peaking” and ”carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents’ commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.
{"title":"Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network","authors":"Song Wang;Tangyuan Zou;Weixin Zhao;Liang Liu","doi":"10.1109/TSUSC.2023.3271220","DOIUrl":"10.1109/TSUSC.2023.3271220","url":null,"abstract":"With the proposed ”carbon peaking” and ”carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents’ commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"299-314"},"PeriodicalIF":3.9,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72619611","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}
Reducing carbon emission to improving the economy of fuel vehicle is one of the effective ways to achieve Carbon Neutrality. The Internet of Vehicles (IoV) is a developing technology for deep integration of the Internet of Things (IoT) and transportation. The Industrial Internet of Things (IIoT) can incorporate vehicle information to pinpoint vehicle carbon emissions and provide a foundation for the subsequent carbon-neutral decision-making process. To achieve the precision needs of IoT, however, more than conventional Global Navigation Satellite Systems (GNSS) are required. To achieve carbon emission detection, provide high-precision positioning, and provide a foundation for subsequent carbon-neutral decision-making, it is essential to design a carbon emission detection and positioning system with the capability of vehicle networking. The geographic proximity of edge Ultra Wide Band (UWB) nodes and the merging of various data sources are two methods we suggest employing in this study to increase location accuracy in IIoT situations. After carefully examining the positioning error of the single-edge node and the range error achieved in the UWB communication system, we choose a suitable filtering strategy to enhance single-node accuracy. Following the improvement of single-node accuracy, we fuse the location information of multiple edge nodes using a Weighted Least Squares algorithm in the spatial dimension; in the temporal dimension, we use Extended Kalman filtering to fuse the data over a period of time due to the temporal correlation of inter-node communication. Experimental results demonstrate that our co-localization method, which combines temporal and spatial information, achieves higher localization accuracy in comparison with previous work.
{"title":"Boosting Research for Carbon Neutral on Edge UWB Nodes Integration Communication Localization Technology of IoV","authors":"Ouhan Huang;Huanle Rao;Zhongyi Zhang;Renshu Gu;Hong Xu;Gangyong Jia","doi":"10.1109/TSUSC.2023.3266729","DOIUrl":"10.1109/TSUSC.2023.3266729","url":null,"abstract":"Reducing carbon emission to improving the economy of fuel vehicle is one of the effective ways to achieve Carbon Neutrality. The Internet of Vehicles (IoV) is a developing technology for deep integration of the Internet of Things (IoT) and transportation. The Industrial Internet of Things (IIoT) can incorporate vehicle information to pinpoint vehicle carbon emissions and provide a foundation for the subsequent carbon-neutral decision-making process. To achieve the precision needs of IoT, however, more than conventional Global Navigation Satellite Systems (GNSS) are required. To achieve carbon emission detection, provide high-precision positioning, and provide a foundation for subsequent carbon-neutral decision-making, it is essential to design a carbon emission detection and positioning system with the capability of vehicle networking. The geographic proximity of edge Ultra Wide Band (UWB) nodes and the merging of various data sources are two methods we suggest employing in this study to increase location accuracy in IIoT situations. After carefully examining the positioning error of the single-edge node and the range error achieved in the UWB communication system, we choose a suitable filtering strategy to enhance single-node accuracy. Following the improvement of single-node accuracy, we fuse the location information of multiple edge nodes using a Weighted Least Squares algorithm in the spatial dimension; in the temporal dimension, we use Extended Kalman filtering to fuse the data over a period of time due to the temporal correlation of inter-node communication. Experimental results demonstrate that our co-localization method, which combines temporal and spatial information, achieves higher localization accuracy in comparison with previous work.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"341-353"},"PeriodicalIF":3.9,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87377894","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 : 2023-04-10DOI: 10.1109/TSUSC.2023.3263172
Baixuan Wu;Zheng Xiao;Peiying Lin;Zhuo Tang;Kenli Li
Graph partitioning is one of the fundamental problems in many graph-based applications and systems. It enables the division of a graph into smaller sub-graphs for subsequent parallel processing, reducing the processing latency of the graph. The critical path of a graph is the logical path with the longest delay from input to output. The processing time of the graph mainly depends on the delay incurred by the critical path, independent of other paths with small delays. Therefore, it can reduce the processing time of the graph by protecting the critical path of the graph from partition. However, existing approaches to graph partitioning only focus on metrics such as minimum cut and partition balance. As a result, the critical paths of graphs may be destroyed in the partitioning procedure. To address this problem, we present a critical path awareness approach, namely path-metis, to protect the critical paths and alleviate the processing latency after graph partitioning. In path-metis, two efficient strategies, including Slack and critical path fix strategies, are introduced. The Slack strategy, which incorporates critical path information into the weights of DAG, is used as pre-processing before traditional multi-level partitioning methods, like Metis. Then, for the generated partitioning scheme, the critical path fix strategy is proposed to further protect critical paths from being cut. We demonstrate the effectiveness of our approach on both real and synthetic datasets. From the experimental results, compared to Metis, our method improves critical path performance by 17.70%.
