Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00067
Zhijun Li, Yongrui Chen
Today’s wireless networks have become increasingly heterogenous, mobile and dense. To satisfy the rising demands of ubiquitous connections, billions of multi-radio gateways have to be deployed, inevitably incurring high deployment cost and extra traffic overhead. Recent advances on Cross-Technology Communication (CTC) have shown its ability to avoid these drawbacks. However, the state-of-the-art CTCs from Bluetooth to WiFi, two of the most popular wireless techniques, still suffer from low data-rate (e.g., 3.1Kbps), which severely restricts their applicability. We present BlueFi, the first physical-layer CTC (PHY-CTC) from Bluetooth Low Energy (BLE) to WiFi, which enables high throughput, bidirectional and parallel transmissions between BLE and WiFi via spectral analysis. The key observation is that commodity WiFi chipsets can operate in the spectral analysis mode, in which WiFi can recognize specific BLE signal waveforms in frequency domain at symbol-level granularity. Leveraging this feature, we manufacture desired waveforms by choosing frame payload at BLE side, and observe spectral patterns at WiFi side. To achieve bidirectional links, we design a PHY-CTC method from WiFi to BLE based on signal emulation. We implement our prototype on USRP (with 802.11g PHY) and commodity BLE devices. Extensive evaluations show that BlueFi can achieve 120Kbps per link from BLE to WiFi with more than 95% frame reception ratio, over 38x faster than state-of-the-art CTCs. Moreover, BlueFi can support 9 wireless links in parallel, leading to the total throughput over 1Mbps.
{"title":"BlueFi: Physical-layer Cross-Technology Communication from Bluetooth to WiFi","authors":"Zhijun Li, Yongrui Chen","doi":"10.1109/ICDCS47774.2020.00067","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00067","url":null,"abstract":"Today’s wireless networks have become increasingly heterogenous, mobile and dense. To satisfy the rising demands of ubiquitous connections, billions of multi-radio gateways have to be deployed, inevitably incurring high deployment cost and extra traffic overhead. Recent advances on Cross-Technology Communication (CTC) have shown its ability to avoid these drawbacks. However, the state-of-the-art CTCs from Bluetooth to WiFi, two of the most popular wireless techniques, still suffer from low data-rate (e.g., 3.1Kbps), which severely restricts their applicability. We present BlueFi, the first physical-layer CTC (PHY-CTC) from Bluetooth Low Energy (BLE) to WiFi, which enables high throughput, bidirectional and parallel transmissions between BLE and WiFi via spectral analysis. The key observation is that commodity WiFi chipsets can operate in the spectral analysis mode, in which WiFi can recognize specific BLE signal waveforms in frequency domain at symbol-level granularity. Leveraging this feature, we manufacture desired waveforms by choosing frame payload at BLE side, and observe spectral patterns at WiFi side. To achieve bidirectional links, we design a PHY-CTC method from WiFi to BLE based on signal emulation. We implement our prototype on USRP (with 802.11g PHY) and commodity BLE devices. Extensive evaluations show that BlueFi can achieve 120Kbps per link from BLE to WiFi with more than 95% frame reception ratio, over 38x faster than state-of-the-art CTCs. Moreover, BlueFi can support 9 wireless links in parallel, leading to the total throughput over 1Mbps.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"85 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030114","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00184
Nandish Chattopadhyay, Ritabrata Maiti, A. Chattopadhyay
In the data-driven world, emerging technologies like the Internet of Things (IoT) and other crowd-sourced data sources like mobile devices etc. generate a tremendous volume of decentralized data that needs to be analyzed for obtaining useful insights, necessary for reliable decision making. Although the overall data is rich, contributors of such kind of data are reluctant to share their own data due to serious concerns regarding protection of their privacy; while those interested in harvesting the data are constrained by the limited computational resources available with each participant. In this paper, we propose an end-to-end algorithm that puts in coalescence the mechanism of learning collaboratively in a decentralized fashion, using Federated Learning, while preserving differential privacy of each participating client, which are typically conceived as resource-constrained edge devices. We have developed the proposed infrastructure and analyzed its performance from the standpoint of a machine learning task using standard metrics. We observed that the collaborative learning framework actually increases prediction capabilities in comparison to a centrally trained model (by 1-2%), without having to share data amongst the participants, while strong guarantees on privacy (ϵ, δ) can be provided with some compromise on performance (about 2-4%). Additionally, quantization of the model for deployment on edge devices do not degrade its capability, whilst enhancing the overall system efficiency.
