Pub Date : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00018
Clemens Lachner, T. Rausch, S. Dustdar
The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to be appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper, we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. The system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure.
{"title":"A Privacy Preserving System for AI-assisted Video Analytics","authors":"Clemens Lachner, T. Rausch, S. Dustdar","doi":"10.1109/ICFEC51620.2021.00018","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00018","url":null,"abstract":"The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to be appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper, we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. The system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131541186","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00017
F. Machida, E. Andrade
Smart drone systems have built-in computing resources for processing real-world images captured by cameras to recognize their surroundings. Due to limited resources and battery constraints, resource-intensive image processing tasks cannot always run on drones. Thus, offloading computation tasks to any available node in a fog computing infrastructure can be considered as a promising solution. An important challenge when applying fog offloading is deciding when to start or stop offloading, taking into account performance and availability impacts under varying workloads and communication link states. In this paper, we present a performability-aware adaptive offloading scheme called PA-Offload that controls the offloading of image processing tasks from a drone to a fog node. To incorporate uncertainty factors, we introduce Stochastic Reward Nets (SRNs) to model the entire system behavior and compute a performability metric that is a composite measure of service throughput and system availability. The estimated performability value is then used to determine when to start or stop the offloading in order to make a better trade-off between performance and availability. Our numerical experiments show the effectiveness of PA-offload in terms of performability compared to non-adaptive fog offloading schemes.
{"title":"PA-Offload: Performability-Aware Adaptive Fog Offloading for Drone Image Processing","authors":"F. Machida, E. Andrade","doi":"10.1109/ICFEC51620.2021.00017","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00017","url":null,"abstract":"Smart drone systems have built-in computing resources for processing real-world images captured by cameras to recognize their surroundings. Due to limited resources and battery constraints, resource-intensive image processing tasks cannot always run on drones. Thus, offloading computation tasks to any available node in a fog computing infrastructure can be considered as a promising solution. An important challenge when applying fog offloading is deciding when to start or stop offloading, taking into account performance and availability impacts under varying workloads and communication link states. In this paper, we present a performability-aware adaptive offloading scheme called PA-Offload that controls the offloading of image processing tasks from a drone to a fog node. To incorporate uncertainty factors, we introduce Stochastic Reward Nets (SRNs) to model the entire system behavior and compute a performability metric that is a composite measure of service throughput and system availability. The estimated performability value is then used to determine when to start or stop the offloading in order to make a better trade-off between performance and availability. Our numerical experiments show the effectiveness of PA-offload in terms of performability compared to non-adaptive fog offloading schemes.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116516963","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00009
Aaron M. Paulos, S. Dasgupta, J. Beal, Yuanqiu Mo, Jon Schewe, Alexander Wald, P. Pal, R. Schantz, J. B. Lyles
Opportunistic managed access to local in-network compute resources can improve the performance of distributed applications and reduce the dependence on shared network resources. Instead of backhauling application data to a centralized cloud data center for processing, networked services may be adaptively and continuously dispersed into shared compute resources that are closer to the source of need. While this approach has several benefits, support for mission-aware access to computation is often an afterthought, and is implemented as a brittle extension over traditional load-balancer solutions.In this work, we investigate the design of two priority-aware resource allocation strategies and two load-balancing dispatching strategies as first class citizens in an open-source dispersed computing middleware. We present a control theoretic analysis of these load-balancing primitives to identify weaknesses and strengths in our design, and recommend future directions. In parallel, we prototype two priority-aware allocation algorithms to validate our priority predictions. In initial experiments our prototype shows substantial gains in processing prioritized load. Finally, we make our source-code and experimental configurations open source.
