arXiv subjects

Blesson Varghese

Publications and source records attributed to Blesson Varghese.

At least 37 records · Page 2Linked to original sources

arXiv2022

FedFly: Towards Migration in Edge-based Distributed Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource constrained, offloading can be used to improve FL performance by transferring computational workload from devices to edge servers. However, due to mobility, devices participating in FL may leave the network during training and need to connect to a different edge server. This is challenging because the offloaded computations from edge server need to be migrated. In line with this assertion, we present FedFly, which is, to the best of our knowledge, the first work to migrate a deep neural network (DNN) when devices move between edge servers during FL training. Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL. FedFly has negligible overhead of up to two seconds and does not compromise accuracy. Finally, we highlight a number of open research issues for further investigation. FedFly can be downloaded from https://github.com/qub-blesson/FedFly.

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arXiv2022

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: (i) execution on devices with limited computational capabilities, (ii) accounting for stragglers due to computational heterogeneity of devices, and (iii) adaptation to the changing network bandwidths. This paper presents FedAdapt, an adaptive offloading FL framework to mitigate the aforementioned challenges. FedAdapt accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. Experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server FedAdapt reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, FedAdapt is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.

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arXiv2022

CONTINUER: Maintaining Distributed DNN Services During Edge Failures

Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may result in cascading failures that will adversely impact the delivery of the service and will result in failure to meet specific objectives. The impact of these failures needs to be minimised at runtime. Three techniques are explored in this paper, namely repartitioning, early-exit and skip-connection. When an edge node fails, the repartitioning technique will repartition and redeploy the DNN thus avoiding the failed nodes. The early-exit technique makes provision for a request to exit (early) before the failed node. The skip connection technique dynamically routes the request by skipping the failed nodes. This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails. To this end, CONTINUER is developed. Two key activities of the framework are estimating the accuracy and latency when using the techniques for distributed DNNs and selecting the best technique. It is demonstrated on a lab-based experimental testbed that CONTINUER estimates accuracy and latency when using the techniques with no more than an average error of 0.28% and 13.06%, respectively and selects the suitable technique with a low overhead of no more than 16.82 milliseconds and an accuracy of up to 99.86%.

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arXiv2021

Roadmap for Edge AI: A Dagstuhl Perspective

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.

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arXiv2021

QUDOS: Quorum-Based Cloud-Edge Distributed DNNs for Security Enhanced Industry 4.0

Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.

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arXiv2021

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on 'Dynamic Switching' in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Two dynamic switching scenarios are considered: when a second edge-cloud pipeline is always running and when a second pipeline is only initialised when the network speed changes. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.

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arXiv2021

Revisiting the Arguments for Edge Computing Research

This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for edge computing research even though its initial concepts were formulated more than a decade ago.

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arXiv2021

TOD: Transprecise Object Detection to Maximise Real-Time Accuracy on the Edge

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.

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arXiv2021

ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer

Industrial Internet of Things (IIoT) applications can benefit from leveraging edge computing. For example, applications underpinned by deep neural networks (DNN) models can be sliced and distributed across the IIoT device and the edge of the network for improving the overall performance of inference and for enhancing privacy of the input data, such as industrial product images. However, low network performance between IIoT devices and the edge is often a bottleneck. In this study, we develop ScissionLite, a holistic framework for accelerating distributed DNN inference using the Transfer Layer (TL). The TL is a traffic-aware layer inserted between the optimal slicing point of a DNN model slice in order to decrease the outbound network traffic without a significant accuracy drop. For the TL, we implement a new lightweight down/upsampling network for performance-limited IIoT devices. In ScissionLite, we develop ScissionTL, the Preprocessor, and the Offloader for end-to-end activities for deploying DNN slices with the TL. They decide the optimal slicing point of the DNN, prepare pre-trained DNN slices including the TL, and execute the DNN slices on an IIoT device and the edge. Employing the TL for the sliced DNN models has a negligible overhead. ScissionLite improves the inference latency by up to 16 and 2.8 times when compared to execution on the local device and an existing state-of-the-art model slicing approach respectively.

