Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00019
Botong Chang, Jiahe Zhang, Chi Harold Liu
Ticket pricing is always a challenging problem for world-wide airline companies when balancing their revenues and sales, where tickets are often discounted to adapt to a marketable price level. In this paper, we transform the problem of modeling Passenger Payment Willingness (PPW) into a top-$K$ recommendation problem, where a list of ticket discounted ratios is recommended by fully considering ticket discount histories of peer airline companies and multi-dimensional ticket attributes, i.e., passenger purchasing capability. We propose a novel deep model, called “NCL”, which integrates N-Beats, a Graph Convolutional Neural Network (GCN) and an LSTM together to model temporal variations of ticket discounts and complex relationships among multi-dimensional ticket attributes. Specifically, first, the ticket discount historical sequence is integrated by N-Beats. Then, multi-dimensional ticket attributes are divided into dynamic and static categories, where an attribute graph of static attributes is constructed, and a GCN is leveraged to extract features from it. After, LSTM is used to perform temporal feature fusion on the dynamic attributes. Finally, NCL integrates features from all the above and predicts future ticket discounts. Experiments confirm that the prediction accuracy of NCL is more than 60% in terms of ACC@1.
{"title":"Passenger Payment Willingness Prediction by Static and Dynamic Multi-dimensional Ticket Attributes Fusion","authors":"Botong Chang, Jiahe Zhang, Chi Harold Liu","doi":"10.1109/ICPADS53394.2021.00019","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00019","url":null,"abstract":"Ticket pricing is always a challenging problem for world-wide airline companies when balancing their revenues and sales, where tickets are often discounted to adapt to a marketable price level. In this paper, we transform the problem of modeling Passenger Payment Willingness (PPW) into a top-$K$ recommendation problem, where a list of ticket discounted ratios is recommended by fully considering ticket discount histories of peer airline companies and multi-dimensional ticket attributes, i.e., passenger purchasing capability. We propose a novel deep model, called “NCL”, which integrates N-Beats, a Graph Convolutional Neural Network (GCN) and an LSTM together to model temporal variations of ticket discounts and complex relationships among multi-dimensional ticket attributes. Specifically, first, the ticket discount historical sequence is integrated by N-Beats. Then, multi-dimensional ticket attributes are divided into dynamic and static categories, where an attribute graph of static attributes is constructed, and a GCN is leveraged to extract features from it. After, LSTM is used to perform temporal feature fusion on the dynamic attributes. Finally, NCL integrates features from all the above and predicts future ticket discounts. Experiments confirm that the prediction accuracy of NCL is more than 60% in terms of ACC@1.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00041
F. V. Zacarias, P. Carpenter, V. Petrucci
HPC clusters are cost-effective, well understood, and scalable, but the rigid boundaries between compute nodes may lead to poor utilization of compute and memory resources. HPC jobs may vary, by orders of magnitude, in memory consumption per core. Thus, even when the system is provisioned to accommodate normal and large capacity nodes, a mismatch between the system and the memory demands of the scheduled jobs can lead to inefficient usage of both memory and compute resources. Disaggregated memory has recently been proposed as a way to mitigate this problem by flexibly allocating memory capacity across cluster nodes. This paper presents a simulation approach for at-scale evaluation of job schedulers with disaggregated memories and it introduces a new disaggregated-aware job allocation policy for the Slurm resource manager. Our results show that using disaggregated memories, depending on the imbalance between the system and the submitted jobs, a similar throughput and job response time can be achieved on a system with up to 33% less total memory provisioning.