{"title":"Priority-enabled Load Balancing for Dispersed Computing","authors":"Aaron M. Paulos, S. Dasgupta, J. Beal, Yuanqiu Mo, Jon Schewe, Alexander Wald, P. Pal, R. Schantz, J. B. Lyles","doi":"10.1109/ICFEC51620.2021.00009","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00009","url":null,"abstract":"Opportunistic managed access to local in-network compute resources can improve the performance of distributed applications and reduce the dependence on shared network resources. Instead of backhauling application data to a centralized cloud data center for processing, networked services may be adaptively and continuously dispersed into shared compute resources that are closer to the source of need. While this approach has several benefits, support for mission-aware access to computation is often an afterthought, and is implemented as a brittle extension over traditional load-balancer solutions.In this work, we investigate the design of two priority-aware resource allocation strategies and two load-balancing dispatching strategies as first class citizens in an open-source dispersed computing middleware. We present a control theoretic analysis of these load-balancing primitives to identify weaknesses and strengths in our design, and recommend future directions. In parallel, we prototype two priority-aware allocation algorithms to validate our priority predictions. In initial experiments our prototype shows substantial gains in processing prioritized load. Finally, we make our source-code and experimental configurations open source.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131571702","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00010
Zahra Najafabadi Samani, Nishant Saurabh, R. Prodan
Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications require methods that handle the resource diversity and network structure of Fog devices, while maximizing the service placement and reducing the resource wastage. Prior studies in this domain primarily focused on optimizing application-specific requirements and fail to address the network topology combined with the different types of resources encountered in Fog devices. To overcome these problems, we propose a multilayer resource-aware partitioning method to minimize the resource wastage and maximize the service placement and deadline satisfaction rates in a Fog infrastructure with high multi-user application placement requests. Our method represents the heterogeneous Fog resources as a multilayered network graph and partitions them based on network topology and resource features. Afterwards, it identifies the appropriate device partitions for placing an application according to its requirements, which need to overlap in the same network topology partition. Simulation results show that our multilayer resource-aware partitioning method is able to place twice as many services, satisfy deadlines for three times as many application requests, and reduce the resource wastage by up to 15–32 times compared to two availability-aware and resource-aware state-of-the-art methods.
{"title":"Multilayer Resource-aware Partitioning for Fog Application Placement","authors":"Zahra Najafabadi Samani, Nishant Saurabh, R. Prodan","doi":"10.1109/ICFEC51620.2021.00010","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00010","url":null,"abstract":"Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications require methods that handle the resource diversity and network structure of Fog devices, while maximizing the service placement and reducing the resource wastage. Prior studies in this domain primarily focused on optimizing application-specific requirements and fail to address the network topology combined with the different types of resources encountered in Fog devices. To overcome these problems, we propose a multilayer resource-aware partitioning method to minimize the resource wastage and maximize the service placement and deadline satisfaction rates in a Fog infrastructure with high multi-user application placement requests. Our method represents the heterogeneous Fog resources as a multilayered network graph and partitions them based on network topology and resource features. Afterwards, it identifies the appropriate device partitions for placing an application according to its requirements, which need to overlap in the same network topology partition. Simulation results show that our multilayer resource-aware partitioning method is able to place twice as many services, satisfy deadlines for three times as many application requests, and reduce the resource wastage by up to 15–32 times compared to two availability-aware and resource-aware state-of-the-art methods.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126807840","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00016
Shuangsheng Lou, G. Agrawal
In the context of developing streaming applications for IoT (or Edge Computing) environments, this paper presents a framework for automated deployment with an emphasis on optimizing latency in the presence of resource constraints. A dynamic programming based deployment algorithm is developed to make deployment decisions. With battery power being a key constraint, a major component of our work is a power model to help assess the power consumption of the edge devices at the runtime. Using three applications, we show the large reductions in both power consumption and response latency with our framework, as compared to a baseline involving cloud-only execution.
