Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00035
Anirudh Ganji, Anandeshwar Singh, Muhammad Shahzad
The switch fabrics of today’s data centers carry traffic controlled by a variety of TCP congestion control algorithms. This leads us to ask: how does the coexistence of multiple variants of TCP on shared switch fabric impacts the performance achieved by different applications in data centers? To answer this question, we conducted an extensive set of experiments with coexisting TCP variants on Leaf-Spine and Fat-Tree switch fabrics. We executed common data center workloads, which include streaming, MapReduce, and storage workloads, using four commonly used TCP variants, namely BBR, DCTCP, CUBIC, and New Reno. We also extensively executed iPerf workloads using these 4 TCP variants to purely study the impact of the coexistence of TCP variants on each other’s performance without incorporating the network behavior of the application layer. Our experiments resulted in a large set of network traces comprised of 160 billion packets (we will release these traces after publication of this work). We present comprehensive observations from these traces that have important implications in ensuring optimal utilization of data center switch fabric and in meeting the network performance needs of application layer workloads.
{"title":"Characterizing the Impact of TCP Coexistence in Data Center Networks","authors":"Anirudh Ganji, Anandeshwar Singh, Muhammad Shahzad","doi":"10.1109/ICDCS47774.2020.00035","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00035","url":null,"abstract":"The switch fabrics of today’s data centers carry traffic controlled by a variety of TCP congestion control algorithms. This leads us to ask: how does the coexistence of multiple variants of TCP on shared switch fabric impacts the performance achieved by different applications in data centers? To answer this question, we conducted an extensive set of experiments with coexisting TCP variants on Leaf-Spine and Fat-Tree switch fabrics. We executed common data center workloads, which include streaming, MapReduce, and storage workloads, using four commonly used TCP variants, namely BBR, DCTCP, CUBIC, and New Reno. We also extensively executed iPerf workloads using these 4 TCP variants to purely study the impact of the coexistence of TCP variants on each other’s performance without incorporating the network behavior of the application layer. Our experiments resulted in a large set of network traces comprised of 160 billion packets (we will release these traces after publication of this work). We present comprehensive observations from these traces that have important implications in ensuring optimal utilization of data center switch fabric and in meeting the network performance needs of application layer workloads.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"22 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":"127823900","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/ICDCS47774.2020.00021
Sang-Hoon Kim, Ho-Ren Chuang, Robert Lyerly, Pierre Olivier, Changwoo Min, B. Ravindran
Increasing the computing performance within a single-machine form factor is becoming increasingly difficult due to the complexities in scaling processor interconnects and coherence protocols. On the other hand, converting existing applications to run on multiple nodes requires a significant effort to rewrite application logic in distributed programming models and adapt the code to the underlying network characteristics.This paper presents DeX, an operating system-level approach to extend the execution boundary of existing applications over multiple machines. DeX allows the threads in a process to be relocated and distributed dynamically through a simple function call. DeX makes it trivial for developers to convert any application to be distributed over multiple nodes and for applications to transparently utilize disaggregated resources in a rack-scale system with minimal effort. Evaluation results using a running prototype and eight real applications showed promising results – six out of the eight scaled beyond the single-machine performance on DeX.
{"title":"DeX: Scaling Applications Beyond Machine Boundaries","authors":"Sang-Hoon Kim, Ho-Ren Chuang, Robert Lyerly, Pierre Olivier, Changwoo Min, B. Ravindran","doi":"10.1109/ICDCS47774.2020.00021","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00021","url":null,"abstract":"Increasing the computing performance within a single-machine form factor is becoming increasingly difficult due to the complexities in scaling processor interconnects and coherence protocols. On the other hand, converting existing applications to run on multiple nodes requires a significant effort to rewrite application logic in distributed programming models and adapt the code to the underlying network characteristics.This paper presents DeX, an operating system-level approach to extend the execution boundary of existing applications over multiple machines. DeX allows the threads in a process to be relocated and distributed dynamically through a simple function call. DeX makes it trivial for developers to convert any application to be distributed over multiple nodes and for applications to transparently utilize disaggregated resources in a rack-scale system with minimal effort. Evaluation results using a running prototype and eight real applications showed promising results – six out of the eight scaled beyond the single-machine performance on DeX.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"55 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":"126224041","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/ICDCS47774.2020.00125
Hao Wu, Wei Liu, Yifan Gong, Jiangming Jin
GPUs have been widely adopted to speedup various throughput-originated applications running on HPC platforms, where typically there are a number of tasks sharing GPUs to maximize GPU utilization. To facilitate GPU sharing, GPU vendors provide tools, allowing multiple processes concurrently to use GPUs. For example, Nvidia provides MPS (Multi-Process Service) managing all GPU processes to achieve high throughput by fully exploiting hardware resources. However, such tool leads to undesired single point of failure for all GPU processes, namely, one process’s exception makes other processes abnormal. In this work, we investigate the seriousness of this GPU process interferences caused by MPS, and propose an approach to address one of these interferences, which takes place during process quitting. By using signal handling and thread synchronization techniques in this approach, GPU processes are able to quit safely without interfering other GPU processes.