{"title":"Improving HPC System Throughput and Response Time using Memory Disaggregation","authors":"F. V. Zacarias, P. Carpenter, V. Petrucci","doi":"10.1109/ICPADS53394.2021.00041","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00041","url":null,"abstract":"HPC clusters are cost-effective, well understood, and scalable, but the rigid boundaries between compute nodes may lead to poor utilization of compute and memory resources. HPC jobs may vary, by orders of magnitude, in memory consumption per core. Thus, even when the system is provisioned to accommodate normal and large capacity nodes, a mismatch between the system and the memory demands of the scheduled jobs can lead to inefficient usage of both memory and compute resources. Disaggregated memory has recently been proposed as a way to mitigate this problem by flexibly allocating memory capacity across cluster nodes. This paper presents a simulation approach for at-scale evaluation of job schedulers with disaggregated memories and it introduces a new disaggregated-aware job allocation policy for the Slurm resource manager. Our results show that using disaggregated memories, depending on the imbalance between the system and the submitted jobs, a similar throughput and job response time can be achieved on a system with up to 33% less total memory provisioning.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121717968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00049
Yanhong Qi, Xindong Liu, Li-Ping Wang
Blind signatures are widely used in Internet banking, e-voting and blockchains. In this paper, we propose the first code-based blind signature scheme under rank metric which is based on a variation of Durandal signature algorithm. We prove that our scheme can satisfy blindness and provide the security proof of our scheme in the random oracle model. Our scheme enjoys the advantages of short signature size. We give the instantiation of our scheme and the experimental results show that our scheme is feasible in practical applications.
{"title":"A New Code-based Blind Signature in Rank Metric","authors":"Yanhong Qi, Xindong Liu, Li-Ping Wang","doi":"10.1109/ICPADS53394.2021.00049","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00049","url":null,"abstract":"Blind signatures are widely used in Internet banking, e-voting and blockchains. In this paper, we propose the first code-based blind signature scheme under rank metric which is based on a variation of Durandal signature algorithm. We prove that our scheme can satisfy blindness and provide the security proof of our scheme in the random oracle model. Our scheme enjoys the advantages of short signature size. We give the instantiation of our scheme and the experimental results show that our scheme is feasible in practical applications.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124684621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00013
Yuzhang Jiang, Xiaolong Xu, Kunda Lin, Weihua Duan
In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms.
{"title":"TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud","authors":"Yuzhang Jiang, Xiaolong Xu, Kunda Lin, Weihua Duan","doi":"10.1109/ICPADS53394.2021.00013","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00013","url":null,"abstract":"In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126008505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article introduces LinkStream, a liquidity modeling system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity models to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.
{"title":"LinkStream: A Liquidity Modeling System on Large-Scale Video Stream in Oilfield","authors":"Hao Yuan, Q. Ma, Zhe Hu, Xiaoxiang Li, Xu Wang, Zheng Yang","doi":"10.1109/ICPADS53394.2021.00103","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00103","url":null,"abstract":"This article introduces LinkStream, a liquidity modeling system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity models to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130445802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00026
Qing Yang, Tianzhang Xing, Zhiping Jiang, Xinhua Fu, Jingyi He
Gesture detection based on WiFi signals does not require users to carry additional equipment, and can better protect the privacy of users during the detection process, so it has received widespread attention. However, the existing work does not consider the actual deployment of the platform, and ignores the requirements for the computing power of the platform and the actual reasoning delay, resulting in many methods that are not suitable for the use of edge devices. In this paper, we propose a WiFi gesture detection system, named WiRN, which is fully deployed on edge devices and does not require the participation of additional computing devices. In WiRN, We have proposed solutions to related problems. First of all, in order to solve the problem of large differences in multiple phase differences obtained in different scenarios due to over-sensitive phases and to improve the robustness and universality of the system, we propose a multi-antenna-based phase difference selection algorithm to find the most suitable phase difference. Then, we fuse the amplitude and phase difference of different dimensions and obtain more fine-grained input data to solve the problem of the inability to deploy complex neural networks to fully extract features due to the limitation of edge device computing power, so that the input data contains richer feature information. In this way, for the first time, we will improve the accuracy of network classification from the data source. We evaluated the system through a series of experiments, and the results showed that under the premise of satisfying the real-time calculation of edge devices, we achieved the same accuracy as the existing complex network by using the simplest two-layer neural network. The recognition accuracy of about 93% is achieved in different environments.