{"title":"Mapping IoT Applications on the Edge to Cloud Continuum with a Filter Stream Model","authors":"Shuangsheng Lou, G. Agrawal","doi":"10.1109/ICFEC51620.2021.00016","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00016","url":null,"abstract":"In the context of developing streaming applications for IoT (or Edge Computing) environments, this paper presents a framework for automated deployment with an emphasis on optimizing latency in the presence of resource constraints. A dynamic programming based deployment algorithm is developed to make deployment decisions. With battery power being a key constraint, a major component of our work is a power model to help assess the power consumption of the edge devices at the runtime. Using three applications, we show the large reductions in both power consumption and response latency with our framework, as compared to a baseline involving cloud-only execution.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129722629","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00014
Theodoros Kasidakis, Giorgos Polychronis, Manos Koutsoubelias, S. Lalis
In data-driven applications, which go beyond simple data collection, drones may need to process sensor measurements at certain locations, during the mission. However, the onboard computing platforms typically have strong resource limitations, which may lead to significant delays and long mission times. To address this problem, we explore the potential of offloading heavyweight computations from the drone to nearby edge computing infrastructure. We discuss a concrete implementation for a service-oriented application software stack, which takes offloading decisions based on the expected service invocation time and the locations of the servers expected to be available in the mission area. We evaluate our implementation using an experimental setup that combines a hardware-in-the-loop and software-in-the-loop configuration. Our results show that the proposed approach can reduce the total mission time significantly, by up to 48% vs local-only processing, and by 10% vs more naive opportunistic offloading, depending on the mission scenario.
{"title":"Reducing the Mission Time of Drone Applications through Location-Aware Edge Computing","authors":"Theodoros Kasidakis, Giorgos Polychronis, Manos Koutsoubelias, S. Lalis","doi":"10.1109/ICFEC51620.2021.00014","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00014","url":null,"abstract":"In data-driven applications, which go beyond simple data collection, drones may need to process sensor measurements at certain locations, during the mission. However, the onboard computing platforms typically have strong resource limitations, which may lead to significant delays and long mission times. To address this problem, we explore the potential of offloading heavyweight computations from the drone to nearby edge computing infrastructure. We discuss a concrete implementation for a service-oriented application software stack, which takes offloading decisions based on the expected service invocation time and the locations of the servers expected to be available in the mission area. We evaluate our implementation using an experimental setup that combines a hardware-in-the-loop and software-in-the-loop configuration. Our results show that the proposed approach can reduce the total mission time significantly, by up to 48% vs local-only processing, and by 10% vs more naive opportunistic offloading, depending on the mission scenario.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882192","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00011
Lei Wang, Mahdi Dolati, Majid Ghaderi
In Mobile Edge Computing (MEC), the network’s edge is equipped with computing and storage resources in order to reduce latency by minimizing communication with remote clouds. However, the available computing capacity at the edge is limited compared to that of remote clouds. A promising solution for efficient utilization of the limited capacity at the edge is fine-grained processing of user demands via Virtual Network Functions (VNFs). In this approach, user service demands are expressed as Service Function Chains (SFCs), which are composed of virtual network functions. Such service composition allows constituent VNFs to be flexibly deployed at the edge or in the cloud such that the service latency is minimized. The increasing number of users, however, challenges the scalability of system-managed SFC orchestration. To address this problem, we propose a user-managed online SFC orchestration framework at the edge of the network, called CHANGE, that minimizes service latency by jointly considering the effect of user mobility, edge capacity and service migration. We first present the theoretical foundations of CHANGE and then evaluate its performance via model-driven simulations and realistic Mininet-WiFi emulations. Our results show that CHANGE can improve latency performance by nearly 20% compared to other approaches.
