Pub Date : 2018-07-09DOI: 10.1109/NOMS.2018.8406332
Robert Norvill, Beltran Borja Fiz Pontiveros, R. State, A. Cullen
In this work we present E-EVM, a tool that emulates and visualises the execution of smart contracts on the Ethereum Virtual Machine. By working with the readily available bytecode of smart contracts we are able to display the program's control flow graph, opcodes and stack for each step of contract execution. This tool is designed to aid the user's understanding of the Etheruem Virtual Machine as well as aid the analysis of any given smart contract. As such, it functions as both an analysis and a learning tool. It allows the user to view the code in each block of a smart contract and follow possible control flow branches. It is able to detect loops and suggest optimisation candidates. It is possible to step through a contract one opcode at a time. E-EVM achieved an average of 85.6% code coverage when tested.
{"title":"Visual emulation for Ethereum's virtual machine","authors":"Robert Norvill, Beltran Borja Fiz Pontiveros, R. State, A. Cullen","doi":"10.1109/NOMS.2018.8406332","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406332","url":null,"abstract":"In this work we present E-EVM, a tool that emulates and visualises the execution of smart contracts on the Ethereum Virtual Machine. By working with the readily available bytecode of smart contracts we are able to display the program's control flow graph, opcodes and stack for each step of contract execution. This tool is designed to aid the user's understanding of the Etheruem Virtual Machine as well as aid the analysis of any given smart contract. As such, it functions as both an analysis and a learning tool. It allows the user to view the code in each block of a smart contract and follow possible control flow branches. It is able to detect loops and suggest optimisation candidates. It is possible to step through a contract one opcode at a time. E-EVM achieved an average of 85.6% code coverage when tested.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76269053","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 : 2018-07-09DOI: 10.1109/NOMS.2018.8406261
Ermias Andargie Walelgne, J. Manner, Vaibhav Bajpai, J. Ott
The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowd- sourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.
{"title":"Analyzing throughput and stability in cellular networks","authors":"Ermias Andargie Walelgne, J. Manner, Vaibhav Bajpai, J. Ott","doi":"10.1109/NOMS.2018.8406261","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406261","url":null,"abstract":"The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowd- sourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84702230","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 : 2018-07-09DOI: 10.1109/NOMS.2018.8406289
Antoine Messager, G. Parisis, R. Harper, P. Tee, I. Kiss, L. Berthouze
ISP and commercial networks are complex and thus difficult to characterise and manage. Network operators rely on a continuous flow of event log messages to identify and handle service outages. However, there is little published information about such events and how they are typically exploited. In this paper, we describe in as much detail as possible the event logs and network topology of a major commercial network. Through analysing the network topology, textual information of events and time of events, we highlight opportunities and challenges brought by such data. In particular, we suggest that the development of methods for inferring functional connectivity could unlock more of the informational value of event log messages and assist network management operators.
{"title":"Network events in a large commercial network: What can we learn?","authors":"Antoine Messager, G. Parisis, R. Harper, P. Tee, I. Kiss, L. Berthouze","doi":"10.1109/NOMS.2018.8406289","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406289","url":null,"abstract":"ISP and commercial networks are complex and thus difficult to characterise and manage. Network operators rely on a continuous flow of event log messages to identify and handle service outages. However, there is little published information about such events and how they are typically exploited. In this paper, we describe in as much detail as possible the event logs and network topology of a major commercial network. Through analysing the network topology, textual information of events and time of events, we highlight opportunities and challenges brought by such data. In particular, we suggest that the development of methods for inferring functional connectivity could unlock more of the informational value of event log messages and assist network management operators.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87170593","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406281
Aldo Febro, Hannan Xiao, Joseph Spring
The Session Initiation Protocol (SIP) is an application-layer control protocol used to establish and terminate calls that are deployed globally. A flood of SIP INVITE packets sent by an attacker causes a Telephony Denial of Service (TDoS) incident, during which legitimate users are unable to use telephony services. Legacy TDoS defense is typically implemented as network appliances and not sufficiently deployed to enable early detection. To make TDoS defense more widely deployed and yet affordable, this paper presents TDoSD@DP where TDoS detection and mitigation is programmed at the data plane so that it can be enabled on every switch port and therefore serves as distributed SIP sensors. With this approach, the damage is isolated at a particular switch and bandwidth saved by not sending attack packets further upstream. Experiments have been performed to track the SIP state machine and to limit the number of active SIP session per port. The results show that TDoSD@DP was able to detect and mitigate ongoing INVITE flood attack, protecting the SIP server, and limiting the damage to a local switch. Bringing the TDoS defense function to the data plane provides a novel data plane application that operates at the SIP protocol and a novel approach for TDoS defense implementation.