{"title":"Safe Process Quitting for GPU Multi-Process Service (MPS)","authors":"Hao Wu, Wei Liu, Yifan Gong, Jiangming Jin","doi":"10.1109/ICDCS47774.2020.00125","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00125","url":null,"abstract":"GPUs have been widely adopted to speedup various throughput-originated applications running on HPC platforms, where typically there are a number of tasks sharing GPUs to maximize GPU utilization. To facilitate GPU sharing, GPU vendors provide tools, allowing multiple processes concurrently to use GPUs. For example, Nvidia provides MPS (Multi-Process Service) managing all GPU processes to achieve high throughput by fully exploiting hardware resources. However, such tool leads to undesired single point of failure for all GPU processes, namely, one process’s exception makes other processes abnormal. In this work, we investigate the seriousness of this GPU process interferences caused by MPS, and propose an approach to address one of these interferences, which takes place during process quitting. By using signal handling and thread synchronization techniques in this approach, GPU processes are able to quit safely without interfering other GPU processes.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"16 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":"128607914","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/ICDCS47774.2020.00121
Ayesha Afzal, Muhammad Adeel Zahid, A. Akhtar, Basit Shafiq, S. Shamail, Abeer Elahraf, Jaideep Vaidya, N. Adam
Business Process (BP) composition is a challenging task for small and medium organizations that do not have sufficient resources for design, coding, and management of their BPs. Cloud infrastructure and service-oriented middleware can be leveraged for rapid development and deployment of BPs of such organizations. BP development in the cloud-based environment can be done by exploiting the knowledge of existing BPs of related organizations. In this demonstration, we present the BP- Com tool which is a Web-based interactive system that enables efficient development of BPs in the cloud. BP-Com implements our service mapping approach called ASSEMBLE that utilizes the attribute, structural and semantics information of service operations of existing BPs in a given domain to help a user organization to compose its BP. Given a collection of related BPs and available service operations of a user organization, BP-Com computes a mapping between the available service operations of the user organization and the BP operations of other organizations. The results of operation mapping are presented to the user for refinement and customization of the generated BP workflow. Executable BP code is then generated in standard BPEL language, which can be deployed on any process execution engine on the user organization’s site or on the cloud.
{"title":"BP-Com: A Service Mapping Tool for Rapid Development of Business Processes","authors":"Ayesha Afzal, Muhammad Adeel Zahid, A. Akhtar, Basit Shafiq, S. Shamail, Abeer Elahraf, Jaideep Vaidya, N. Adam","doi":"10.1109/ICDCS47774.2020.00121","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00121","url":null,"abstract":"Business Process (BP) composition is a challenging task for small and medium organizations that do not have sufficient resources for design, coding, and management of their BPs. Cloud infrastructure and service-oriented middleware can be leveraged for rapid development and deployment of BPs of such organizations. BP development in the cloud-based environment can be done by exploiting the knowledge of existing BPs of related organizations. In this demonstration, we present the BP- Com tool which is a Web-based interactive system that enables efficient development of BPs in the cloud. BP-Com implements our service mapping approach called ASSEMBLE that utilizes the attribute, structural and semantics information of service operations of existing BPs in a given domain to help a user organization to compose its BP. Given a collection of related BPs and available service operations of a user organization, BP-Com computes a mapping between the available service operations of the user organization and the BP operations of other organizations. The results of operation mapping are presented to the user for refinement and customization of the generated BP workflow. Executable BP code is then generated in standard BPEL language, which can be deployed on any process execution engine on the user organization’s site or on the cloud.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"36 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":"130604068","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/ICDCS47774.2020.00111
Yan Li, Bo An, Junming Ma, Donggang Cao, Yasha Wang, Hong Mei
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms. But due to the large searching space, HPT is usually time-consuming and resource-intensive. Nowadays, many researchers use public cloud resources to train machine learning models, convenient yet expensive. How to speed up the HPT process while at the same time reduce cost is very important for cloud ML users. In this paper, we propose SpotTune, an approach that exploits transient revocable resources in the public cloud with some tailored strategies to do HPT in a parallel and cost-efficient manner. Orchestrating the HPT process upon transient servers, SpotTune uses two main techniques, fine-grained cost-aware resource provisioning, and ML training trend predicting, to reduce the monetary cost and runtime of HPT processes. Our evaluations show that SpotTune can reduce the cost by up to 90% and achieve a 16.61x performance-cost rate improvement.
