Pub Date : 2020-11-01DOI: 10.1109/SEC50012.2020.00041
Lanyu Xu, A. Iyengar, Weisong Shi
Voice assistant systems are becoming immersive in our daily lives nowadays. However, current voice assistant systems rely on the cloud for command understanding and fulfillment, resulting in unstable performance and unnecessary frequent network transmission. In this paper, we introduce CHA, an edge-based caching framework for voice assistant systems, and especially for smart homes where resource-restricted edge devices can be deployed. Located between the voice assistant device and the cloud, CHA introduces a layered architecture with modular design in each layer. By introducing an understanding module and adaptive learning, CHA understands the user’s intent with high accuracy. By maintaining a cache, CHA reduces the interaction with the cloud and provides fast and stable responses in a smart home. Targeting on resource-constrained edge devices, CHA uses joint classification and model pruning on a pre-trained language model to achieve performance and system efficiency. We compare CHA to the status quo solution of voice assistant systems and show that CHA benefits voice assistant systems. We evaluate CHA on three edge devices that differ in hardware configuration and demonstrate its ability to meet the latency and accuracy demands with efficient resource utilization. Our evaluation shows that compared to the current solution for voice assistant systems, CHA can provide at least 70% speedup in responses for frequently asked voice commands with less than 13% CPU consumption, and less than 9% memory consumption when running on a Raspberry Pi.
{"title":"CHA: A Caching Framework for Home-based Voice Assistant Systems","authors":"Lanyu Xu, A. Iyengar, Weisong Shi","doi":"10.1109/SEC50012.2020.00041","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00041","url":null,"abstract":"Voice assistant systems are becoming immersive in our daily lives nowadays. However, current voice assistant systems rely on the cloud for command understanding and fulfillment, resulting in unstable performance and unnecessary frequent network transmission. In this paper, we introduce CHA, an edge-based caching framework for voice assistant systems, and especially for smart homes where resource-restricted edge devices can be deployed. Located between the voice assistant device and the cloud, CHA introduces a layered architecture with modular design in each layer. By introducing an understanding module and adaptive learning, CHA understands the user’s intent with high accuracy. By maintaining a cache, CHA reduces the interaction with the cloud and provides fast and stable responses in a smart home. Targeting on resource-constrained edge devices, CHA uses joint classification and model pruning on a pre-trained language model to achieve performance and system efficiency. We compare CHA to the status quo solution of voice assistant systems and show that CHA benefits voice assistant systems. We evaluate CHA on three edge devices that differ in hardware configuration and demonstrate its ability to meet the latency and accuracy demands with efficient resource utilization. Our evaluation shows that compared to the current solution for voice assistant systems, CHA can provide at least 70% speedup in responses for frequently asked voice commands with less than 13% CPU consumption, and less than 9% memory consumption when running on a Raspberry Pi.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"34 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":"116798024","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/SEC50012.2020.00044
A. García, D. May, E. Nutting
The shift towards edge computing is bringing data processing and storage closer to the edge of the network. This makes it desirable to use productive modern programming languages, like Python and C#, to program edge devices. Modern programming languages mitigate the added complexity of edge computing by abstracting software developers away from tedious tasks like freeing unused memory. But these languages rely on garbage collectors that impose high overheads and introduce unpredictable pauses, so they are rarely used in small embedded systems that make up the majority of edge devices. We propose a novel hardware garbage collector that addresses these problems to unlock the benefits of modern languages in edge devices.