{"title":"WiRN: Real-Time and Lightweight Gesture Detection System on Edge Device","authors":"Qing Yang, Tianzhang Xing, Zhiping Jiang, Xinhua Fu, Jingyi He","doi":"10.1109/ICPADS53394.2021.00026","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00026","url":null,"abstract":"Gesture detection based on WiFi signals does not require users to carry additional equipment, and can better protect the privacy of users during the detection process, so it has received widespread attention. However, the existing work does not consider the actual deployment of the platform, and ignores the requirements for the computing power of the platform and the actual reasoning delay, resulting in many methods that are not suitable for the use of edge devices. In this paper, we propose a WiFi gesture detection system, named WiRN, which is fully deployed on edge devices and does not require the participation of additional computing devices. In WiRN, We have proposed solutions to related problems. First of all, in order to solve the problem of large differences in multiple phase differences obtained in different scenarios due to over-sensitive phases and to improve the robustness and universality of the system, we propose a multi-antenna-based phase difference selection algorithm to find the most suitable phase difference. Then, we fuse the amplitude and phase difference of different dimensions and obtain more fine-grained input data to solve the problem of the inability to deploy complex neural networks to fully extract features due to the limitation of edge device computing power, so that the input data contains richer feature information. In this way, for the first time, we will improve the accuracy of network classification from the data source. We evaluated the system through a series of experiments, and the results showed that under the premise of satisfying the real-time calculation of edge devices, we achieved the same accuracy as the existing complex network by using the simplest two-layer neural network. The recognition accuracy of about 93% is achieved in different environments.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00094
Mingwei Xu, Yu Zhou, Yuanyuan Qiao, Kai Xu, Yu Wang, Jie Yang
Erasure-coded redundancy is a fault-tolerant method with low-cost storage overhead. It only stores data fragments and parity fragments rather than full data across the cluster. The write process of erasure-coded data can be asynchronous or synchronous. For synchronous write process, data are encoded when written to servers. The common method doing the process needs to confirm that each coded-fragment of the data is stored in a different server to maintain the best fault tolerance. This method underperforms in terms of availability, and also fails to achieve good performance because any failure of servers will shortly disturb the write process. Some consensus protocols such as RS- Paxos and CRaft, which are based on Paxos and Raft, can solve above problems by providing fault-tolerant ability for systems. However, RS-Paxos cannot achieve the same liveness as Paxos. CRaft still adopts full data redundancy to keep the same liveness as Raft when there are not enough healthy servers. Therefore, to solve the availability problem during synchronous erasure-coded data write process, we present a novel protocol ECRaft based on Raft. It always uses erasure-coded redundancy when the ratio of erasure-coded data fragments to parity fragments is bigger than 1. It also can reach the same liveness as Raft. With state machine purge, storage redundancy can be reduced to the extent that typical erasure-coded storage systems can achieve. We build a key-value store based on ECRaft to evaluate it. In our experiments, compared with CRaft using complete-entry replication, ECRaft can save 63 % of storage, increase write throughput by 28.2 %, and reduce write latency by 19 %.
