2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)最新文献

AI-based Robust Convex Relaxations for Supporting Diverse QoS in Next-Generation Wireless Systems 基于人工智能的鲁棒凸松弛支持下一代无线系统的多样化QoS

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00014

Steve Chan, M. Krunz, Bob Griffin

{"title":"AI-based Robust Convex Relaxations for Supporting Diverse QoS in Next-Generation Wireless Systems","authors":"Steve Chan, M. Krunz, Bob Griffin","doi":"10.1109/icdcsw53096.2021.00014","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00014","url":null,"abstract":"Supporting diverse Quality of Service (QoS) requirements in 5G and beyond wireless systems often involves solving a succession of convex optimization problems, with varied approaches to optimally resolve each problem. Even when the input set is specifically designed/architected to segue to a convex paradigm, the resultant output set may still turn out to be nonconvex, thereby necessitating a transformation to a convex optimization problem via certain relaxation techniques. This transformation in itself may spawn yet other nonconvex optimization problems, highlighting the need/opportunity to utilize a Robust Convex Relaxation (RCR) framework. In this paper, we explore a particular class of Convolutional Neural Networks (CNNs), namely Deep Convolutional Generative Adversarial Network (DCGANs), to solve not only the QoS-related convex optimization problems but also to leverage the same RCR mechanism for tuning its own hyperparameters. This approach gives rise to various technical challenges. For example, Particle Swarm Optimization (PSO) is often used for hyperparameter reduction/tuning. When implemented on a DCGAN, PSO requires converting continuous/discontinuous hyperparameters to discrete values, which may result in premature stagnation of particles at local optima. The involved implementation mechanics, such as increasing the inertial weighting, may spawn yet other convex optimization problems. We introduce a RCR framework that capitalizes upon the feed-forward structure of the “You Only Look Once” (YOLO)- based DCGAN. Specifically, we use a squeezed Deep Convolutional-YOLO-Generative Adversarial Network (DC-YOLO-GAN), hereinafter referred to as a Modified Squeezed YOLO v3 Implementation (MSY3I), combined with convex relaxation adversarial training to improve the bound tightening for each successive neural network layer and to better facilitate the global optimization via a specific numerical stability implementation within MSY3I.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121122811","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

Workshop Committee 车间委员会

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00006

引用次数: 0

Title Page i 第1页

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00001

引用次数: 0

Backup Placement in WSNs in the Network Management Distributed Setting 网络管理分布式环境下wsn中的备份放置

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00015

Gal Oren, Leonid Barenboim

{"title":"Backup Placement in WSNs in the Network Management Distributed Setting","authors":"Gal Oren, Leonid Barenboim","doi":"10.1109/icdcsw53096.2021.00015","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00015","url":null,"abstract":"Sensor nodes are inherently a cheap piece of hardware – due to the common need to use many of them over a large area – and usually contain a small amount of RAM and flash memory, which are insufficient in case of high degree of data sampling. An overloaded sensor can harm the data integrity, or even completely reject incoming messages. The problem gets even worse when data should be received from many nodes, as missing data becomes a more common phenomenon as deployed WSNs grow in scale. As a solution, we consider the Backup Placement problem in networks in the network management distributed setting: Given a network graph G = (V,E), the goal of each vertex v ∈ V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. In cases of an overflow, our Distributed Adaptive Clustering algorithm (D-ACR) reconfigures the network, by adaptively and hierarchically re-clustering parts of it, based on the rate of incoming data packages in order to minimize the energy-consumption, and prevent premature death of nodes. In the network management setting of a grid, we proved that a backup placement of load at most O(1) can be maintained within O(log2 n) update time, and an O(n log2 n) energy consumption using the constructed ACR tree.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122020689","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

Towards Energy-efficient Federated Edge Intelligence for IoT Networks 面向物联网网络的节能联邦边缘智能

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00016

Qu Wang, Yong Xiao, Huixiang Zhu, Zijian Sun, Yingyu Li, Xiaohu Ge

{"title":"Towards Energy-efficient Federated Edge Intelligence for IoT Networks","authors":"Qu Wang, Yong Xiao, Huixiang Zhu, Zijian Sun, Yingyu Li, Xiaohu Ge","doi":"10.1109/icdcsw53096.2021.00016","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00016","url":null,"abstract":"Federated edge intelligence (FEI) is an emerging framework that implements federated learning (FL)-based learning solutions in an edge networking and computing system. It has attracted significant interest due to its potential to enable machine learning (ML)-based smart services and applications in the next-generation wireless systems. Despite its potential, the environmental impact of implementing energy-consuming ML-based solutions in a large networking system has been considered as one of the major challenges for the sustainability of future digital networking systems. This paper proposes energy-efficient FEI (EE-FEI), an energy-efficient framework, that jointly optimizes multiple key parameters to minimize the overall energy consumption of an FEI-supported Internet of Things (IoT) network. We establish models to quantify the relationship between the total energy consumption of FEI and the key parameters including the number of edge servers, the number of local model training rounds, and the number of global coordination rounds. We formulate the energy consumption minimization problem and prove its approximation problem is biconvex. Alternate Convex Search (ACS) algorithm for solving the key parameters to minimize the energy consumption of an FEI system has been used. Finally, we evaluate our theoretical results using a hardware prototype. Numerical results have shown that EE-FEI can significantly reduce the energy consumption of FEI systems by 49.8%.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128401135","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: 10