{"title":"Critical Path Awareness Techniques for Large-Scale Graph Partitioning","authors":"Baixuan Wu;Zheng Xiao;Peiying Lin;Zhuo Tang;Kenli Li","doi":"10.1109/TSUSC.2023.3263172","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3263172","url":null,"abstract":"Graph partitioning is one of the fundamental problems in many graph-based applications and systems. It enables the division of a graph into smaller sub-graphs for subsequent parallel processing, reducing the processing latency of the graph. The critical path of a graph is the logical path with the longest delay from input to output. The processing time of the graph mainly depends on the delay incurred by the critical path, independent of other paths with small delays. Therefore, it can reduce the processing time of the graph by protecting the critical path of the graph from partition. However, existing approaches to graph partitioning only focus on metrics such as minimum cut and partition balance. As a result, the critical paths of graphs may be destroyed in the partitioning procedure. To address this problem, we present a critical path awareness approach, namely path-metis, to protect the critical paths and alleviate the processing latency after graph partitioning. In path-metis, two efficient strategies, including Slack and critical path fix strategies, are introduced. The Slack strategy, which incorporates critical path information into the weights of DAG, is used as pre-processing before traditional multi-level partitioning methods, like Metis. Then, for the generated partitioning scheme, the critical path fix strategy is proposed to further protect critical paths from being cut. We demonstrate the effectiveness of our approach on both real and synthetic datasets. From the experimental results, compared to Metis, our method improves critical path performance by 17.70%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"412-422"},"PeriodicalIF":3.9,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50280160","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}
As energy-intensive entities, data centers are associated with significant environmental impacts, making their sustainability a subject of growing interest in recent years. In this article, we revisit data center sustainability and propose a forward-looking vision for improving data center sustainability. We argue that data center sustainability encompasses more than just energy efficiency and must be evaluated and optimized through a multi-faceted approach. To this end, we first present an overview of the sustainability metrics from five aspects. After that, we demonstrate the sustainability status of the latest data centers utilizing publicly available data center sustainability ratings. Furthermore, we examine the evolution of data center sustainability standards in Singapore to highlight several trending features. Based on the analysis, we identify several key elements of sustainable data centers. We then propose the Cognitive Digital Twin (CDT) architecture, which incorporates a digital twin engine for system-wide simulation and a decision engine for optimal control to improve data center sustainability. A case study is performed to optimize the chiller plant efficiency of a production data center in Singapore. The results demonstrate that the CDT can improve chiller plant energy efficiency by 5%, indicating around 140 metric tons of annual carbon emission savings.
{"title":"Data Center Sustainability: Revisits and Outlooks","authors":"Zhiwei Cao;Xin Zhou;Xiangyu Wu;Zhaomeng Zhu;Tracy Liu;Jeffery Neng;Yonggang Wen","doi":"10.1109/TSUSC.2023.3281583","DOIUrl":"10.1109/TSUSC.2023.3281583","url":null,"abstract":"As energy-intensive entities, data centers are associated with significant environmental impacts, making their sustainability a subject of growing interest in recent years. In this article, we revisit data center sustainability and propose a forward-looking vision for improving data center sustainability. We argue that data center sustainability encompasses more than just energy efficiency and must be evaluated and optimized through a multi-faceted approach. To this end, we first present an overview of the sustainability metrics from five aspects. After that, we demonstrate the sustainability status of the latest data centers utilizing publicly available data center sustainability ratings. Furthermore, we examine the evolution of data center sustainability standards in Singapore to highlight several trending features. Based on the analysis, we identify several key elements of sustainable data centers. We then propose the Cognitive Digital Twin (CDT) architecture, which incorporates a digital twin engine for system-wide simulation and a decision engine for optimal control to improve data center sustainability. A case study is performed to optimize the chiller plant efficiency of a production data center in Singapore. The results demonstrate that the CDT can improve chiller plant energy efficiency by 5%, indicating around 140 metric tons of annual carbon emission savings.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"236-248"},"PeriodicalIF":3.9,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88809223","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 : 2023-03-31DOI: 10.1109/TSUSC.2023.3263510
Xinghao Yang;Yongshun Gong;Weifeng Liu;James Bailey;Dacheng Tao;Wei Liu
Deep learning models are known immensely brittle to adversarial text examples. Existing text adversarial attack strategies can be roughly divided into character-level, word-level, and sentence-level attacks. Despite the success brought by recent text attack methods, how to induce misclassification with minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency is still a challenge. In this paper, we devise a Bigram and Unigram-based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constrain the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making minimal word modifications compared with competitive methods.
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