{"title":"Deploy-able Privacy Preserving Collaborative ML","authors":"Nandish Chattopadhyay, Ritabrata Maiti, A. Chattopadhyay","doi":"10.1109/ICDCS47774.2020.00184","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00184","url":null,"abstract":"In the data-driven world, emerging technologies like the Internet of Things (IoT) and other crowd-sourced data sources like mobile devices etc. generate a tremendous volume of decentralized data that needs to be analyzed for obtaining useful insights, necessary for reliable decision making. Although the overall data is rich, contributors of such kind of data are reluctant to share their own data due to serious concerns regarding protection of their privacy; while those interested in harvesting the data are constrained by the limited computational resources available with each participant. In this paper, we propose an end-to-end algorithm that puts in coalescence the mechanism of learning collaboratively in a decentralized fashion, using Federated Learning, while preserving differential privacy of each participating client, which are typically conceived as resource-constrained edge devices. We have developed the proposed infrastructure and analyzed its performance from the standpoint of a machine learning task using standard metrics. We observed that the collaborative learning framework actually increases prediction capabilities in comparison to a centrally trained model (by 1-2%), without having to share data amongst the participants, while strong guarantees on privacy (ϵ, δ) can be provided with some compromise on performance (about 2-4%). Additionally, quantization of the model for deployment on edge devices do not degrade its capability, whilst enhancing the overall system efficiency.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268944","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00069
Han Zhao, Weihao Cui, Quan Chen, Jingwen Leng, Kai Yu, Deze Zeng, Chao Li, M. Guo
While deep neural network (DNN) models are often trained on GPUs, many companies and research institutes build GPU clusters that are shared by different groups. On such GPU cluster, DNN training jobs also require CPU cores to run pre-processing, gradient synchronization. Our investigation shows that the number of cores allocated to a training job significantly impact its performance. To this end, we characterize representative deep learning models on their requirement for CPU cores under different GPU resource configurations, and study the sensitivity of these models to other CPU-side shared resources. Based on the characterization, we propose CODA, a scheduling system that is comprised of an adaptive CPU allocator, a real-time contention eliminator, and a multi-array job scheduler. Experimental results show that CODA improves GPU utilization by 20.8% on average without increasing the queuing time of CPU jobs.
{"title":"CODA: Improving Resource Utilization by Slimming and Co-locating DNN and CPU Jobs","authors":"Han Zhao, Weihao Cui, Quan Chen, Jingwen Leng, Kai Yu, Deze Zeng, Chao Li, M. Guo","doi":"10.1109/ICDCS47774.2020.00069","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00069","url":null,"abstract":"While deep neural network (DNN) models are often trained on GPUs, many companies and research institutes build GPU clusters that are shared by different groups. On such GPU cluster, DNN training jobs also require CPU cores to run pre-processing, gradient synchronization. Our investigation shows that the number of cores allocated to a training job significantly impact its performance. To this end, we characterize representative deep learning models on their requirement for CPU cores under different GPU resource configurations, and study the sensitivity of these models to other CPU-side shared resources. Based on the characterization, we propose CODA, a scheduling system that is comprised of an adaptive CPU allocator, a real-time contention eliminator, and a multi-array job scheduler. Experimental results show that CODA improves GPU utilization by 20.8% on average without increasing the queuing time of CPU jobs.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126163544","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00158
Sheng Zhang, Yung-Shiuan Liang, Zhuzhong Qian, Mingjun Xiao, Jidong Ge, Jie Wu, Sanglu Lu
In this paper, we consider a fundamental problem: given one mobile charger that can charge multiple sensor nodes simultaneously, how we can schedule it to charge a given WSN to maximize the energy usage effectiveness (EUE)? We propose a novel charging paradigm–Overlapped Mobile Charging (OMC)– the first of its kind to the best of our knowledge. Firstly, OMC clusters sensor nodes into multiple non-overlapped sets using k-means evaluated by the Davies-Bouldin Index, such that the sensor nodes in each set have similar recharging cycles. Secondly, for each set of sensor nodes, OMC further divides them into multiple overlapped groups, and charges each group at different locations for different time durations to make sure that each overlapped sensor node just receives its required energy from multiple charging locations.