{"title":"Garbage Collection for Edge Computing","authors":"A. García, D. May, E. Nutting","doi":"10.1109/SEC50012.2020.00044","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00044","url":null,"abstract":"The shift towards edge computing is bringing data processing and storage closer to the edge of the network. This makes it desirable to use productive modern programming languages, like Python and C#, to program edge devices. Modern programming languages mitigate the added complexity of edge computing by abstracting software developers away from tedious tasks like freeing unused memory. But these languages rely on garbage collectors that impose high overheads and introduce unpredictable pauses, so they are rarely used in small embedded systems that make up the majority of edge devices. We propose a novel hardware garbage collector that addresses these problems to unlock the benefits of modern languages in edge devices.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"30 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":"116860655","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/SEC50012.2020.00069
Xiang Chen, Zhuwei Qin
Recent progress in deep learning techniques enabled collaborative edge training, which usually deploys identical neural network models globally on multiple devices for aggregating parameter updates over distributed data collection. However, as more and more heterogeneous edge devices are involved in practical training, the identical model deployment over collaborative edge devices cannot be guaranteed: On one hand, the weak edge devices with less computation resources may not catch up stronger ones’ training progress, and appropriate local model training customization is necessary to balance the collaboration. On the other hand, a particular local edge device may have specific learning task preference, while the global identical model would exceed the practical local demand and cause unnecessary computation cost. Therefore, we explored the collaborative learning with heterogeneous convolutional neural networks (CNNs) in this work, expecting to address aforementioned real problems. Specifically, we proposed a novel decentralized collaborative training method by decoupling a training target CNN model into independently trainable sub-models correspond to a sub-set of learning tasks for each edge device. After sub-models are well-trained on edge nodes, the model parameters for individual learning tasks can be harvested from local models on every edge device and ensemble the global training model back to a single piece. Experiments demonstrate that, for the AlexNet and VGG on the CIFAR10, CIFAR100 and KWS dataset, our decentralized training method can save up to 11.8× less computation load while achieve central sever test accuracy.
{"title":"Exploring Decentralized Collaboration in Heterogeneous Edge Training","authors":"Xiang Chen, Zhuwei Qin","doi":"10.1109/SEC50012.2020.00069","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00069","url":null,"abstract":"Recent progress in deep learning techniques enabled collaborative edge training, which usually deploys identical neural network models globally on multiple devices for aggregating parameter updates over distributed data collection. However, as more and more heterogeneous edge devices are involved in practical training, the identical model deployment over collaborative edge devices cannot be guaranteed: On one hand, the weak edge devices with less computation resources may not catch up stronger ones’ training progress, and appropriate local model training customization is necessary to balance the collaboration. On the other hand, a particular local edge device may have specific learning task preference, while the global identical model would exceed the practical local demand and cause unnecessary computation cost. Therefore, we explored the collaborative learning with heterogeneous convolutional neural networks (CNNs) in this work, expecting to address aforementioned real problems. Specifically, we proposed a novel decentralized collaborative training method by decoupling a training target CNN model into independently trainable sub-models correspond to a sub-set of learning tasks for each edge device. After sub-models are well-trained on edge nodes, the model parameters for individual learning tasks can be harvested from local models on every edge device and ensemble the global training model back to a single piece. Experiments demonstrate that, for the AlexNet and VGG on the CIFAR10, CIFAR100 and KWS dataset, our decentralized training method can save up to 11.8× less computation load while achieve central sever test accuracy.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"25 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":"114892210","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/SEC50012.2020.00032
R. Figueiredo, Kensworth C. Subratie
Edge and fog computing encompass a variety of technologies that are poised to enable new applications across the Internet that support data capture, storage, processing, and communication across the networking continuum. These environments pose new challenges to the design and implementation of networks-as membership can be dynamic and devices are heterogeneous, widely distributed geographically, and in proximity to end-users, as is the case with mobile and Internet-of-Things (IoT) devices. We present a demonstration of EdgeVPN.io (Evio for short), an open-source programmable, software-defined network that addresses challenges in the deployment of virtual networks spanning distributed edge and cloud resources, in particular highlighting its use in support of the Kubernetes container orchestration middleware. The demo highlights a deployment of unmodified Kubernetes middleware across a virtual cluster comprising virtual machines deployed both in cloud providers, and in distinct networks at the edge-where all nodes are assigned private IP addresses and subject to different NAT (Network Address Translation) middleboxes, connected through an Evio virtual network. The demo includes an overview of the configuration of Kubernetes and Evio nodes and the deployment of Docker-based container pods, highlighting the seamless connectivity for TCP/IP applications deployed on the pods.