会话发起协议(SIP)是一个应用层控制协议，用于建立和终止全局部署的呼叫。攻击者发送大量SIP INVITE报文，导致合法用户无法使用电话业务的TDoS (telephone Denial of Service)事件。传统的TDoS防御通常作为网络设备来实现，并且没有充分部署以支持早期检测。为了使TDoS防御更广泛地部署并且价格合理，本文提出了TDoSD@DP，其中TDoS检测和缓解在数据平面编程，以便它可以在每个交换机端口上启用，因此可以用作分布式SIP传感器。使用这种方法，损害被隔离在特定的交换机上，并且由于不向上游发送攻击数据包而节省了带宽。已经执行了一些实验来跟踪SIP状态机并限制每个端口的活动SIP会话的数量。结果表明，TDoSD@DP能够检测和减轻正在进行的INVITE洪水攻击，保护SIP服务器，并限制对本地交换机的损害。将TDoS防御功能引入数据平面提供了一个在SIP协议下操作的新颖数据平面应用程序，并为TDoS防御实现提供了一种新的方法。
{"title":"Telephony Denial of Service defense at data plane (TDoSD@DP)","authors":"Aldo Febro, Hannan Xiao, Joseph Spring","doi":"10.1109/NOMS.2018.8406281","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406281","url":null,"abstract":"The Session Initiation Protocol (SIP) is an application-layer control protocol used to establish and terminate calls that are deployed globally. A flood of SIP INVITE packets sent by an attacker causes a Telephony Denial of Service (TDoS) incident, during which legitimate users are unable to use telephony services. Legacy TDoS defense is typically implemented as network appliances and not sufficiently deployed to enable early detection. To make TDoS defense more widely deployed and yet affordable, this paper presents TDoSD@DP where TDoS detection and mitigation is programmed at the data plane so that it can be enabled on every switch port and therefore serves as distributed SIP sensors. With this approach, the damage is isolated at a particular switch and bandwidth saved by not sending attack packets further upstream. Experiments have been performed to track the SIP state machine and to limit the number of active SIP session per port. The results show that TDoSD@DP was able to detect and mitigate ongoing INVITE flood attack, protecting the SIP server, and limiting the damage to a local switch. Bringing the TDoS defense function to the data plane provides a novel data plane application that operates at the SIP protocol and a novel approach for TDoS defense implementation.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87361731","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406273
Fan Jiang, C. Castillo, S. Ahalt
Collaborative and data-intensive applications are hosted on geo-distributed infrastructures to exploit computing resources at scale. However, these applications typically incur massive data transfers over bandwidth-constrained wide- area networks (WANs) which impose significant performance overhead. Conventional distributed computing platforms (e.g., Spark) leverage caching to avoid duplicate executions of common computations and thus reduce network traffic. However, these techniques were developed for data center environments and therefore lack advanced network-aware mechanisms to support high-performance, data-intensive applications over the WAN in geo-distributed environments. Hence, we develop Cachalot - a novel network-aware, cooperative cache network for caching datasets generated by common computations shared among geo- distributed, data-intensive applications. We perform a simulation- based deep evaluation using both synthetic and real traces. The experimental results indicate Cachalot speeds up data-intensive applications by over 50%, reducing network traffic by up to 60%; and, outperforms state-of-the-art baselines by over 20% in geo-distributed environments for various common user-driven performance metrics.
{"title":"Cachalot: A network-aware, cooperative cache network for geo-distributed, data-intensive applications","authors":"Fan Jiang, C. Castillo, S. Ahalt","doi":"10.1109/NOMS.2018.8406273","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406273","url":null,"abstract":"Collaborative and data-intensive applications are hosted on geo-distributed infrastructures to exploit computing resources at scale. However, these applications typically incur massive data transfers over bandwidth-constrained wide- area networks (WANs) which impose significant performance overhead. Conventional distributed computing platforms (e.g., Spark) leverage caching to avoid duplicate executions of common computations and thus reduce network traffic. However, these techniques were developed for data center environments and therefore lack advanced network-aware mechanisms to support high-performance, data-intensive applications over the WAN in geo-distributed environments. Hence, we develop Cachalot - a novel network-aware, cooperative cache network for caching datasets generated by common computations shared among geo- distributed, data-intensive applications. We perform a simulation- based deep evaluation using both synthetic and real traces. The experimental results indicate Cachalot speeds up data-intensive applications by over 50%, reducing network traffic by up to 60%; and, outperforms state-of-the-art baselines by over 20% in geo-distributed environments for various common user-driven performance metrics.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89813106","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406170
Bogdan-Mihai Andrus, A. Autenrieth, T. Szyrkowiec, J. Olmos, I. Monroy
Flexible spectrum assignment in Elastic Optical Networks (EON) has emerged as a potential solution for allowing dynamic and elastic management of available bandwidth resources. In this paper, we demonstrate and evaluate our developed flexi-grid optical domain controller based on NETCONF/YANG. Our proposed modular architecture, based on Finite State Machines (FSMs), allows the flexibility to deploy the controller either in a centralized or in a distributed state for on the fly encrypted device management connections. A testbed composed of two physical Sliceable Bandwidth Variable Transponders (SBVTs) and an emulated flexi-grid optical network was used for our software evaluation. Controller startup and synchronization time, as well as media channel setup time are evaluated to compare the two deployment options and assess network scaling effects. Results demonstrate that our software is scalable by maintaining a relatively constant startup time on the networks tested (i.e., 1 to 64 nodes) in both deployment options. Software scalability is also supported by the media channel setup time, which presents a modest log scale growth when increasing the number of nodes from one to 64.