{"title":"ECRaft: A Raft Based Consensus Protocol for Highly Available and Reliable Erasure-Coded Storage Systems","authors":"Mingwei Xu, Yu Zhou, Yuanyuan Qiao, Kai Xu, Yu Wang, Jie Yang","doi":"10.1109/ICPADS53394.2021.00094","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00094","url":null,"abstract":"Erasure-coded redundancy is a fault-tolerant method with low-cost storage overhead. It only stores data fragments and parity fragments rather than full data across the cluster. The write process of erasure-coded data can be asynchronous or synchronous. For synchronous write process, data are encoded when written to servers. The common method doing the process needs to confirm that each coded-fragment of the data is stored in a different server to maintain the best fault tolerance. This method underperforms in terms of availability, and also fails to achieve good performance because any failure of servers will shortly disturb the write process. Some consensus protocols such as RS- Paxos and CRaft, which are based on Paxos and Raft, can solve above problems by providing fault-tolerant ability for systems. However, RS-Paxos cannot achieve the same liveness as Paxos. CRaft still adopts full data redundancy to keep the same liveness as Raft when there are not enough healthy servers. Therefore, to solve the availability problem during synchronous erasure-coded data write process, we present a novel protocol ECRaft based on Raft. It always uses erasure-coded redundancy when the ratio of erasure-coded data fragments to parity fragments is bigger than 1. It also can reach the same liveness as Raft. With state machine purge, storage redundancy can be reduced to the extent that typical erasure-coded storage systems can achieve. We build a key-value store based on ECRaft to evaluate it. In our experiments, compared with CRaft using complete-entry replication, ECRaft can save 63 % of storage, increase write throughput by 28.2 %, and reduce write latency by 19 %.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115221355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00056
Yihui Lu, Weidong Liu, Chentao Wu, Jia Wang, Xiaoming Gao, Jie Li, M. Guo
Within the cloud computing scenario, each server usually carries multiple service processes, which intensifies the concurrency pressure of the system. As a result, the process of memory management during page allocation and deallocation becomes a significant bottleneck. Although several methods such as Buddy System and Inverse Buddy System (iBuddy) have been proposed to improve the performance of memory management, they cannot adapt to the highly concurrent environment of cloud computing, because they either force the memory allocation/deallocation requests to be serialized or bring extra fragmentation. To address the above problem, we propose Spring Buddy, which improves the concurrency of both memory allocation and deallocation and avoids unnecessary fragmentation. It can detect the changes of system- and process-level memory request patterns and dynamically adjust the organization of page frames. Inventively, Spring Buddy uses the spring core layer to provide both concurrent response and resource aggregation capability which is adapted to the system's concurrency pressure, and also uses the spring lazy layer to further mitigate the system resource contention through process behavior prediction. To demonstrate the effectiveness of Spring Buddy, we implement it in the Linux kernel. The results demonstrate that Spring Buddy can reduce memory allocation latency by 71.47 % and deallocation latency by 93.20% on average compared to the existing methods.
在云计算场景中，每台服务器通常承载多个业务进程，这加大了系统的并发压力。因此，页面分配和回收过程中的内存管理过程成为一个重要的瓶颈。虽然已经提出了Buddy System和Inverse Buddy System (iBuddy)等几种方法来提高内存管理的性能，但它们不能适应云计算的高并发环境，因为它们要么强制内存分配/回收请求被序列化，要么带来额外的碎片。为了解决上述问题，我们提出了Spring Buddy，它提高了内存分配和回收的并发性，并避免了不必要的碎片。它可以检测系统级和进程级内存请求模式的变化，并动态调整页面框架的组织。Spring Buddy利用Spring核心层提供适应系统并发压力的并发响应和资源聚合能力，并利用Spring lazy层通过进程行为预测进一步缓解系统资源争用。为了演示Spring Buddy的有效性，我们在Linux内核中实现它。结果表明，与现有方法相比，Spring Buddy可将内存分配延迟平均降低71.47%，内存分配延迟平均降低93.20%。
{"title":"Spring Buddy: A Self-Adaptive Elastic Memory Management Scheme for Efficient Concurrent Allocation/Deallocation in Cloud Computing Systems","authors":"Yihui Lu, Weidong Liu, Chentao Wu, Jia Wang, Xiaoming Gao, Jie Li, M. Guo","doi":"10.1109/ICPADS53394.2021.00056","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00056","url":null,"abstract":"Within the cloud computing scenario, each server usually carries multiple service processes, which intensifies the concurrency pressure of the system. As a result, the process of memory management during page allocation and deallocation becomes a significant bottleneck. Although several methods such as Buddy System and Inverse Buddy System (iBuddy) have been proposed to improve the performance of memory management, they cannot adapt to the highly concurrent environment of cloud computing, because they either force the memory allocation/deallocation requests to be serialized or bring extra fragmentation. To address the above problem, we