{"title":"Demo: EdgeVPN.io: Open-source Virtual Private Network for Seamless Edge Computing with Kubernetes","authors":"R. Figueiredo, Kensworth C. Subratie","doi":"10.1109/SEC50012.2020.00032","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00032","url":null,"abstract":"Edge and fog computing encompass a variety of technologies that are poised to enable new applications across the Internet that support data capture, storage, processing, and communication across the networking continuum. These environments pose new challenges to the design and implementation of networks-as membership can be dynamic and devices are heterogeneous, widely distributed geographically, and in proximity to end-users, as is the case with mobile and Internet-of-Things (IoT) devices. We present a demonstration of EdgeVPN.io (Evio for short), an open-source programmable, software-defined network that addresses challenges in the deployment of virtual networks spanning distributed edge and cloud resources, in particular highlighting its use in support of the Kubernetes container orchestration middleware. The demo highlights a deployment of unmodified Kubernetes middleware across a virtual cluster comprising virtual machines deployed both in cloud providers, and in distinct networks at the edge-where all nodes are assigned private IP addresses and subject to different NAT (Network Address Translation) middleboxes, connected through an Evio virtual network. The demo includes an overview of the configuration of Kubernetes and Evio nodes and the deployment of Docker-based container pods, highlighting the seamless connectivity for TCP/IP applications deployed on the pods.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"27 3 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":"116425387","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/SEC50012.2020.00031
Moysis Symeonides, Z. Georgiou, Demetris Trihinas, G. Pallis, M. Dikaiakos
For more than the better parts of the last decades, we are witnessing the proliferation of IoT devices, as well as an exponential growth in the volume of data generated outside of datacenters. With the generated data at the extremes of the network and the restricted device-to-cloud bandwidth, data mitigation is becoming the major barrier of cloud-based IoT services [1]. To alleviate these challenges, Fog Computing extends the Cloud’s capabilities closer to IoT devices.
{"title":"Demo: Emulating Geo-Distributed Fog Services","authors":"Moysis Symeonides, Z. Georgiou, Demetris Trihinas, G. Pallis, M. Dikaiakos","doi":"10.1109/SEC50012.2020.00031","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00031","url":null,"abstract":"For more than the better parts of the last decades, we are witnessing the proliferation of IoT devices, as well as an exponential growth in the volume of data generated outside of datacenters. With the generated data at the extremes of the network and the restricted device-to-cloud bandwidth, data mitigation is becoming the major barrier of cloud-based IoT services [1]. To alleviate these challenges, Fog Computing extends the Cloud’s capabilities closer to IoT devices.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"38 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":"117082646","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/SEC50012.2020.00027
Jun Lin Chen, D. Liaqat, Moshe Gabel, E. D. Lara
Containers are an emerging approach for application deployment on the edge, as they are modular, lightweight, and easy to use for development and maintenance. However, deploying containers in an edge computing environment brings new challenges: high latency links, limited resources, and user mobility. This work proposes a new edge deployment architecture that accelerates deployment and updates for edge applications. By overcoming the design limitations of current registries, the accelerator would reduce the deployment, start-up, and update times of container-based applications.
{"title":"Poster: An Accelerator for Fast Container-based Applications Deployment on the Edge","authors":"Jun Lin Chen, D. Liaqat, Moshe Gabel, E. D. Lara","doi":"10.1109/SEC50012.2020.00027","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00027","url":null,"abstract":"Containers are an emerging approach for application deployment on the edge, as they are modular, lightweight, and easy to use for development and maintenance. However, deploying containers in an edge computing environment brings new challenges: high latency links, limited resources, and user mobility. This work proposes a new edge deployment architecture that accelerates deployment and updates for edge applications. By overcoming the design limitations of current registries, the accelerator would reduce the deployment, start-up, and update times of container-based applications.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"111 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":"130671208","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/SEC50012.2020.00059
S. Müksch, Theo X. Olausson, John Wilhelm, Pavlos Andreadis
Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in edge computing applications, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to 98.2% accuracy on optical character recognition using MNIST-10, with a memory footprint as little as 6KB. However, their potential on more complex multi-class and multi-channel image classification has yet to be determined. In this paper, we compare CNNs with ProtoNN, Bonsai and FastGRNN when applied to 3-channel image classification using CIFAR-10. For our analysis, we use the existing Direct Convolution algorithm to implement the CNNs memory-optimally and propose new methods of adjusting the FastGRNN model to work with multi-channel images. We extend the evaluation of each algorithm to a memory size budget of 8KB, 16KB, 32KB, 64KB and 128KB to show quantitatively that Direct Convolution CNNs perform best for all chosen budgets, with a top performance of 65.7% accuracy at a memory footprint of 58.23KB.