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arXiv2021

AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Edge computing offers the distinct advantage of harnessing compute capabilities on resources located at the edge of the network to run workloads of relatively weak user devices. This is achieved by offloading computationally intensive workloads, such as deep learning from user devices to the edge. Using the edge reduces the overall communication latency of applications as workloads can be processed closer to where data is generated on user devices rather than sending them to geographically distant clouds. Specialised hardware accelerators, such as Graphics Processing Units (GPUs) available in the cloud-edge network can enhance the performance of computationally intensive workloads that are offloaded from devices on to the edge. The underlying approach required to facilitate this is virtualization of GPUs. This paper therefore sets out to investigate the potential of GPU accelerator virtualization to improve the performance of deep learning workloads in a cloud-edge environment. The AVEC accelerator virtualization framework is proposed that incurs minimum overheads and requires no source-code modification of the workload. AVEC intercepts local calls to a GPU on a device and forwards them to an edge resource seamlessly. The feasibility of AVEC is demonstrated on a real-world application, namely OpenPose using the Caffe deep learning library. It is observed that on a lab-based experimental test-bed AVEC delivers up to 7.48x speedup despite communication overheads incurred due to data transfers.

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arXiv2020

A Survey on Edge Performance Benchmarking

Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However, given the loosely coupled nature of such complex systems, their operational conditions are expected to change significantly over time. In this context, the performance characteristics of such systems will need to be captured rapidly, which is referred to as performance benchmarking, for application deployment, resource orchestration, and adaptive decision-making. Edge performance benchmarking is a nascent research avenue that has started gaining momentum over the past five years. This article first reviews articles published over the past three decades to trace the history of performance benchmarking from tightly coupled to loosely coupled systems. It then systematically classifies previous research to identify the system under test, techniques analyzed, and benchmark runtime in edge performance benchmarking.

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arXiv2020

A Case For Adaptive Deep Neural Networks in Edge Computing

Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transferred to a cloud data center. Deep Neural Networks (DNNs) are one class of applications that are reported to benefit from collaboratively computing between the edge and the cloud. A DNN is partitioned such that specific layers of the DNN are deployed onto the edge and the cloud to meet performance and privacy objectives. However, there is limited understanding of: (a) whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and the cloud) affect the performance of already deployed DNNs, and (b) whether a new partition configuration is required to maximize performance. A DNN that adapts to changing operational conditions is referred to as an 'adaptive DNN'. This paper investigates whether there is a case for adaptive DNNs in edge computing by considering three questions: (i) Are DNNs sensitive to operational conditions? (ii) How sensitive are DNNs to operational conditions? (iii) Do individual or a combination of operational conditions equally affect DNNs? (iv) Is DNN partitioning sensitive to hardware architectures on the cloud/edge? The exploration is carried out in the context of 8 pre-trained DNN models and the results presented are from analyzing nearly 8 million data points. The results highlight that network conditions affects DNN performance more than CPU or memory related operational conditions. Repartitioning is noted to provide a performance gain in a number of cases, but a specific trend was not noted in relation to its correlation to the underlying hardware architecture. Nonetheless, the need for adaptive DNNs is confirmed.

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arXiv2020

Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, for a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge. This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The benchmarking overheads of Scission allow for responding to operational changes periodically rather than in real-time. Scission is available for public download at https://github.com/qub-blesson/Scission.

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arXiv2020

WattsApp: Power-Aware Container Scheduling

Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensive requiring hardware level changes) for obtaining the power consumed by containers for facilitating power-aware container scheduling, an essential activity for efficient management of distributed systems. This paper presents WattsApp, a tool underpinned by a six step software-based method for power-aware container scheduling to minimize power cap violations on a server. The proposed method relies on a neural network-based power estimation model and a power capped container scheduling technique. Experimental studies are pursued in a lab-based environment on 10 benchmarks deployed on Intel and ARM processors. The results highlight that the power estimation model has negligible overheads for data collection - nearly 90% of all data samples can be estimated with less than a 10% error, and the Mean Absolute Percentage Error (MAPE) is less than 6%. The power-aware scheduling of WattsApp is more effective than Intel's Running Power Average Limit (RAPL) based power capping for both single and multiple containers as it does not degrade the performance of all containers running on the server. The results confirm the feasibility of WattsApp.