{"title":"Evaluation and experimental demonstration of SDN-enabled flexi-grid optical domain controller based on NETCONF/YANG","authors":"Bogdan-Mihai Andrus, A. Autenrieth, T. Szyrkowiec, J. Olmos, I. Monroy","doi":"10.1109/NOMS.2018.8406170","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406170","url":null,"abstract":"Flexible spectrum assignment in Elastic Optical Networks (EON) has emerged as a potential solution for allowing dynamic and elastic management of available bandwidth resources. In this paper, we demonstrate and evaluate our developed flexi-grid optical domain controller based on NETCONF/YANG. Our proposed modular architecture, based on Finite State Machines (FSMs), allows the flexibility to deploy the controller either in a centralized or in a distributed state for on the fly encrypted device management connections. A testbed composed of two physical Sliceable Bandwidth Variable Transponders (SBVTs) and an emulated flexi-grid optical network was used for our software evaluation. Controller startup and synchronization time, as well as media channel setup time are evaluated to compare the two deployment options and assess network scaling effects. Results demonstrate that our software is scalable by maintaining a relatively constant startup time on the networks tested (i.e., 1 to 64 nodes) in both deployment options. Software scalability is also supported by the media channel setup time, which presents a modest log scale growth when increasing the number of nodes from one to 64.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77029476","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406223
Tho V. Le, R. V. Rijswijk-Deij, Luca Allodi, Nicola Zannone
The security extensions to the DNS (DNSSEC) currently cover approximately 3% of all domains worldwide. In response to the low deployment of DNSSEC, a few top-level domains started offering 'per-domain' economic incentives to encourage adoption of the protocol by offering a yearly discount on each signed domain. However, it remains unclear whether these incentives are well-balanced and foster the overall security of the infrastructure as well as its deployment at scale. In this paper we argue that, in the presence of fixed costs of deployment, misaligned 'per-domain' incentives may have the collateral effect of encouraging large operators to massively deploy unsecure implementations of DNSSEC, whereas smaller operators, for which the effect of the economic incentive is negligible, may not significantly benefit from it. To investigate this, we study the security of DNSSEC deployment at scale, particularly in TLDs that offer economic incentives. We find that the security of DNSSEC implementations in the wild poorly reflects standard recommendations, particularly for tasks that cannot be solved by triggering a flag in the DNS software service (e.g. key rollover). Further, we find that, on average, large operators deploy weak DNSSEC security more frequently than small DNSSEC operators, suggesting that current incentives are ineffective in promoting a secure adoption and in deterring insecure implementations. We conclude the paper with actionable recommendations for TLD registry operators to improve the alignment of economic incentives with secure DNSSEC requirements.