{"title":"Benchmarking the Accuracy of Algorithms for Memory-Constrained Image Classification","authors":"S. Müksch, Theo X. Olausson, John Wilhelm, Pavlos Andreadis","doi":"10.1109/SEC50012.2020.00059","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00059","url":null,"abstract":"Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in edge computing applications, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to 98.2% accuracy on optical character recognition using MNIST-10, with a memory footprint as little as 6KB. However, their potential on more complex multi-class and multi-channel image classification has yet to be determined. In this paper, we compare CNNs with ProtoNN, Bonsai and FastGRNN when applied to 3-channel image classification using CIFAR-10. For our analysis, we use the existing Direct Convolution algorithm to implement the CNNs memory-optimally and propose new methods of adjusting the FastGRNN model to work with multi-channel images. We extend the evaluation of each algorithm to a memory size budget of 8KB, 16KB, 32KB, 64KB and 128KB to show quantitatively that Direct Convolution CNNs perform best for all chosen budgets, with a top performance of 65.7% accuracy at a memory footprint of 58.23KB.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","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":"121133438","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/SEC50012.2020.00018
Kwame-Lante Wright, A. Sivakumar, P. Steenkiste, Bo Yu, F. Bai
Vehicular applications are becoming increasingly complex and resource hungry (e.g. autonomous driving). Today, they run entirely on the vehicle, which is a costly solution that also imposes undesirable resource constraints. This paper uses Simultaneous Localization and Mapping (SLAM) as an example application to explore how these applications can instead leverage edge clouds, utilizing their inexpensive and elastic resource pool. This is challenging as these applications are often latency-sensitive and mission-critical. They also process high-bandwidth sensor data streams and maintain large, complex data structures. As a result, traditional offloading techniques generate too much traffic, incurring high delay. To overcome these challenges, we designed CloudSLAM. It partitions SLAM between the vehicle and the edge. To manage the complex, replicated SLAM state, we propose a new consistency model, Output-driven Consistency, that allows us to maintain a level of consistency that is sufficient for accurate SLAM output while minimizing network traffic. This paper motivates and describes our offloading design and discusses the results of an extensive performance evaluation of a CloudSLAM prototype based on ORB-SLAM.
{"title":"CloudSLAM: Edge Offloading of Stateful Vehicular Applications","authors":"Kwame-Lante Wright, A. Sivakumar, P. Steenkiste, Bo Yu, F. Bai","doi":"10.1109/SEC50012.2020.00018","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00018","url":null,"abstract":"Vehicular applications are becoming increasingly complex and resource hungry (e.g. autonomous driving). Today, they run entirely on the vehicle, which is a costly solution that also imposes undesirable resource constraints. This paper uses Simultaneous Localization and Mapping (SLAM) as an example application to explore how these applications can instead leverage edge clouds, utilizing their inexpensive and elastic resource pool. This is challenging as these applications are often latency-sensitive and mission-critical. They also process high-bandwidth sensor data streams and maintain large, complex data structures. As a result, traditional offloading techniques generate too much traffic, incurring high delay. To overcome these challenges, we designed CloudSLAM. It partitions SLAM between the vehicle and the edge. To manage the complex, replicated SLAM state, we propose a new consistency model, Output-driven Consistency, that allows us to maintain a level of consistency that is sufficient for accurate SLAM output while minimizing network traffic. This paper motivates and describes our offloading design and discusses the results of an extensive performance evaluation of a CloudSLAM prototype based on ORB-SLAM.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"159 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":"128084615","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/SEC50012.2020.00013
Chanh Nguyen, C. Klein, E. Elmroth
Elasticity is a fundamental property required for Mobile Edge Clouds (MECs) to become mature computing platforms hosting software applications. However, MECs must cope with several challenges that do not arise in the context of conventional cloud platforms. These include the potentially highly distributed geographical deployment, heterogeneity, and limited resource capacity of Edge Data Centers (EDCs), and end-user mobility.In this paper, we present an elasticity controller to help MECs overcome these challenges by automatic proactive resource scaling. The controller utilizes information on the physical locations of EDCs and the correlation of workload changes in physically neighboring EDCs to predict request arrival rates at EDCs. These predictions are used as inputs for a queueing theory-driven performance model that estimates the number of resources that should be provisioned to EDCs in order to meet predefined Service Level Objectives (SLOs) while maximizing resource utilization. The controller also incorporates a grouplevel load balancer that is responsible for redirecting requests among EDCs during runtime so as to minimize the request rejection rate. We evaluate our approach by performing simulations with an emulated MEC deployed over a metropolitan area and a simulated application workload using a real-world user mobility trace. The results show that our proposed pro-active controller exhibits better scaling behavior than a state-of-the-art re-active controller and increases the efficiency of resource provisioning, thereby helping MECs to sustain resource utilization and rejection rates that satisfy predefined SLOs while maintaining system stability.