{"title":"Overlapped Mobile Charging for Sensor Networks","authors":"Sheng Zhang, Yung-Shiuan Liang, Zhuzhong Qian, Mingjun Xiao, Jidong Ge, Jie Wu, Sanglu Lu","doi":"10.1109/ICDCS47774.2020.00158","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00158","url":null,"abstract":"In this paper, we consider a fundamental problem: given one mobile charger that can charge multiple sensor nodes simultaneously, how we can schedule it to charge a given WSN to maximize the energy usage effectiveness (EUE)? We propose a novel charging paradigm–Overlapped Mobile Charging (OMC)– the first of its kind to the best of our knowledge. Firstly, OMC clusters sensor nodes into multiple non-overlapped sets using k-means evaluated by the Davies-Bouldin Index, such that the sensor nodes in each set have similar recharging cycles. Secondly, for each set of sensor nodes, OMC further divides them into multiple overlapped groups, and charges each group at different locations for different time durations to make sure that each overlapped sensor node just receives its required energy from multiple charging locations.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133854335","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00100
A. Kshemkalyani, A. R. Molla, Gokarna Sharma
The dispersion problem on graphs asks k ≤n robots placed initially arbitrarily on the nodes of an n-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, and relocation of self-driving electric cars (robots) to recharge stations (nodes). The objective is to simultaneously minimize (or provide trade-off between) two fundamental performance metrics: (i) time to achieve dispersion and (ii) memory requirement at each robot. This problem has been relatively well-studied on static graphs. In this paper, we investigate it for the very first time on dynamic graphs. Particularly, we show that, even with unlimited memory at each robot and 1-neighborhood knowledge, dispersion is impossible to solve on dynamic graphs in the local communication model, where a robot can only communicate with other robots that are present at the same node. We then show that, even with unlimited memory at each robot but without 1-neighborhood knowledge, dispersion is impossible to solve in the global communication model, where a robot can communicate with any other robot in the graph possibly at different nodes. We then consider the global communication model with 1-neighborhood knowledge and establish a tight bound of Θ(k) on the time complexity of solving dispersion in any n-node arbitrary anonymous dynamic graph with Θ(log k) bits memory at each robot. Finally, we extend the fault-free algorithm to solve dispersion for (crash) faulty robots under the global model with 1-neighborhood knowledge.