{"title":"Economic incentives on DNSSEC deployment: Time to move from quantity to quality","authors":"Tho V. Le, R. V. Rijswijk-Deij, Luca Allodi, Nicola Zannone","doi":"10.1109/NOMS.2018.8406223","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406223","url":null,"abstract":"The security extensions to the DNS (DNSSEC) currently cover approximately 3% of all domains worldwide. In response to the low deployment of DNSSEC, a few top-level domains started offering 'per-domain' economic incentives to encourage adoption of the protocol by offering a yearly discount on each signed domain. However, it remains unclear whether these incentives are well-balanced and foster the overall security of the infrastructure as well as its deployment at scale. In this paper we argue that, in the presence of fixed costs of deployment, misaligned 'per-domain' incentives may have the collateral effect of encouraging large operators to massively deploy unsecure implementations of DNSSEC, whereas smaller operators, for which the effect of the economic incentive is negligible, may not significantly benefit from it. To investigate this, we study the security of DNSSEC deployment at scale, particularly in TLDs that offer economic incentives. We find that the security of DNSSEC implementations in the wild poorly reflects standard recommendations, particularly for tasks that cannot be solved by triggering a flag in the DNS software service (e.g. key rollover). Further, we find that, on average, large operators deploy weak DNSSEC security more frequently than small DNSSEC operators, suggesting that current incentives are ineffective in promoting a secure adoption and in deterring insecure implementations. We conclude the paper with actionable recommendations for TLD registry operators to improve the alignment of economic incentives with secure DNSSEC requirements.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73073732","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406180
Tzu-Chi Huang, Kuo-Chih Chu, Guo-Hao Huang, Yan-Chen Shen, C. Shieh
A MapReduce cloud becomes a key to the success of cloud computing today. However, a MapReduce cloud uses a single Master node as the brain to manage tasks distributed over Slave nodes for controlling the entire progress of the application execution. Accordingly, a MapReduce cloud easily overloads the Master node with reports sent from Slave nodes at run time to harm performance. Besides, a MapReduce cloud makes the Master node a single failure point to suspend the application execution when the Master node cannot work. A MapReduce cloud can use the Distributed Control Framework (DCF) proposed in this paper to improve both performance and fault tolerance, because DCF shifts most works of a Master node to a DCF Master Agent coexisting in each Slave node and allows Slave nodes to join or leave a cloud at run time without interrupting the application execution. According to observations on experiments with various applications in this paper, a MapReduce cloud can use DCF to have better performance and fault tolerance in comparison to a native MapReduce cloud.
{"title":"Distributed control framework for mapreduce cloud on cloud computing","authors":"Tzu-Chi Huang, Kuo-Chih Chu, Guo-Hao Huang, Yan-Chen Shen, C. Shieh","doi":"10.1109/NOMS.2018.8406180","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406180","url":null,"abstract":"A MapReduce cloud becomes a key to the success of cloud computing today. However, a MapReduce cloud uses a single Master node as the brain to manage tasks distributed over Slave nodes for controlling the entire progress of the application execution. Accordingly, a MapReduce cloud easily overloads the Master node with reports sent from Slave nodes at run time to harm performance. Besides, a MapReduce cloud makes the Master node a single failure point to suspend the application execution when the Master node cannot work. A MapReduce cloud can use the Distributed Control Framework (DCF) proposed in this paper to improve both performance and fault tolerance, because DCF shifts most works of a Master node to a DCF Master Agent coexisting in each Slave node and allows Slave nodes to join or leave a cloud at run time without interrupting the application execution. According to observations on experiments with various applications in this paper, a MapReduce cloud can use DCF to have better performance and fault tolerance in comparison to a native MapReduce cloud.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82702793","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 : 2018-07-06DOI: 10.1109/NOMS.2018.8406133
Yu-Chen Hsieh, Hua-Jun Hong, P. Tsai, Yu-Rong Wang, Qiuxi Zhu, Md Yusuf Sarwar Uddin, N. Venkatasubramanian, Cheng-Hsin Hsu
We demonstrate a managed edge computing platform for Internet-of-Things (IoT) devices, which supports dynamic deployment of virtualized containers running distributed analytics. We build a model city, and install multiple Raspberry Pis as minions, and a mini PC as the master. Through the web dashboard on the master, we show how users can remotely monitor, manage, and upgrade the IoT analytics and devices. Multiple concrete IoT analytics, namely: (i) air quality monitor, (ii) sound classifier, and (iii) image recognizer are demonstrated. Several sample measurements on deployment speed, Quality-of- Service (QoS) achievements, and event-driven mechanisms are also carried out on the testbed.
{"title":"Managed edge computing on Internet-of-Things devices for smart city applications","authors":"Yu-Chen Hsieh, Hua-Jun Hong, P. Tsai, Yu-Rong Wang, Qiuxi Zhu, Md Yusuf Sarwar Uddin, N. Venkatasubramanian, Cheng-Hsin Hsu","doi":"10.1109/NOMS.2018.8406133","DOIUrl":"https://doi.org/10.1109/NOMS.2018.8406133","url":null,"abstract":"We demonstrate a managed edge computing platform for Internet-of-Things (IoT) devices, which supports dynamic deployment of virtualized containers running distributed analytics. We build a model city, and install multiple Raspberry Pis as minions, and a mini PC as the master. Through the web dashboard on the master, we show how users can remotely monitor, manage, and upgrade the IoT analytics and devices. Multiple concrete IoT analytics, namely: (i) air quality monitor, (ii) sound classifier, and (iii) image recognizer are demonstrated. Several sample measurements on deployment speed, Quality-of- Service (QoS) achievements, and event-driven mechanisms are also carried out on the testbed.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91473650","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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