{"title":"SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud","authors":"Yan Li, Bo An, Junming Ma, Donggang Cao, Yasha Wang, Hong Mei","doi":"10.1109/ICDCS47774.2020.00111","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00111","url":null,"abstract":"Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms. But due to the large searching space, HPT is usually time-consuming and resource-intensive. Nowadays, many researchers use public cloud resources to train machine learning models, convenient yet expensive. How to speed up the HPT process while at the same time reduce cost is very important for cloud ML users. In this paper, we propose SpotTune, an approach that exploits transient revocable resources in the public cloud with some tailored strategies to do HPT in a parallel and cost-efficient manner. Orchestrating the HPT process upon transient servers, SpotTune uses two main techniques, fine-grained cost-aware resource provisioning, and ML training trend predicting, to reduce the monetary cost and runtime of HPT processes. Our evaluations show that SpotTune can reduce the cost by up to 90% and achieve a 16.61x performance-cost rate improvement.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"54 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":"128252721","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/ICDCS47774.2020.00079
Chi Lin, Tingting Xu, Jie Xiong, Fenglong Ma, Lei Wang, Guowei Wu
Handwriting recognition system provides people a convenient and alternative way for writing in the air with fingers rather than typing keyboards. For people with blurred vision and patients with generalized hand neurological disease, writing in the air is particularly attracting due to the small input screen of smartphones and smartwatches. Existing recognition systems still face drawbacks such as requiring to wear dedicated devices, relatively low accuracy and infeasible for cross domain identification, which greatly limit the usability of these systems. To address these issues, we propose WiWrite, an accurate device-free handwriting recognition system which allows writing in the air without a need of attaching any device to the user. Specifically, we use Commercial Off-The-Shelf (COTS) WiFi hardware to achieve fine-grained finger tracking. We develop a CSI division scheme to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces the noise of the CSI amplitude. To automatically retain low noise data for identification, we propose a self-paced dense convolutional network (SPDCN), which consists of the self-paced loss function based on a modified convolutional neural network, together with a dense convolutional network. Comprehensive experiments are conducted to show the merits of WiWrite, revealing that, the recognition accuracies for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve a one-fit-for-all recognition regardless of environment diversities.
