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2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)最新文献

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Passenger Payment Willingness Prediction by Static and Dynamic Multi-dimensional Ticket Attributes Fusion 基于静态与动态多维客票属性融合的乘客支付意愿预测
Pub Date : 2021-12-01 DOI: 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.
对于世界各地的航空公司来说,在平衡收入和销售时,机票定价一直是一个具有挑战性的问题,因为机票通常会打折,以适应一个适合市场的价格水平。在本文中,我们将乘客支付意愿(PPW)建模问题转化为top-$K$推荐问题,该问题通过充分考虑同行航空公司的机票折扣历史和多维机票属性(即乘客购买能力)来推荐机票折扣比率列表。我们提出了一种新的深度模型,称为“NCL”,它将N-Beats、图卷积神经网络(GCN)和LSTM结合在一起,来模拟门票折扣的时间变化和多维门票属性之间的复杂关系。具体来说,首先,N-Beats对门票折扣历史序列进行整合。然后,将多维票证属性分为动态和静态两类,构造静态属性的属性图,利用GCN从中提取特征;然后,利用LSTM对动态属性进行时间特征融合。最后,NCL集成了上述所有功能,并预测未来的门票折扣。实验证实,以ACC@1为例,NCL的预测准确率在60%以上。
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引用次数: 0
Improving HPC System Throughput and Response Time using Memory Disaggregation 利用内存分解提高高性能计算系统的吞吐量和响应时间
Pub Date : 2021-12-01 DOI: 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.
HPC集群具有成本效益高、易于理解和可扩展的特点,但是计算节点之间的严格边界可能导致计算和内存资源的利用率低下。HPC作业在每个核心的内存消耗方面可能会有数量级的变化。因此,即使将系统配置为容纳普通和大容量节点,系统与计划作业的内存需求之间的不匹配也可能导致内存和计算资源的低效使用。分解内存最近被提出作为一种通过灵活地在集群节点间分配内存容量来缓解这个问题的方法。提出了一种大规模评估分解内存作业调度器的仿真方法,并为Slurm资源管理器引入了一种新的分解感知作业分配策略。我们的结果表明,根据系统和提交作业之间的不平衡,使用分解的内存,在总内存配置最多减少33%的情况下,可以在系统上实现类似的吞吐量和作业响应时间。
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引用次数: 3
A New Code-based Blind Signature in Rank Metric 一种新的基于码的秩度量盲签名
Pub Date : 2021-12-01 DOI: 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.
盲签名被广泛应用于互联网银行、电子投票和区块链。本文在Durandal签名算法的基础上,提出了基于等级度量的第一个基于码的盲签名方案。证明了该方案能够满足盲性,并在随机oracle模型下给出了该方案的安全性证明。我们的方案具有签名大小短的优点。给出了该方案的实例,实验结果表明该方案在实际应用中是可行的。
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引用次数: 0
TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud TSC-ECFA:一种边缘云可信服务组合方案
Pub Date : 2021-12-01 DOI: 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.
为了从边缘云的大规模web服务中选择满足用户需求和高性能的组合方案,本文提出了一种边缘云可信服务组合优化方案TSC-ECFA,该方案将捕食策略应用于萤火虫算法(FA),将输入输出相同的服务划分为一类,减少组合次数,寻找可行的解决方案。利用余切混沌理论生成初始萤火虫种群并干扰个体的位置,提高了蚁群算法的整体搜索效率。本文还对遗传算法的阶跃因子和吸引力公式进行了改进,充分发挥了算法早期阶跃因子的检测能力,平衡了局部搜索和全局搜索。考虑到服务的QoS属性值容易被篡改,为了保证QoS属性值的防篡改和可靠性,使用区块链来存储QoS属性值。最后通过仿真实验比较了三种算法的迭代次数和优化效果。实验结果表明,TSC-ECFA的综合性能优于其他算法。
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引用次数: 1
LinkStream: A Liquidity Modeling System on Large-Scale Video Stream in Oilfield LinkStream:油田大规模视频流的流动性建模系统
Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00103
Hao Yuan, Q. Ma, Zhe Hu, Xiaoxiang Li, Xu Wang, Zheng Yang
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.
本文介绍了面向油田开发的基于多视频流的流动性建模系统LinkStream。LinkStream结合了多种技术来解决计算能力和网络延迟方面的几个问题。首先,该系统采用边缘中心架构和基于时空相关的裁剪,大大降低了计算能力要求和网络成本,能够在有限的边缘设备上实时分析大规模视频流。其次,设计了一套流动性模型来描述油田的流动性状况。最后,利用目标跟踪技术设计了针对油田中唯一油管目标的计数算法。我们已经在伊拉克的一个油田部署了LinkStream。LinkStream可以在200多个视频流上执行实时推理,资源开销可接受。
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引用次数: 0
WiRN: Real-Time and Lightweight Gesture Detection System on Edge Device WiRN:基于边缘设备的实时轻量级手势检测系统
Pub Date : 2021-12-01 DOI: 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.
