Awais Khan, M. Attique, Youngjae Kim, Sungyong Park, Byungchul Tak
With the increasing adoption of edge computing, the capacity requirements of the edge servers are also growing. Especially the data volume generated from a large number of edge clients and/or edge devices demand more capacity to be able to store them for processing. The growing gap between the data volume and current storage capacity is motivating the need towards building aggregated storage spaces. Aggregated storage can be an effective way to extend edge servers' overall storage capacity by combining storage resources of other nodes under the agreement to share. Several Federation file systems exist to meet this aggregate storage needs but are not without limitations. Dependency to the specific software stack makes it unfit for general-purpose use and they often neglect important features critical for the performance. In this paper, we address the important challenges of building the Federation on top of edge servers with the heterogeneous file system and resource configurations. We prototyped EDGESTORE, a Federation File System for Edge Servers. EDGESTORE equips the users with an aggregate storage namespace and federates resources of edge servers, to enable high resource-sharing in Federation. We propose, Job and Resource-Aware Request Placement algorithm (JRAP) to take advantage of edge server resource heterogeneity. To evaluate the usefulness of EDGESTORE, we consider two federation scenarios i) with same resource configurations and ii) with different resource configurations. We evaluate the efficacy of various big data applications from data storage to analysis using EDGESTORE on a real testbed.
{"title":"EDGESTORE: A Single Namespace and Resource-Aware Federation File System for Edge Servers","authors":"Awais Khan, M. Attique, Youngjae Kim, Sungyong Park, Byungchul Tak","doi":"10.1109/EDGE.2018.00021","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00021","url":null,"abstract":"With the increasing adoption of edge computing, the capacity requirements of the edge servers are also growing. Especially the data volume generated from a large number of edge clients and/or edge devices demand more capacity to be able to store them for processing. The growing gap between the data volume and current storage capacity is motivating the need towards building aggregated storage spaces. Aggregated storage can be an effective way to extend edge servers' overall storage capacity by combining storage resources of other nodes under the agreement to share. Several Federation file systems exist to meet this aggregate storage needs but are not without limitations. Dependency to the specific software stack makes it unfit for general-purpose use and they often neglect important features critical for the performance. In this paper, we address the important challenges of building the Federation on top of edge servers with the heterogeneous file system and resource configurations. We prototyped EDGESTORE, a Federation File System for Edge Servers. EDGESTORE equips the users with an aggregate storage namespace and federates resources of edge servers, to enable high resource-sharing in Federation. We propose, Job and Resource-Aware Request Placement algorithm (JRAP) to take advantage of edge server resource heterogeneity. To evaluate the usefulness of EDGESTORE, we consider two federation scenarios i) with same resource configurations and ii) with different resource configurations. We evaluate the efficacy of various big data applications from data storage to analysis using EDGESTORE on a real testbed.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133106","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}
Lucas R. B. Brasilino, A. Shroyer, Naveen Marri, Saurabh Agrawal, Catherine L. Pilachowski, E. Kissel, D. M. Swany
With proliferating sensor networks and Internet of Things-scale devices, networks are increasingly diverse and heterogeneous. To enable the most efficient use of network bandwidth with the lowest possible latency, we propose InLocus, a stream-oriented architecture situated at (or near) the network's edge which balances hardware-accelerated performance with the flexibility of asynchronous software-based control. In this paper we utilize a flexible platform (Xilinx Zynq SoC) to compare microbenchmarks of several InLocus implementations: naive JavaScript, Handwritten C, and High-Level Synthesis (HLS) in programmable hardware.
{"title":"Data Distillation at the Network's Edge: Exposing Programmable Logic with InLocus","authors":"Lucas R. B. Brasilino, A. Shroyer, Naveen Marri, Saurabh Agrawal, Catherine L. Pilachowski, E. Kissel, D. M. Swany","doi":"10.1109/EDGE.2018.00011","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00011","url":null,"abstract":"With proliferating sensor networks and Internet of Things-scale devices, networks are increasingly diverse and heterogeneous. To enable the most efficient use of network bandwidth with the lowest possible latency, we propose InLocus, a stream-oriented architecture situated at (or near) the network's edge which balances hardware-accelerated performance with the flexibility of asynchronous software-based control. In this paper we utilize a flexible platform (Xilinx Zynq SoC) to compare microbenchmarks of several InLocus implementations: naive JavaScript, Handwritten C, and High-Level Synthesis (HLS) in programmable hardware.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428915","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}
Christoph Pallasch, S. Wein, Nicolai Hoffmann, M. Obdenbusch, Tilman Buchner, J. Waltl, C. Brecher
In the past, industrial control of field devices was comprised of self-contained systems in a dedicated network for exchanging control information between field devices and control hardware to accomplish process tasks. Nowadays, cloud computing enables a massive amount of computing resources and high availability, which opens up new potentials in the industrial sector. Until now, the integration of cloud solutions in industrial control was limited due to missing technologies connecting the Internet of Things with industrial requirements. Furthermore, based on existing paradigms there is a lack of appropriate architecture concepts for industrial control. This paper depicts a platform concept, which combines cloud computing and industrial control using edge devices realized for an automation cell.