{"title":"WiWrite: An Accurate Device-Free Handwriting Recognition System with COTS WiFi","authors":"Chi Lin, Tingting Xu, Jie Xiong, Fenglong Ma, Lei Wang, Guowei Wu","doi":"10.1109/ICDCS47774.2020.00079","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00079","url":null,"abstract":"Handwriting recognition system provides people a convenient and alternative way for writing in the air with fingers rather than typing keyboards. For people with blurred vision and patients with generalized hand neurological disease, writing in the air is particularly attracting due to the small input screen of smartphones and smartwatches. Existing recognition systems still face drawbacks such as requiring to wear dedicated devices, relatively low accuracy and infeasible for cross domain identification, which greatly limit the usability of these systems. To address these issues, we propose WiWrite, an accurate device-free handwriting recognition system which allows writing in the air without a need of attaching any device to the user. Specifically, we use Commercial Off-The-Shelf (COTS) WiFi hardware to achieve fine-grained finger tracking. We develop a CSI division scheme to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces the noise of the CSI amplitude. To automatically retain low noise data for identification, we propose a self-paced dense convolutional network (SPDCN), which consists of the self-paced loss function based on a modified convolutional neural network, together with a dense convolutional network. Comprehensive experiments are conducted to show the merits of WiWrite, revealing that, the recognition accuracies for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve a one-fit-for-all recognition regardless of environment diversities.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"67 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":"129767944","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/icdcs47774.2020.00008
Diwakar Krishnamurthy
Program Committee Bram Adams (École Polytechnique de Montréal, Canada) Cor-Paul Bezemer (Delft University of Technology, Netherlands) Thomas Cerqueus (University College Dublin, Ireland) Christoph Csallner (University of Texas at Arlington, USA) Shaun Dunning (NetApp Inc., USA) Gregory Franks (Carleton University, Canada) Vahid Garousi (University of Calgary, Canada) Ahmed E. Hassan (Queen’s University, Canada) Robert Horrox (EMC Isilon, USA) André van Hoorn (University of Stuttgart, Germany) Diwakar Krishnamurthy (University of Calgary, Canada) Haroon Malik (University of Waterloo, Canada) Jerome A. Rolia (HP Labs, USA) Gerson Sunyé (University of Nantes, France) Anthony Ventresque (University College Dublin, Ireland)
项目委员会Bram Adams (École Polytechnique de montr，加拿大)co - paul Bezemer(荷兰代尔夫特理工大学)Thomas Cerqueus(爱尔兰都柏林大学)Christoph Csallner(美国德克萨斯大学阿灵顿分校)Shaun Dunning(美国NetApp Inc.) Gregory Franks(加拿大卡尔顿大学)Vahid Garousi(加拿大卡尔加里大学)Ahmed E. Hassan(加拿大皇后大学)Robert Horrox(美国EMC Isilon) andr van Hoorn(斯图加特大学)Diwakar Krishnamurthy(加拿大卡尔加里大学)Haroon Malik(加拿大滑铁卢大学)Jerome A. Rolia(美国惠普实验室)Gerson suny(法国南特大学)Anthony Ventresque(爱尔兰都柏林大学学院)
{"title":"Workshop Organizers","authors":"Diwakar Krishnamurthy","doi":"10.1109/icdcs47774.2020.00008","DOIUrl":"https://doi.org/10.1109/icdcs47774.2020.00008","url":null,"abstract":"Program Committee Bram Adams (École Polytechnique de Montréal, Canada) Cor-Paul Bezemer (Delft University of Technology, Netherlands) Thomas Cerqueus (University College Dublin, Ireland) Christoph Csallner (University of Texas at Arlington, USA) Shaun Dunning (NetApp Inc., USA) Gregory Franks (Carleton University, Canada) Vahid Garousi (University of Calgary, Canada) Ahmed E. Hassan (Queen’s University, Canada) Robert Horrox (EMC Isilon, USA) André van Hoorn (University of Stuttgart, Germany) Diwakar Krishnamurthy (University of Calgary, Canada) Haroon Malik (University of Waterloo, Canada) Jerome A. Rolia (HP Labs, USA) Gerson Sunyé (University of Nantes, France) Anthony Ventresque (University College Dublin, Ireland)","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"55 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":"130076109","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.
{"title":"Towards a Lightweight Blockchain Platform for Critical Infrastructure Protection","authors":"Jerin Sunny, S. Sankaran, V. Saraswat","doi":"10.1109/ICDCS47774.2020.00166","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00166","url":null,"abstract":"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.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"266 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":"123319331","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/ICDCS47774.2020.00178
Rui Liang, Shengyong Xu
In recent years, 3D reconstruction technology has developed rapidly. It is a promising field to apply 3D reconstruction technology to the measurement of plant configuration parameters. The main content of this paper is the 3D reconstruction technology for rape roots and the measurement methods for their key traits. Firstly, we set up a set of low-cost image sequence acquisition device of rape roots. We collected image data with common consumption level camera and used the method of SfM to carry out 3D reconstruction of rape roots. Then we proposed a series of algorithms to measure the surface area, volume, number of primary lateral roots and length of taproot based on the huge point cloud data obtained from 3D reconstruction. Finally, we designed a set of nondestructive measurement system for key traits of rape roots. The total volume of root, the number of primary lateral roots and the length of taproot were measured manually. Compared with the results of manual measurement, the accuracy of the main algorithm proposed in this paper is not less than 95%. Our contribution is to provide a 3D reconstruction method that is easy to operate, and to provide a high-precision measurement method for the key traits of rape roots, which has an important value for quantitative analysis of rape roots phenotype.