VPN-nonVPN Traffic Classification Using Deep Reinforced Naive Bayes and Fuzzy K-means Clustering 基于深度增强朴素贝叶斯和模糊k均值聚类的vpn -非vpn流量分类

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00008

Abhishek Gupta

{"title":"VPN-nonVPN Traffic Classification Using Deep Reinforced Naive Bayes and Fuzzy K-means Clustering","authors":"Abhishek Gupta","doi":"10.1109/icdcsw53096.2021.00008","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00008","url":null,"abstract":"This paper addresses one of the attack methods where threat actors break into secure networks through virtual private networks (VPN) to launch tunneling based encrypted attacks, obfuscated advanced persistent threats and malware. The paper proposes Naive Bayes augmented with deep reinforcement learning (DRL) and fuzzy k-means clustering to classify VPN and non-VPN data. The proposed method is validated on the publicly available UNB-CIC VPN non-VPN dataset and shows an accuracy comparable to other state-of-the-art machine learning algorithms for traffic characterization. The proposed approach for traffic characterization of traffic classes (e.g., FTP and P2P) and application identification (e.g., Netflix and Amazon Prime) is analyzed for traffic-class detection efficiency and accuracy, and the ability to distinguish between VPN and non-VPN network traffic. The experimental results reveal that an optimum network traffic classification is achieved for ‘NB+DRL’ approach, where DRL is used to reinforce the NB classification model through iterative policy evaluation and improvement and achieves recall of 0.96 in traffic categorization and 0.95 in application identification. This is further improved with fuzzy k-means clustering to reduce computational costs for encrypted network traffic classification using ‘NB+DRL+fuzzy k-means clustering’, where the class partitions based on k-means converge to a local minimum based on dissimilarity measures for given packet features. The test set F1 scores of 0.973 and 0.965 are achieved for traffic characterization and application identification, respectively.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121647506","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

Message from the Workshop Co-Chairs 讲习班联合主席致辞

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/WOWMOM.2005.64

R. Molva

引用次数: 0

Effect-Centric Networking Effect-Centric网络

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00017

Hang Liu, Xianfu Chen

引用次数: 0

Towards Understanding the Adaptation Space of AI-Assisted Data Protection for Video Analytics at the Edge 了解边缘视频分析中人工智能辅助数据保护的适应空间

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00009

Clemens Lachner, Z. Mann, S. Dustdar

{"title":"Towards Understanding the Adaptation Space of AI-Assisted Data Protection for Video Analytics at the Edge","authors":"Clemens Lachner, Z. Mann, S. Dustdar","doi":"10.1109/icdcsw53096.2021.00009","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00009","url":null,"abstract":"Edge computing facilitates the deployment of distributed AI applications, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, such mechanisms also entail threats regarding privacy and security, for example if the video contains identifiable persons. Therefore, adequate data protection is an increasing concern in video analytics. AI-assisted data protection mechanisms, such as face blurring, can help, but are often computationally expensive. Additionally, the heterogeneous hardware of end devices and the time-varying load on edge services need to be considered. Therefore, such systems need to adapt to react to changes during their operation, ensuring that conflicting requirements on data protection, performance, and accuracy are addressed in the best possible way. Sound adaptation decisions require an understanding of the adaptation options and their impact on different quality attributes. In this paper, we identify factors that can be adapted in AI-assisted data protection for video analytics using the example of a face blurring pipeline. We measure the impact of these factors using a heterogeneous edge computing hardware testbed. The results show a large and complex adaptation space, with varied impacts on data protection, performance, and accuracy.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525975","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

Joint Offloading and Resource Allocation of UAV-assisted Mobile Edge Computing with Delay Constraints 时延约束下无人机辅助移动边缘计算联合卸载与资源分配

2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2021-07-01 DOI: 10.1109/icdcsw53096.2021.00011

Tiao Tan, Ming Zhao, Yusen Zhu, Z. Zeng

{"title":"Joint Offloading and Resource Allocation of UAV-assisted Mobile Edge Computing with Delay Constraints","authors":"Tiao Tan, Ming Zhao, Yusen Zhu, Z. Zeng","doi":"10.1109/icdcsw53096.2021.00011","DOIUrl":"https://doi.org/10.1109/icdcsw53096.2021.00011","url":null,"abstract":"Due to the increase of mobile applications and their users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Motivated by this, we consider a UAV-assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an unmanned aerial vehicle (UAV). Unmanned aerial vehicles can provide users with a wide range of communication and certain computing power. It can not only process the tasks received by users but also unload the tasks to the edge server or the cloud server. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources so as to minimize the overall energy consumption and the costs of computing and maximum delay. In order to solve the joint optimization problem, we propose an efficient USS algorithm, which includes UAV location optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user task compared with other four unloading decisions such as traditional cloud computing which only uses the edge server.","PeriodicalId":296648,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122802397","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