{"title":"CHANGE: Delay-Aware Service Function Chain Orchestration at the Edge","authors":"Lei Wang, Mahdi Dolati, Majid Ghaderi","doi":"10.1109/ICFEC51620.2021.00011","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00011","url":null,"abstract":"In Mobile Edge Computing (MEC), the network’s edge is equipped with computing and storage resources in order to reduce latency by minimizing communication with remote clouds. However, the available computing capacity at the edge is limited compared to that of remote clouds. A promising solution for efficient utilization of the limited capacity at the edge is fine-grained processing of user demands via Virtual Network Functions (VNFs). In this approach, user service demands are expressed as Service Function Chains (SFCs), which are composed of virtual network functions. Such service composition allows constituent VNFs to be flexibly deployed at the edge or in the cloud such that the service latency is minimized. The increasing number of users, however, challenges the scalability of system-managed SFC orchestration. To address this problem, we propose a user-managed online SFC orchestration framework at the edge of the network, called CHANGE, that minimizes service latency by jointly considering the effect of user mobility, edge capacity and service migration. We first present the theoretical foundations of CHANGE and then evaluate its performance via model-driven simulations and realistic Mininet-WiFi emulations. Our results show that CHANGE can improve latency performance by nearly 20% compared to other approaches.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"128 15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130048310","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00020
Ioanna-Vasiliki Stypsanelli, O. Brun, B. Prabhu
Fog Computing brings resources closer to the end-user and improves user experience. Tasks with stringent QoS requirements can be processed locally in the Edge while the more elastic ones can be sent to the Cloud. For the benefits of this flexible architecture to be seen, task allocation algorithms should be dynamic and adapt to the load in the Fog and in the Cloud. Using a discrete-event simulation approach, we evaluate the performance of four simple adaptive algorithms based on congestion estimation and compare them with the standard nearest node algorithm that uses non adaptive routing. We consider a setting in which base stations (access nodes) forward traffic to computing nodes (Fog and Cloud nodes) in a distributed way without coordination and sharing of state-information between the access and computing nodes. The algorithms are tested for their adaptability to sudden changes in the arrival rate of requests (to model peak hours) as well as robustness to the variance of the request-size distributions to understand the advantages and drawbacks of each of them. They are shown to perform well in scenarios with and without offloading.
{"title":"Performance Evaluation of Some Adaptive Task Allocation Algorithms for Fog Networks","authors":"Ioanna-Vasiliki Stypsanelli, O. Brun, B. Prabhu","doi":"10.1109/ICFEC51620.2021.00020","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00020","url":null,"abstract":"Fog Computing brings resources closer to the end-user and improves user experience. Tasks with stringent QoS requirements can be processed locally in the Edge while the more elastic ones can be sent to the Cloud. For the benefits of this flexible architecture to be seen, task allocation algorithms should be dynamic and adapt to the load in the Fog and in the Cloud. Using a discrete-event simulation approach, we evaluate the performance of four simple adaptive algorithms based on congestion estimation and compare them with the standard nearest node algorithm that uses non adaptive routing. We consider a setting in which base stations (access nodes) forward traffic to computing nodes (Fog and Cloud nodes) in a distributed way without coordination and sharing of state-information between the access and computing nodes. The algorithms are tested for their adaptability to sudden changes in the arrival rate of requests (to model peak hours) as well as robustness to the variance of the request-size distributions to understand the advantages and drawbacks of each of them. They are shown to perform well in scenarios with and without offloading.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368992","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 : 2021-05-01DOI: 10.1109/ICFEC51620.2021.00015
JunKyu Lee, B. Varghese, Roger Francis Woods, H. Vandierendonck
Real-time video analytics on the edge is challenging as the computationally constrained resources typically cannot analyse video streams at full fidelity and frame rate, which results in loss of accuracy. This paper proposes a Transprecise Object Detector (TOD) which maximises the real-time object detection accuracy on an edge device by selecting an appropriate Deep Neural Network (DNN) on the fly with negligible computational overhead. TOD makes two key contributions over the state of the art: (1) TOD leverages characteristics of the video stream such as object size and speed of movement to identify networks with high prediction accuracy for the current frames; (2) it selects the best-performing network based on projected accuracy and computational demand using an effective and low-overhead decision mechanism. Experimental evaluation on a Jetson Nano demonstrates that TOD improves the average object detection precision by 34.7% over the YOLOv4-tiny-288 model on average over the MOT17Det dataset. In the MOT17-05 test dataset, TOD utilises only 45.1% of GPU resource and 62.7% of the GPU board power without losing accuracy, compared to YOLOv4-416 model. We expect that TOD will maximise the application of edge devices to real-time object detection, since TOD maximises real-time object detection accuracy given edge devices according to dynamic input features without increasing inference latency in practice.
