2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)最新文献_第7页

Lightweight Mobile Devices Indoor Location Based on Image Database 基于图像数据库的轻型移动设备室内定位

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00038

Ran Gao, Yanchao Zhao, Maoxing Tang

{"title":"Lightweight Mobile Devices Indoor Location Based on Image Database","authors":"Ran Gao, Yanchao Zhao, Maoxing Tang","doi":"10.1109/ICPADS51040.2020.00038","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00038","url":null,"abstract":"Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloud-based scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate on-device image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124250355","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}

引用次数: 1

Using Configuration Semantic Features and Machine Learning Algorithms to Predict Build Result in Cloud-Based Container Environment 在基于云的容器环境中使用配置语义特征和机器学习算法预测构建结果

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00042

Yiwen Wu, Yang Zhang, Junsheng Chang, Bo Ding, Tao Wang, Huaimin Wang

引用次数: 0

Enabling Generic Verifiable Aggregate Query on Blockchain Systems 在区块链系统上启用通用可验证聚合查询

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00066

Yanchao Zhu, Zhao Zhang, Cheqing Jin, Aoying Zhou

{"title":"Enabling Generic Verifiable Aggregate Query on Blockchain Systems","authors":"Yanchao Zhu, Zhao Zhang, Cheqing Jin, Aoying Zhou","doi":"10.1109/ICPADS51040.2020.00066","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00066","url":null,"abstract":"Currently, users in a blockchain system must maintain all the data on the blockchain and query the data locally to ensure the integrity of the query results. However, since data is updated in an append-only way, resulting in a huge amount of data, it will take considerable maintenance costs to users. In this paper, we present an approach to support verifiable aggregate queries on blockchain systems that alleviates both storage and computing costs for users, while ensuring the integrity of the query results. We design an accumulator-based authenticated data structure (ADS) that supports verifiable multidimensional aggregate queries (i.e., aggregate queries with multiple selection predicates). The structure is built for each block, based on which verifiable multidimensional aggregate queries within a single block or involving multiple blocks are supported. We further optimize the performance by merging ADSs on different blocks to reduce the verification time at the client side and reduce the verification object (VO) size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed approach.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132301751","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}

引用次数: 4

A Learning-based Dynamic Load Balancing Approach for Microservice Systems in Multi-cloud Environment 多云环境下基于学习的微服务系统动态负载平衡方法

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00052

Jieqi Cui, Pengfei Chen, Guangba Yu

{"title":"A Learning-based Dynamic Load Balancing Approach for Microservice Systems in Multi-cloud Environment","authors":"Jieqi Cui, Pengfei Chen, Guangba Yu","doi":"10.1109/ICPADS51040.2020.00052","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00052","url":null,"abstract":"Multi-cloud environment has become common since companies manage to prevent cloud vendor lock-in for security and cost concerns. Meanwhile, the microservice architecture is often considered for its flexibility. Combining multi-cloud with microservice, the problem of routing requests among all possible microservice instances in multi-cloud environment arises. This paper presents a learning-based approach to route requests in order to balance the load. In our approach, the performance of microservice is modeled explicitly through machine learning models. The model can derive the response time from request volume, route decision, and other cloud metrics. Then the balanced route decision is obtained from optimizing the model with Bayesian Optimization. With this approach, the request route decision can adjust to dynamic runtime metrics instead of remaining static for all different circumstances. Explicit performance modeling avoids searching on an actual microservice system which is time-consuming. Experiments show that our approach reduces average response time by 10% at least.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129160927","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}

引用次数: 6

Efficient Edge Service Migration in Mobile Edge Computing 移动边缘计算中的高效边缘业务迁移

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00098

Zeng Zeng, Shihao Li, Weiwei Miao, Lei Wei, Chengling Jiang, Chuanjun Wang, Mingxuan Zhang