{"title":"Efficient Dispersion of Mobile Robots on Dynamic Graphs","authors":"A. Kshemkalyani, A. R. Molla, Gokarna Sharma","doi":"10.1109/ICDCS47774.2020.00100","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00100","url":null,"abstract":"The dispersion problem on graphs asks k ≤n robots placed initially arbitrarily on the nodes of an n-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, and relocation of self-driving electric cars (robots) to recharge stations (nodes). The objective is to simultaneously minimize (or provide trade-off between) two fundamental performance metrics: (i) time to achieve dispersion and (ii) memory requirement at each robot. This problem has been relatively well-studied on static graphs. In this paper, we investigate it for the very first time on dynamic graphs. Particularly, we show that, even with unlimited memory at each robot and 1-neighborhood knowledge, dispersion is impossible to solve on dynamic graphs in the local communication model, where a robot can only communicate with other robots that are present at the same node. We then show that, even with unlimited memory at each robot but without 1-neighborhood knowledge, dispersion is impossible to solve in the global communication model, where a robot can communicate with any other robot in the graph possibly at different nodes. We then consider the global communication model with 1-neighborhood knowledge and establish a tight bound of Θ(k) on the time complexity of solving dispersion in any n-node arbitrary anonymous dynamic graph with Θ(log k) bits memory at each robot. Finally, we extend the fault-free algorithm to solve dispersion for (crash) faulty robots under the global model with 1-neighborhood knowledge.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130919856","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00107
Daping Li, Ji-guang Wan, Jun Wang, Jian Zhou, Kai Lu, Peng Xu, Fei Wu, C. Xie
The Intel Optane DC Persistent Memory Module (AEP), which is the first commercial available Non-Volatile Memory (NVM) product, offers comparable performance with DRAM while providing larger capacities and data persistence. Existing researches that substitute NVM with DRAM or hybridize them are either emulator-based or focused on how to improve the energy efficiency for writes. Unfortunately, the energy efficiency of the real AEP system is less explored. Based on real AEP, we observe that even though eliminating the DRAM-like refresh energy consumptions, AEP consumes significant different energy at different performance levels. Specifically, requests with time intervals (dispersed) underperform in both performance and energy efficiency when compared with the case of requests without time intervals (compact). This disparity and parallelism exploitation potentials motivate us to propose Sprint-AEP, an energy-efficiency-oriented scheduling method for AEP-equipped servers. Sprint-AEP fully activates adequate AEPs to serve most of the requests by deferring the write requests and prefetching the hottest data. The remaining AEPs will stay in idle mode with a low idle power to save energy. Besides, we also utilize the read parallelism to accelerate the sync and prefetching processes. Compared with energy-unaware AEP usages, our experimental results show that Sprint-AEP saves up to 26% energy with little performance degradation.
英特尔Optane DC Persistent Memory Module (AEP)是第一款商用非易失性内存(NVM)产品，在提供更大容量和数据持久性的同时，提供与DRAM相当的性能。现有的用DRAM替代NVM或混合它们的研究要么是基于仿真器的，要么是关注如何提高写入的能效。不幸的是，真正的AEP系统的能源效率很少被探索。基于实际的AEP，我们观察到，即使消除了类似dram的刷新能耗，AEP在不同性能水平上消耗的能量也有显著差异。具体来说，与没有时间间隔(紧凑)的请求相比，具有时间间隔(分散)的请求在性能和能源效率方面都表现不佳。这种差异和并行开发潜力促使我们提出了Sprint-AEP，这是一种针对配备aep的服务器的面向能效的调度方法。Sprint-AEP通过延迟写请求和预取最热的数据来充分激活足够的aep来服务大多数请求。剩余的aep将保持低空闲功率的空闲模式，以节省能源。此外，我们还利用读并行性来加速同步和预取过程。与不考虑能量的AEP使用相比，我们的实验结果表明，Sprint-AEP在性能下降很小的情况下节省了高达26%的能量。