{"title":"TOD: Transprecise Object Detection to Maximise Real-Time Accuracy on the Edge","authors":"JunKyu Lee, B. Varghese, Roger Francis Woods, H. Vandierendonck","doi":"10.1109/ICFEC51620.2021.00015","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00015","url":null,"abstract":"Real-time video analytics on the edge is challenging as the computationally constrained resources typically cannot analyse video streams at full fidelity and frame rate, which results in loss of accuracy. This paper proposes a Transprecise Object Detector (TOD) which maximises the real-time object detection accuracy on an edge device by selecting an appropriate Deep Neural Network (DNN) on the fly with negligible computational overhead. TOD makes two key contributions over the state of the art: (1) TOD leverages characteristics of the video stream such as object size and speed of movement to identify networks with high prediction accuracy for the current frames; (2) it selects the best-performing network based on projected accuracy and computational demand using an effective and low-overhead decision mechanism. Experimental evaluation on a Jetson Nano demonstrates that TOD improves the average object detection precision by 34.7% over the YOLOv4-tiny-288 model on average over the MOT17Det dataset. In the MOT17-05 test dataset, TOD utilises only 45.1% of GPU resource and 62.7% of the GPU board power without losing accuracy, compared to YOLOv4-416 model. We expect that TOD will maximise the application of edge devices to real-time object detection, since TOD maximises real-time object detection accuracy given edge devices according to dynamic input features without increasing inference latency in practice.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114773997","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 : 2021-04-07DOI: 10.1109/ICFEC51620.2021.00019
André Luckow, Kartik Rattan, S. Jha
A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructure from the edge to the cloud to provide responsive, intelligent services while complying with privacy and security requirements. However, the integration of heterogeneous IoT, edge, and cloud technologies and the design of end-to-end applications that seamlessly work across multiple layers and types of infrastructures is challenging. A significant issue is resource management and the need to ensure that the right type and scale of resources is allocated on every layer to fulfill the application’s processing needs. As edge and cloud layers are increasingly tightly integrated, imbalanced resource allocations and sub-optimally placed tasks can quickly deteriorate the overall system performance. This paper proposes an emulation approach for the investigation of task placements across the edge-to-cloud continuum. We demonstrate that emulation can address the complexity and many degrees-of-freedom of the problem, allowing us to investigate essential deployment patterns and trade-offs. We evaluate our approach using a machine learning-based workload, demonstrating the validity by comparing emulation and real-world experiments. Further, we show that the right task placement strategy has a significant impact on performance – in our experiments, between 5% and 65% depending on the scenario.
{"title":"Exploring Task Placement for Edge-to-Cloud Applications using Emulation","authors":"André Luckow, Kartik Rattan, S. Jha","doi":"10.1109/ICFEC51620.2021.00019","DOIUrl":"https://doi.org/10.1109/ICFEC51620.2021.00019","url":null,"abstract":"A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructure from the edge to the cloud to provide responsive, intelligent services while complying with privacy and security requirements. However, the integration of heterogeneous IoT, edge, and cloud technologies and the design of end-to-end applications that seamlessly work across multiple layers and types of infrastructures is challenging. A significant issue is resource management and the need to ensure that the right type and scale of resources is allocated on every layer to fulfill the application’s processing needs. As edge and cloud layers are increasingly tightly integrated, imbalanced resource allocations and sub-optimally placed tasks can quickly deteriorate the overall system performance. This paper proposes an emulation approach for the investigation of task placements across the edge-to-cloud continuum. We demonstrate that emulation can address the complexity and many degrees-of-freedom of the problem, allowing us to investigate essential deployment patterns and trade-offs. We evaluate our approach using a machine learning-based workload, demonstrating the validity by comparing emulation and real-world experiments. Further, we show that the right task placement strategy has a significant impact on performance – in our experiments, between 5% and 65% depending on the scenario.","PeriodicalId":436220,"journal":{"name":"2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126739384","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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