{"title":"Efficient Edge Service Migration in Mobile Edge Computing","authors":"Zeng Zeng, Shihao Li, Weiwei Miao, Lei Wei, Chengling Jiang, Chuanjun Wang, Mingxuan Zhang","doi":"10.1109/ICPADS51040.2020.00098","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00098","url":null,"abstract":"Edge computing is one of the emerging technologies aiming to enable timely computation at the network edge. With virtualization technologies, the role of the traditional edge providers is separated into two: edge infrastructure providers (EIPs), who manage the physical edge infrastructure, and edge service providers (ESPs), who purchase slices of physical resources (e.g., CPU, bandwidth, memory space, disk storage) from EIPs and then cache service entities to offer their own value-added services to end users. These value-added services are also called virtual network function or VNF. As we know, edge computing environments are dynamic, and the requirements of edge service for computing resources usually fluctuate over time. Thus, when the demand of a VNF cannot be satisfied, we need to design the strategies for migrating the VNF so as to meet its demand and retain the network performance. In this paper, we concentrate on migrating VNFs efficiently (MV), such that the migration can meet the bandwidth requirement for data transmission. We prove that MV is NP-complete. We present several exact and heuristic solutions to tackle it. Extensive simulations demonstrate that the proposed heuristics are efficient and effective.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084464","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}

引用次数: 1

An Efficient and Scalable Sparse Polynomial Multiplication Accelerator for LAC on FPGA

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00059

Jipeng Zhang, Zhe Liu, Hao Yang, Junhao Huang, Weibin Wu

{"title":"An Efficient and Scalable Sparse Polynomial Multiplication Accelerator for LAC on FPGA","authors":"Jipeng Zhang, Zhe Liu, Hao Yang, Junhao Huang, Weibin Wu","doi":"10.1109/ICPADS51040.2020.00059","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00059","url":null,"abstract":"LAC, a Ring-LWE based scheme, has shortlisted for the second round evaluation of the National Institute of Standards and Technology Post-Quantum Cryptography (NIST-PQC) Standardization. FPGAs are widely used to design accelerators for cryptographic schemes, especially in resource-constrained scenarios, such as IoT. Sparse Polynomial Multiplication (SPM) is the most compute-intensive routine in LAC. Designing an accelerator for SPM on FPGA can significantly improve the performance of LAC. However, as far as we know, there are currently no works related to the hardware implementation of SPM for LAC. In this paper, the proposed efficient and scalable SPM accelerator fills this gap. More concretely, we firstly develop the Dual-For-Loop-Parallel (DFLP) technique to optimize the accelerator's parallel design. This technique can achieve 2x performance improvement compared with the previous works. Secondly, we design a hardware-friendly modular reduction algorithm for the modulus 251. Our method not only saves hardware resources but also improves performance. Then, we launch a detailed analysis and optimization of the pipeline design, achieving a frequency improvement of up to 34%. Finally, our design is scalable, and we can achieve various performance-area trade-offs through parameter $p$. Our results demonstrate that the proposed design can achieve a very considerable performance improvement with moderate hardware area costs. For example, our medium-scale architecture for LAC-128 takes only 783 LUTs, 432 FFs, 5BRAMs, and no DSP on an Artix-7 FPGA and can complete LAC's polynomial multiplication in 8512 cycles at a frequency of 202MHz.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123172549","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}

引用次数: 0

Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters 异构集群上基于通信感知的LU分解负载均衡

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00017

Lucas Leandro Nesi, L. Schnorr, Arnaud Legrand

{"title":"Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters","authors":"Lucas Leandro Nesi, L. Schnorr, Arnaud Legrand","doi":"10.1109/ICPADS51040.2020.00017","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00017","url":null,"abstract":"Supercomputers are designed to be as homogeneous as possible but it is common that a few nodes exhibit variable performance capabilities due to processor manufacturing. It is also common to find partitions equipped with different types of accelerators. Data distribution over heterogeneous nodes is very challenging but essential to exploit all resources efficiently. In this article, we build upon task-based runtimes' flexibility of managing data to study the interplay between static communication-aware data distribution strategies and dynamic scheduling of the linear algebra LU factorization over heterogeneous sets of hybrid nodes. We propose two techniques derived from the state-of-the-art $1mathrm{D}times 1mathrm{D}$ data distributions. First, to use fewer computing nodes towards the end to better match performance bounds and save computing power. Second, to carefully move a few blocks between nodes to optimize even further the load balancing among nodes. We also demonstrate how $1mathrm{D}times 1mathrm{D}$ data distributions, tailored for heterogeneous nodes, can scale better with homogeneous clusters than classical block-cyclic distributions. Validation is carried out both in real and in simulated environments under homogeneous and heterogeneous platforms, demonstrating compelling performance improvements.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117212153","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}