{"title":"Three-Dimensional Reconstruction and Phenotype Nondestructive Measurement Technology for Rape Roots","authors":"Rui Liang, Shengyong Xu","doi":"10.1109/ICDCS47774.2020.00178","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00178","url":null,"abstract":"In recent years, 3D reconstruction technology has developed rapidly. It is a promising field to apply 3D reconstruction technology to the measurement of plant configuration parameters. The main content of this paper is the 3D reconstruction technology for rape roots and the measurement methods for their key traits. Firstly, we set up a set of low-cost image sequence acquisition device of rape roots. We collected image data with common consumption level camera and used the method of SfM to carry out 3D reconstruction of rape roots. Then we proposed a series of algorithms to measure the surface area, volume, number of primary lateral roots and length of taproot based on the huge point cloud data obtained from 3D reconstruction. Finally, we designed a set of nondestructive measurement system for key traits of rape roots. The total volume of root, the number of primary lateral roots and the length of taproot were measured manually. Compared with the results of manual measurement, the accuracy of the main algorithm proposed in this paper is not less than 95%. Our contribution is to provide a 3D reconstruction method that is easy to operate, and to provide a high-precision measurement method for the key traits of rape roots, which has an important value for quantitative analysis of rape roots phenotype.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"20 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":"124363059","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/ICDCS47774.2020.00077
Qing Li, Nanyang Huang, Yong Jiang, R. Sinnott, Mingwei Xu
Data plane programming languages enable administrators of Software-Defined Networks (SDNs) to perform fine-grained flow control by compiling high-level policies into low-level rules and deploying rules in the data plane. However, it is difficult to scale the data plane with the dynamics of network traffic and the limited storage space of switches. In this paper, we propose a lazy OpenFlow Rule Placement (ORP) framework to enforce control polices and scale the SDN data plane by placing and reusing wildcard rules. We provide an offline rule placement scheme to meet performance objectives under real-world constraints. To handle dynamic traffic and perform incremental rule updates, we design an online matching rule deployment algorithm to place rules in polynomial time and prove it to be conditionally-optimal. Furthermore, to address the rule dependency problem during online rule placement, we extend the algorithm to deploy dependent rules and present lightweight heuristics to guarantee the fast reaction to the new flows. Extensive experiments are conducted on diverse network topologies and datasets to show that the lazy ORP framework significantly reduces the storage cost, improves data plane scalability and is flexible enough to accomplish different optimization goals.
{"title":"Scale the Data Plane of Software-Defined Networks: a Lazy Rule Placement Approach","authors":"Qing Li, Nanyang Huang, Yong Jiang, R. Sinnott, Mingwei Xu","doi":"10.1109/ICDCS47774.2020.00077","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00077","url":null,"abstract":"Data plane programming languages enable administrators of Software-Defined Networks (SDNs) to perform fine-grained flow control by compiling high-level policies into low-level rules and deploying rules in the data plane. However, it is difficult to scale the data plane with the dynamics of network traffic and the limited storage space of switches. In this paper, we propose a lazy OpenFlow Rule Placement (ORP) framework to enforce control polices and scale the SDN data plane by placing and reusing wildcard rules. We provide an offline rule placement scheme to meet performance objectives under real-world constraints. To handle dynamic traffic and perform incremental rule updates, we design an online matching rule deployment algorithm to place rules in polynomial time and prove it to be conditionally-optimal. Furthermore, to address the rule dependency problem during online rule placement, we extend the algorithm to deploy dependent rules and present lightweight heuristics to guarantee the fast reaction to the new flows. Extensive experiments are conducted on diverse network topologies and datasets to show that the lazy ORP framework significantly reduces the storage cost, improves data plane scalability and is flexible enough to accomplish different optimization goals.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"12 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":"126561714","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}
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