propose Spring Buddy, which improves the concurrency of both memory allocation and deallocation and avoids unnecessary fragmentation. It can detect the changes of system- and process-level memory request patterns and dynamically adjust the organization of page frames. Inventively, Spring Buddy uses the spring core layer to provide both concurrent response and resource aggregation capability which is adapted to the system's concurrency pressure, and also uses the spring lazy layer to further mitigate the system resource contention through process behavior prediction. To demonstrate the effectiveness of Spring Buddy, we implement it in the Linux kernel. The results demonstrate that Spring Buddy can reduce memory allocation latency by 71.47 % and deallocation latency by 93.20% on average compared to the existing methods.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131539996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environments and the insufficient accuracy of smartphone inertial sensors, it is still challenging to get an indoor pedestrian positioning solution with stable positioning accuracy and good environmental adaptability. Aiming at this problem, a novel iBeacon deployment scheme for indoor pedestrian is proposed in this paper. Firstly, we introduce an abstract method for complex and diverse plane structures of the indoor environments. Secondly, a mapping function between positioning accuracy and pedestrian walking distance is deduced by error analysis of pedestrian dead-reckoning (PDR) method. Finally, this paper proposes a generation algorithm of iBeacon deployment scheme which not only satisfies the positioning accuracy requirement of LBSs but also greatly reduces the number of deployed iBeacons. We have carried out experimental analysis on three different plane structures of real indoor environments. And it turns out that the proposed iBeacon deployment scheme can help PDR-based indoor pedestrian positioning solutions to achieve breakthrough in indoor environment adaptability.
{"title":"A Novel iBeacon Deployment Scheme for Indoor Pedestrian Positioning","authors":"Wenping Yu, Jianzhong Zhang, Junyu Cai, Jingdong Xu","doi":"10.1109/ICPADS53394.2021.00007","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00007","url":null,"abstract":"With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environments and the insufficient accuracy of smartphone inertial sensors, it is still challenging to get an indoor pedestrian positioning solution with stable positioning accuracy and good environmental adaptability. Aiming at this problem, a novel iBeacon deployment scheme for indoor pedestrian is proposed in this paper. Firstly, we introduce an abstract method for complex and diverse plane structures of the indoor environments. Secondly, a mapping function between positioning accuracy and pedestrian walking distance is deduced by error analysis of pedestrian dead-reckoning (PDR) method. Finally, this paper proposes a generation algorithm of iBeacon deployment scheme which not only satisfies the positioning accuracy requirement of LBSs but also greatly reduces the number of deployed iBeacons. We have carried out experimental analysis on three different plane structures of real indoor environments. And it turns out that the proposed iBeacon deployment scheme can help PDR-based indoor pedestrian positioning solutions to achieve breakthrough in indoor environment adaptability.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00100
Yinan Zhu, Chunhui Duan, Xuan Ding, Zheng Yang
RFID tags authentication is always a critical but challenging problem because only checking the EPC is vulnerable to counterfeiting attacks. Past works explore the unique backscat-ter signal features induced by tags' manufacturing imperfection as fingerprints, but fail to support simultaneous authentication for a batch of tags in practice, which is vital for large-scale RFID applications (e.g., warehouse inventory). In this paper, we present a universal architecture, namely B-AUT, to simultaneously authenticate multiple tags even with the same EPC and pinpoint them, which is fully compatible with Gen2 standard and applicable to almost all tags' hardware fingerprints proposed in existing works. The workflow of B-AUT is threefold based on our novel algorithms. First, the extracted fuzzy fingerprint and EPC are jointly exploited to cluster raw data. Second, we extract the tags' fine-grained fingerprints for genuineness validation and obtain the invalid clusters. Third, we harness localization methods to match the invalid cluster to dubious tags and further conduct small-scale re-validation to pinpoint the counterfeit tags. We have implemented a prototype of B-AUT and evaluated it in extreme cases. Experiment results demonstrate that B-AUT can maintain nearly the same authentication accuracy as that of separate authentication and reduce the time overhead by 43.3%. Moreover, the pinpointing accuracy can reach as high as 92.8%, regardless of tags' total quantities or tag models.
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