{"title":"Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies","authors":"Christoph Pallasch, S. Wein, Nicolai Hoffmann, M. Obdenbusch, Tilman Buchner, J. Waltl, C. Brecher","doi":"10.1109/EDGE.2018.00026","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00026","url":null,"abstract":"In the past, industrial control of field devices was comprised of self-contained systems in a dedicated network for exchanging control information between field devices and control hardware to accomplish process tasks. Nowadays, cloud computing enables a massive amount of computing resources and high availability, which opens up new potentials in the industrial sector. Until now, the integration of cloud solutions in industrial control was limited due to missing technologies connecting the Internet of Things with industrial requirements. Furthermore, based on existing paradigms there is a lack of appropriate architecture concepts for industrial control. This paper depicts a platform concept, which combines cloud computing and industrial control using edge devices realized for an automation cell.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114376107","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}
Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.
{"title":"An Energy-Aware Edge Server Placement Algorithm in Mobile Edge Computing","authors":"Yuanzhen Li, Shangguang Wang","doi":"10.1109/EDGE.2018.00016","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00016","url":null,"abstract":"Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131067104","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}
With the increasing popularity and the need for low-cost green computing systems, new paradigms and models such as fog, edge, and volunteer cloud computing (e.g. cuCloud) have recently emerged. cuCloud, one of the appealing volunteer cloud computing system, share the same philosophy as desktop grid, which runs on underutilized and or spare resources of personal computers (i.e. volunteer hosts) owned by individuals and organizations. On one side of the spectrum, underlying cuCloud infrastructure comprises varying levels of availability, volatility, and trust, allowing volunteers to randomly join and leave the model, which makes the resource management and scheduling of tasks a challenging process. On the other side, it is even more challenging and critical to guarantee the Quality of Service (QoS) for applications deployed in the cuCloud model, which requires the tracking and monitoring the reliability and trust of highly distributed volunteer resources. The majority of the available reputation models consider only the ratio of successfully completed tasks to total tasks requested in the determination of reliability decisions of the volunteer nodes, which, in turn, make the reliability model coarse-grained. These models lack of fine-grained parameters such as task-level behaviors (e.g. success or fail) and task characteristics (e.g. priority of a task). To address these challenges, we propose SaRa, a probabilistic system to estimate the reliability of untrusted edge resources in volunteer cloud. Our validation results showed that SaRa's reputation model obtained better reliability estimation than existing methods.
{"title":"SaRa: A Stochastic Model to Estimate Reliability of Edge Resources in Volunteer Cloud","authors":"Yousef S. Alsenani, G. Crosby, Tomas Velasco","doi":"10.1109/EDGE.2018.00024","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00024","url":null,"abstract":"With the increasing popularity and the need for low-cost green computing systems, new paradigms and models such as fog, edge, and volunteer cloud computing (e.g. cuCloud) have recently emerged. cuCloud, one of the appealing volunteer cloud computing system, share the same philosophy as desktop grid, which runs on underutilized and or spare resources of personal computers (i.e. volunteer hosts) owned by individuals and organizations. On one side of the spectrum, underlying cuCloud infrastructure comprises varying levels of availability, volatility, and trust, allowing volunteers to randomly join and leave the model, which makes the resource management and scheduling of tasks a challenging process. On the other side, it is even more challenging and critical to guarantee the Quality of Service (QoS) for applications deployed in the cuCloud model, which requires the tracking and monitoring the reliability and trust of highly distributed volunteer resources. The majority of the available reputation models consider only the ratio of successfully completed tasks to total tasks requested in the determination of reliability decisions of the volunteer nodes, which, in turn, make the reliability model coarse-grained. These models lack of fine-grained parameters such as task-level behaviors (e.g. success or fail) and task characteristics (e.g. priority of a task). To address these challenges, we propose SaRa, a probabilistic system to estimate the reliability of untrusted edge resources in volunteer cloud. Our validation results showed that SaRa's reputation model obtained better reliability estimation than existing methods.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129624977","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}
The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection.