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arXiv2020

Cross Architectural Power Modelling

Existing power modelling research focuses on the model rather than the process for developing models. An automated power modelling process that can be deployed on different processors for developing power models with high accuracy is developed. For this, (i) an automated hardware performance counter selection method that selects counters best correlated to power on both ARM and Intel processors, (ii) a noise filter based on clustering that can reduce the mean error in power models, and (iii) a two stage power model that surmounts challenges in using existing power models across multiple architectures are proposed and developed. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual method reported in the literature, (ii) the noise filter reduces the mean error in power models by up to 55%, and (iii) the two stage power model can predict dynamic power with less than 8% error on both ARM and Intel processors, which is an improvement over classic models.

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arXiv2020

DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments

Multi-tenancy in resource-constrained environments is a key challenge in Edge computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in Edge' environments, which is the first light-weight and dynamic vertical scaling mechanism for managing resources allocated to applications for facilitating multi-tenancy in Edge environments. To enable dynamic vertical scaling, one static and three dynamic priority management approaches that are workload-aware, community-aware and system-aware, respectively are proposed. This research advocates that dynamic vertical scaling and priority management approaches reduce Service Level Objective (SLO) violation rates. An online-game and a face detection workload in a Cloud-Edge test-bed are used to validate the research. The merits of DYVERSE is that there is only a sub-second overhead per Edge server when 32 Edge servers are deployed on a single Edge node. When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload. Moreover, for both workloads, the system-aware dynamic vertical scaling method effectively reduces the latency of non-violated requests, when compared to other methods.

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arXiv2020

Modelling Fog Offloading Performance

Fog computing has emerged as a computing paradigm aimed at addressing the issues of latency, bandwidth and privacy when mobile devices are communicating with remote cloud services. The concept is to offload compute services closer to the data. However many challenges exist in the realisation of this approach. During offloading, (part of) the application underpinned by the services may be unavailable, which the user will experience as down time. This paper describes work aimed at building models to allow prediction of such down time based on metrics (operational data) of the underlying and surrounding infrastructure. Such prediction would be invaluable in the context of automated Fog offloading and adaptive decision making in Fog orchestration. Models that cater for four container-based stateless and stateful offload techniques, namely Save and Load, Export and Import, Push and Pull and Live Migration, are built using four (linear and non-linear) regression techniques. Experimental results comprising over 42 million data points from multiple lab-based Fog infrastructure are presented. The results highlight that reasonably accurate predictions (measured by the coefficient of determination for regression models, mean absolute percentage error, and mean absolute error) may be obtained when considering 25 metrics relevant to the infrastructure.

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arXiv2020

Priority-based Fair Scheduling in Edge Computing

Scheduling is important in Edge computing. In contrast to the Cloud, Edge resources are hardware limited and cannot support workload-driven infrastructure scaling. Hence, resource allocation and scheduling for the Edge requires a fresh perspective. Existing Edge scheduling research assumes availability of all needed resources whenever a job request is made. This paper challenges that assumption, since not all job requests from a Cloud server can be scheduled on an Edge node. Thus, guaranteeing fairness among the clients (Cloud servers offloading jobs) while accounting for priorities of the jobs becomes a critical task. This paper presents four scheduling techniques, the first is a naive first come first serve strategy and further proposes three strategies, namely a client fair, priority fair, and hybrid that accounts for the fairness of both clients and job priorities. An evaluation on a target platform under three different scenarios, namely equal, random, and Gaussian job distributions is presented. The experimental studies highlight the low overheads and the distribution of scheduled jobs on the Edge node when compared to the naive strategy. The results confirm the superior performance of the hybrid strategy and showcase the feasibility of fair schedulers for Edge computing.

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