{"title":"Disperse Access Considered Energy Inefficiency in Intel Optane DC Persistent Memory Servers","authors":"Daping Li, Ji-guang Wan, Jun Wang, Jian Zhou, Kai Lu, Peng Xu, Fei Wu, C. Xie","doi":"10.1109/ICDCS47774.2020.00107","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00107","url":null,"abstract":"The Intel Optane DC Persistent Memory Module (AEP), which is the first commercial available Non-Volatile Memory (NVM) product, offers comparable performance with DRAM while providing larger capacities and data persistence. Existing researches that substitute NVM with DRAM or hybridize them are either emulator-based or focused on how to improve the energy efficiency for writes. Unfortunately, the energy efficiency of the real AEP system is less explored. Based on real AEP, we observe that even though eliminating the DRAM-like refresh energy consumptions, AEP consumes significant different energy at different performance levels. Specifically, requests with time intervals (dispersed) underperform in both performance and energy efficiency when compared with the case of requests without time intervals (compact). This disparity and parallelism exploitation potentials motivate us to propose Sprint-AEP, an energy-efficiency-oriented scheduling method for AEP-equipped servers. Sprint-AEP fully activates adequate AEPs to serve most of the requests by deferring the write requests and prefetching the hottest data. The remaining AEPs will stay in idle mode with a low idle power to save energy. Besides, we also utilize the read parallelism to accelerate the sync and prefetching processes. Compared with energy-unaware AEP usages, our experimental results show that Sprint-AEP saves up to 26% energy with little performance degradation.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128754225","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00189
Jiaping Yu, Haiwen Chen, Kui Wu, Zhiping Cai, Jinhua Cui
Surveillance cameras have been extensively used in smart cities and high security zones. Recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. To tackle the problem, we develop a distributed camera storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances robustness by judiciously distributing erasure coded video blocks across optimally-chosen surveillance cameras. We implement the distributed storage system for surveillance cameras and evaluate its performance via real-world field test. Our system is the first solution that can defend against geo-range attacks in a robust and privacy-preserving manner.
{"title":"A Distributed Storage System for Robust, Privacy-Preserving Surveillance Cameras","authors":"Jiaping Yu, Haiwen Chen, Kui Wu, Zhiping Cai, Jinhua Cui","doi":"10.1109/ICDCS47774.2020.00189","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00189","url":null,"abstract":"Surveillance cameras have been extensively used in smart cities and high security zones. Recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. To tackle the problem, we develop a distributed camera storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances robustness by judiciously distributing erasure coded video blocks across optimally-chosen surveillance cameras. We implement the distributed storage system for surveillance cameras and evaluate its performance via real-world field test. Our system is the first solution that can defend against geo-range attacks in a robust and privacy-preserving manner.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808290","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00055
John E. Augustine, Seth Gilbert, F. Kuhn, Peter Robinson, S. Sourav
We study the cost of distributed MST construction in the setting where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time and messages required to construct an MST.When edge weights exactly correspond with the latencies, we show that, perhaps interestingly, the bottleneck parameter in determining the running time of an algorithm is the total weight W of the MST (rather than the total number of nodes n, as in the standard CONGEST model). That is, we show a tight bound of $tilde Theta $ (D + $sqrt {W/c} $) rounds, where D refers to the latency diameter of the graph, W refers to the total weight of the constructed MST and edges have capacity c. The proposed algorithm sends Õ (m + W) messages, where m, the total number of edges in the network graph under consideration, is a known lower bound on message complexity for MST construction. We also show that Ω(W) is a lower bound for fast MST constructions.When the edge latencies and the corresponding edge weights are unrelated, and either can take arbitrary values, we show that (unlike the sub-linear time algorithms in the standard CONGEST model, on small diameter graphs), the best time complexity that can be achieved is Θ(D + n/c). However, if we restrict all edges to have equal latency ℓ and capacity c while having possibly different weights (weights could deviate arbitrarily from ℓ), we give an algorithm that constructs an MST in Õ (D + $sqrt {nell /c} $) time. In each case, we provide nearly matching upper and lower bounds.