{"title":"Elasticity Control for Latency-Intolerant Mobile Edge Applications","authors":"Chanh Nguyen, C. Klein, E. Elmroth","doi":"10.1109/SEC50012.2020.00013","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00013","url":null,"abstract":"Elasticity is a fundamental property required for Mobile Edge Clouds (MECs) to become mature computing platforms hosting software applications. However, MECs must cope with several challenges that do not arise in the context of conventional cloud platforms. These include the potentially highly distributed geographical deployment, heterogeneity, and limited resource capacity of Edge Data Centers (EDCs), and end-user mobility.In this paper, we present an elasticity controller to help MECs overcome these challenges by automatic proactive resource scaling. The controller utilizes information on the physical locations of EDCs and the correlation of workload changes in physically neighboring EDCs to predict request arrival rates at EDCs. These predictions are used as inputs for a queueing theory-driven performance model that estimates the number of resources that should be provisioned to EDCs in order to meet predefined Service Level Objectives (SLOs) while maximizing resource utilization. The controller also incorporates a grouplevel load balancer that is responsible for redirecting requests among EDCs during runtime so as to minimize the request rejection rate. We evaluate our approach by performing simulations with an emulated MEC deployed over a metropolitan area and a simulated application workload using a real-world user mobility trace. The results show that our proposed pro-active controller exhibits better scaling behavior than a state-of-the-art re-active controller and increases the efficiency of resource provisioning, thereby helping MECs to sustain resource utilization and rejection rates that satisfy predefined SLOs while maintaining system stability.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"37 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":"117164376","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/SEC50012.2020.00039
S. H. Mortazavi, Mohammad Salehe, Moshe Gabel, E. D. Lara
In many edge computing scenarios data is generated over a wide geographic area and is stored near the edges, before being pushed upstream to a hierarchy of data centers. Querying such geo-distributed data traditionally falls into two general approaches: push incoming queries down to the edge where the data is, or run them locally in the cloud.Feather is a hybrid querying scheme that exploits the hierarchical structure of such geo-distributed systems to trade temporal accuracy (freshness) for improved latency and reduced bandwidth. Rather than pushing queries to the edge or executing them in the cloud, Feather selectively pushes queries towards the edge while guaranteeing a user-supplied per-query freshness limit. Partial results are then aggregated along the path to the cloud, until a final result is provided with guaranteed freshness.We evaluate Feather in controlled experiments using real-world geo-tagged traces, as well as a real system running across 10 datacenters in 3 continents. Feather combines the best of cloud and edge execution, answering queries with a fraction of edge latency, providing fresher answers than cloud, while reducing network bandwidth and load on edges.
{"title":"Feather: Hierarchical Querying for the Edge","authors":"S. H. Mortazavi, Mohammad Salehe, Moshe Gabel, E. D. Lara","doi":"10.1109/SEC50012.2020.00039","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00039","url":null,"abstract":"In many edge computing scenarios data is generated over a wide geographic area and is stored near the edges, before being pushed upstream to a hierarchy of data centers. Querying such geo-distributed data traditionally falls into two general approaches: push incoming queries down to the edge where the data is, or run them locally in the cloud.Feather is a hybrid querying scheme that exploits the hierarchical structure of such geo-distributed systems to trade temporal accuracy (freshness) for improved latency and reduced bandwidth. Rather than pushing queries to the edge or executing them in the cloud, Feather selectively pushes queries towards the edge while guaranteeing a user-supplied per-query freshness limit. Partial results are then aggregated along the path to the cloud, until a final result is provided with guaranteed freshness.We evaluate Feather in controlled experiments using real-world geo-tagged traces, as well as a real system running across 10 datacenters in 3 continents. Feather combines the best of cloud and edge execution, answering queries with a fraction of edge latency, providing fresher answers than cloud, while reducing network bandwidth and load on edges.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"140 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":"124688773","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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