引用次数: 4

OOOPS: An Innovative Tool for IO Workload Management on Supercomputers OOOPS:超级计算机IO工作负载管理的创新工具

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00069

Lei Huang, Si Liu

引用次数: 8

A Novel Classification Model to Predict Batch Job Failures in Co-located Cloud 一种新的批作业失败预测分类模型

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00080

Yurui Li, Weiwei Lin, Keqin Li, J. Wang, Fagui Liu, Jie Liu

{"title":"A Novel Classification Model to Predict Batch Job Failures in Co-located Cloud","authors":"Yurui Li, Weiwei Lin, Keqin Li, J. Wang, Fagui Liu, Jie Liu","doi":"10.1109/ICPADS51040.2020.00080","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00080","url":null,"abstract":"Nowadays, cloud co-location is often used for data centers to improve the utilization of computing resources. However, batch jobs in a Co-location Datacenter (CLD) are vulnerable to failures due to the competition for limited resources with online service jobs. Such failed batch jobs would be rescheduled and failed repeatedly, resulting in the waste of computing resources and instability of the computing clusters. Therefore, we propose a method to accurately predict the potential failures of batch jobs for CLD. The core of the proposed method is STLF (SMOTE Tomek and LightGBM [5] Framework), which is divided into three parts. First, we use the co-feature extraction method to generate Co-located Feature Dataset (CLFD). Then SMOTE Tomek is used to oversampling the CLFD to ensure that the classifier can learn more minority features. Finally, we use LightGBM classifier to predict batch jobs' failure. The performance experiments conducted on the Ali Trace 2018 dataset show that our proposed STLF significantly outperforms the existing popular classifiers in terms of the ROC curve, the area under the ROC curve (AUC), precision, and recall.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701443","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}

引用次数: 1

Exploring Data Correlation between Feature Pairs for Generating Constraint-based Adversarial Examples 探索特征对之间的数据相关性以生成基于约束的对抗示例

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2020-12-01 DOI: 10.1109/ICPADS51040.2020.00064

Yunzhe Tian, Yingdi Wang, Endong Tong, Wenjia Niu, Liang Chang, Qi Alfred Chen, Gang Li, Jiqiang Liu

{"title":"Exploring Data Correlation between Feature Pairs for Generating Constraint-based Adversarial Examples","authors":"Yunzhe Tian, Yingdi Wang, Endong Tong, Wenjia Niu, Liang Chang, Qi Alfred Chen, Gang Li, Jiqiang Liu","doi":"10.1109/ICPADS51040.2020.00064","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00064","url":null,"abstract":"Adversarial example (AE), an input that is modified slightly to cause a machine learning system to produce erroneous outputs, has seen significant studies recently. Unfortunately, the fine data perturbation of AE ignores to keep potential data correlations between feature pairs. Thus, such AE will be easily filtered by configuring data correlations as basic filtering rules. In this paper, avoiding not to be filtered as well as causing false classification, an advanced robust AE generation attack is proposed. We first define four basic data correlations called strict linear constraint, approximate linear constraint, addition boundary constraint and zero multiplication constraint. Then, based on embedding multiple data correlations into one constraint matrix from the Pearson analysis, our approach can enable a Hadamard product of the constraint matrix and the sign of gradient matrix to craft perturbations, keeping consistent data correlations. Experimental results on intrusion detection system (IDS) indicate: 1) Nearly all AEs from original IFGSM are invalid by filtering according to basic data correlations; 2) In our method, AEs against a targeted DNN-based classifier can achieve an attack success rate of 99%, with transfer attack ability of 94% average success rate to attack other different mainstream classifiers.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127894428","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}

引用次数: 6