{"title":"Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection","authors":"U. Sreekumar, Revathy Devaraj, Qi Li, Kaikai Liu","doi":"10.1109/EDGE.2018.00028","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00028","url":null,"abstract":"The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"59 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930555","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}
Location based services inside smart buildings are dependent on scalable localization methods. However, for enterprises, privacy of individual employees is a major concern. In this paper, we present a privacy aware occupancy sensing mechanism for large scale enterprises with multiple floors in multiple buildings of multiple cities. This is achieved through Wi-Fi fingerprint based localization methods implemented on edge devices. We present some preliminary results on occupancy sensing from our pilot study inside the office spaces of IBM India.
{"title":"Enterprise Scale Privacy Aware Occupancy Sensing","authors":"Surya Sajja, Ashok Pon Kumar, Rohun Tripathi, Satyam Dwivedi, Amith Singhee, M. Vermeulen","doi":"10.1109/EDGE.2018.00022","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00022","url":null,"abstract":"Location based services inside smart buildings are dependent on scalable localization methods. However, for enterprises, privacy of individual employees is a major concern. In this paper, we present a privacy aware occupancy sensing mechanism for large scale enterprises with multiple floors in multiple buildings of multiple cities. This is achieved through Wi-Fi fingerprint based localization methods implemented on edge devices. We present some preliminary results on occupancy sensing from our pilot study inside the office spaces of IBM India.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115051972","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}
The transition from virtual machine-based infrastructures to container-based ones brings the promise of swift and efficient software deployment in large-scale computing infrastructures. However, in fog computing environments which are often made of very small computers such as Raspberry PIs, deploying even a very simple Docker container may take multiple minutes. We demonstrate that Docker makes inefficient usage of the available hardware resources, essentially using different hardware subsystems (network bandwidth, CPU, disk I/O) sequentially rather than simultaneously. We therefore propose three optimizations which, once combined, reduce container deployment times by a factor up to 4. These optimizations also speed up deployment time by about 30% in datacenter-grade servers.
{"title":"Docker Container Deployment in Fog Computing Infrastructures","authors":"Arif Ahmed, G. Pierre","doi":"10.1109/EDGE.2018.00008","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00008","url":null,"abstract":"The transition from virtual machine-based infrastructures to container-based ones brings the promise of swift and efficient software deployment in large-scale computing infrastructures. However, in fog computing environments which are often made of very small computers such as Raspberry PIs, deploying even a very simple Docker container may take multiple minutes. We demonstrate that Docker makes inefficient usage of the available hardware resources, essentially using different hardware subsystems (network bandwidth, CPU, disk I/O) sequentially rather than simultaneously. We therefore propose three optimizations which, once combined, reduce container deployment times by a factor up to 4. These optimizations also speed up deployment time by about 30% in datacenter-grade servers.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130435863","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}
A key challenge for smart city analytics is fast extraction, accumulation and processing of sensor data collected from a large number of IoT devices. Edge computing has enabled processing of simple analytics, such as aggregation, geographically closer to the IoT devices to improve latency. However, the throughput of processing in the edge depends on the type of resources available, the number of IoT devices connected and the type of stream analytics performed in the edge. We introduce a framework called Seagull for building efficient, large scale IoT-based applications. Our framework distributes the stream analytics processing tasks to the nodes based on their proximity to the sensor data source as well as the amount of processing the nodes can handle. Our evaluation shows the effect of various stream analytics parameters on the maximum sustainable throughput for a resource-constrained edge device.
{"title":"Large Scale Stream Analytics Using a Resource-Constrained Edge","authors":"R. Das, G. Bernardo, H. Bal","doi":"10.1109/EDGE.2018.00027","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00027","url":null,"abstract":"A key challenge for smart city analytics is fast extraction, accumulation and processing of sensor data collected from a large number of IoT devices. Edge computing has enabled processing of simple analytics, such as aggregation, geographically closer to the IoT devices to improve latency. However, the throughput of processing in the edge depends on the type of resources available, the number of IoT devices connected and the type of stream analytics performed in the edge. We introduce a framework called Seagull for building efficient, large scale IoT-based applications. Our framework distributes the stream analytics processing tasks to the nodes based on their proximity to the sensor data source as well as the amount of processing the nodes can handle. Our evaluation shows the effect of various stream analytics parameters on the maximum sustainable throughput for a resource-constrained edge device.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128184883","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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