基于WiFi信号的手势检测不需要用户携带额外的设备,并且在检测过程中可以更好的保护用户的隐私,因此受到了广泛的关注。然而,现有的工作没有考虑平台的实际部署,忽略了对平台计算能力的要求和实际推理延迟,导致许多方法不适合边缘设备的使用。在本文中,我们提出了一种WiFi手势检测系统,命名为WiRN,该系统完全部署在边缘设备上,不需要额外的计算设备参与。在WiRN中,我们针对相关问题提出了解决方案。首先,为了解决由于相位过于敏感导致不同场景下多个相位差相差较大的问题,提高系统的鲁棒性和通用性,我们提出了一种基于多天线的相位差选择算法,寻找最合适的相位差。然后,我们将不同维度的幅相差进行融合,得到更细粒度的输入数据,解决边缘设备计算能力限制导致无法部署复杂神经网络充分提取特征的问题,使输入数据包含更丰富的特征信息。这样,我们第一次从数据源上提高了网络分类的准确率。我们通过一系列实验对系统进行了评估,结果表明,在满足边缘设备实时计算的前提下,我们使用最简单的双层神经网络,达到了与现有复杂网络相同的精度。在不同环境下的识别准确率达到93%左右。
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引用次数: 0
ECRaft: A Raft Based Consensus Protocol for Highly Available and Reliable Erasure-Coded Storage Systems 基于Raft的高可用和可靠的擦除编码存储系统共识协议
Pub Date : 2021-12-01 DOI: 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 %.
擦除编码冗余是一种低存储开销的容错方法。它只存储数据片段和奇偶校验片段,而不是整个集群的完整数据。擦除编码数据的写过程分为异步和同步两种。对于同步写过程,数据写入服务器时进行编码。执行该过程的常用方法需要确认数据的每个代码片段存储在不同的服务器中,以保持最佳的容错性。这种方法在可用性方面表现不佳,而且也无法实现良好的性能,因为服务器的任何故障都会很快干扰写过程。一些基于Paxos和Raft的共识协议,如RS- Paxos和CRaft,可以通过为系统提供容错能力来解决上述问题。但是,RS-Paxos无法达到Paxos那样的活跃性。在没有足够的健康服务器时,CRaft仍然采用完全的数据冗余来保持与Raft相同的活动性。因此,为了解决同步擦除编码数据写入过程中的可用性问题,我们提出了一种基于Raft的新型协议ECRaft。当数据分片与校验分片的比值大于1时,采用erasure-coded冗余。它也可以达到筏子一样的活力。使用状态机清除,可以将存储冗余减少到典型的擦除编码存储系统所能达到的程度。我们建立了一个基于ECRaft的键值存储来评估它。在我们的实验中,与使用完全条目复制的CRaft相比,ECRaft可以节省63%的存储空间,提高28.2%的写吞吐量,减少19%的写延迟。
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引用次数: 2
Spring Buddy: A Self-Adaptive Elastic Memory Management Scheme for Efficient Concurrent Allocation/Deallocation in Cloud Computing Systems Spring Buddy:一种云计算系统中高效并发分配/回收的自适应弹性内存管理方案
Pub Date : 2021-12-01 DOI: 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%。
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引用次数: 1
A Novel iBeacon Deployment Scheme for Indoor Pedestrian Positioning 一种新的室内行人定位iBeacon部署方案
Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00007
Wenping Yu, Jianzhong Zhang, Junyu Cai, Jingdong Xu
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.
随着人们对位置服务(LBS)需求的多样化,基于智能手机的室内行人定位成为学术界和产业界的研究热点。由于室内环境的复杂性和智能手机惯性传感器的精度不足,获得定位精度稳定、环境适应性好的室内行人定位方案仍然是一个挑战。针对这一问题,本文提出了一种新的室内行人iBeacon部署方案。首先介绍了室内环境复杂多样平面结构的抽象方法。其次,通过对行人航位推算(PDR)方法的误差分析,推导出定位精度与行人行走距离之间的映射函数;最后,本文提出了一种iBeacon部署方案的生成算法,该方案既满足了lbs的定位精度要求,又大大减少了iBeacon的部署数量。对三种不同平面结构的真实室内环境进行了实验分析。结果表明,本文提出的iBeacon部署方案可以帮助基于pdr的室内行人定位解决方案在室内环境适应性方面实现突破。
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引用次数: 0
B-AUT: A Universal Architecture for Batch RFID Tags Authentication 批量RFID标签认证的通用架构
Pub Date : 2021-12-01 DOI: 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.
RFID标签认证一直是一个关键但具有挑战性的问题,因为只检查EPC容易受到假冒攻击。过去的研究探索了由于标签制造缺陷而产生的独特的反向散射信号特征,但在实践中未能支持批量标签的同时认证,这对于大规模RFID应用(例如仓库库存)至关重要。在本文中,我们提出了一种通用的架构,即B-AUT,可以在同一EPC下同时对多个标签进行身份验证并精确定位,该架构完全兼容Gen2标准,适用于现有工作中提出的几乎所有标签的硬件指纹。基于我们的新算法,B-AUT的工作流程分为三个部分。首先,利用提取的模糊指纹和EPC对原始数据进行聚类;其次,提取标签的细粒度指纹进行真伪验证,得到无效聚类;第三,我们利用定位方法将无效聚类与可疑标签进行匹配,并进一步进行小规模重新验证以确定假冒标签。我们已经实现了B-AUT的原型,并在极端情况下对其进行了评估。实验结果表明,B-AUT可以保持与单独认证几乎相同的认证精度,并将时间开销减少43.3%。并且,无论标签总量或标签型号如何,精确定位准确率均可达到92.8%。
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引用次数: 3
期刊
2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)
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