{"title":"Latency, Capacity, and Distributed Minimum Spanning Tree†","authors":"John E. Augustine, Seth Gilbert, F. Kuhn, Peter Robinson, S. Sourav","doi":"10.1109/ICDCS47774.2020.00055","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00055","url":null,"abstract":"We study the cost of distributed MST construction in the setting where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time and messages required to construct an MST.When edge weights exactly correspond with the latencies, we show that, perhaps interestingly, the bottleneck parameter in determining the running time of an algorithm is the total weight W of the MST (rather than the total number of nodes n, as in the standard CONGEST model). That is, we show a tight bound of $tilde Theta $ (D + $sqrt {W/c} $) rounds, where D refers to the latency diameter of the graph, W refers to the total weight of the constructed MST and edges have capacity c. The proposed algorithm sends Õ (m + W) messages, where m, the total number of edges in the network graph under consideration, is a known lower bound on message complexity for MST construction. We also show that Ω(W) is a lower bound for fast MST constructions.When the edge latencies and the corresponding edge weights are unrelated, and either can take arbitrary values, we show that (unlike the sub-linear time algorithms in the standard CONGEST model, on small diameter graphs), the best time complexity that can be achieved is Θ(D + n/c). However, if we restrict all edges to have equal latency ℓ and capacity c while having possibly different weights (weights could deviate arbitrarily from ℓ), we give an algorithm that constructs an MST in Õ (D + $sqrt {nell /c} $) time. In each case, we provide nearly matching upper and lower bounds.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133913159","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00197
Yuanhu Yang, Jing Hu, Yusi Yang
In a social network, it needs to protect the network data effectively. To improve the security and privacy protection ability of the network data, a social network data protection algorithm is proposed based on dynamic cyclic encryption and link equilibrium configuration. The architecture model and routing control protocol of mobile social network are constructed. The mixed recommended values of user behavior attribution data of social network are calculated, and the data encryption in social network is realized by using sub-key random amplitude modulation method. The dynamic cyclic encryption algorithm is used to encrypt and transmit the data and the adaptive equalization scheduling of the data output of the social network is carried out by using the link equalization configuration method to improve the protection ability in the process of data transmission. The simulation results show that the proposed algorithm has good encryption ability, and the ability of data storage and transmission is improved.
{"title":"Research on Data Protection Algorithm Based on Social Network","authors":"Yuanhu Yang, Jing Hu, Yusi Yang","doi":"10.1109/ICDCS47774.2020.00197","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00197","url":null,"abstract":"In a social network, it needs to protect the network data effectively. To improve the security and privacy protection ability of the network data, a social network data protection algorithm is proposed based on dynamic cyclic encryption and link equilibrium configuration. The architecture model and routing control protocol of mobile social network are constructed. The mixed recommended values of user behavior attribution data of social network are calculated, and the data encryption in social network is realized by using sub-key random amplitude modulation method. The dynamic cyclic encryption algorithm is used to encrypt and transmit the data and the adaptive equalization scheduling of the data output of the social network is carried out by using the link equalization configuration method to improve the protection ability in the process of data transmission. The simulation results show that the proposed algorithm has good encryption ability, and the ability of data storage and transmission is improved.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132048989","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 : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00166
Jerin Sunny, S. Sankaran, V. Saraswat
Critical Infrastructures are one of the vital systems that support modern societies. The adoption of technologies like Industry 4.0, Industrial Internet of Things (IIoT) in critical infrastructures have made it a lucrative target for cyberattackers. Protecting critical infrastructure is of paramount importance due to the sensitive nature of the data coupled with the resource-constrained nature of the devices. The advent of blockchains can be a significant enabler for protecting critical infrastructure through the use of immutable ledger for storing the operations. However, blockchains are computationally expensive, have limited scalability and incur significant delays in processing transactions thus necessitating the development of a lightweight platform while retaining the functionality. This paper develops a lightweight blockchain based framework for protecting critical infrastructure by leveraging its hierarchical nature. Evaluation using embedded devices shows that our proposed framework minimizes the execution time of blockchain operations thus making it suitable for protecting critical infrastructure. Finally, our proposed framework is generic, in that it can be applied to any of the domains operating in the critical infrastructure.
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