Blesson Varghese

Blesson Varghese
University of St Andrews · School of Computer Science

PhD

About

164
Publications
65,227
Reads
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2,776
Citations
Citations since 2016
100 Research Items
2639 Citations
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20162017201820192020202120220100200300400500600
20162017201820192020202120220100200300400500600
20162017201820192020202120220100200300400500600
Additional affiliations
July 2013 - December 2015
University of St Andrews
Position
  • Research Associate
January 2012 - June 2013
Dalhousie University
Position
  • Lead Postdoctoral Fellow
Education
January 2009 - December 2011
University of Reading
Field of study
  • Computer Science
October 2007 - September 2008
University of Reading
Field of study
  • Networked Centered Computing and High-Performance Computing
June 2002 - May 2006
University of Kerala
Field of study
  • Information Technology

Publications

Publications (164)
Preprint
Full-text available
Collaborative machine learning (CML) techniques, such as federated learning, were proposed to collaboratively train deep learning models using multiple end-user devices and a server. CML techniques preserve the privacy of end-users as it does not require user data to be transferred to the server. Instead, local models are trained and shared with th...
Preprint
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label informa...
Preprint
Full-text available
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exchange incurs a communication overhead and impacts the performance of FL training. There is limited understanding of how communication protocols specifically contribute to...
Article
Full-text available
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 m...
Article
This paper proposes IoT-based an enterprise health information system called IoTPulse to predict alcohol addiction providing real-time data using machine-learning in fog computing environment. We used data from 300 alcohol addicts from Punjab (India) as a case study to train machine-learning models. The performance of IoTPulse is compared against e...
Article
Full-text available
Applying federated learning (FL) on Internet of Things (IoT) 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: 1) execution on devices with limited computational capabilities; 2) accounting for stragglers due to...
Conference Paper
Full-text available
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...
Preprint
Full-text available
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...
Article
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,...
Article
Full-text available
Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can improve the overall latency of the application since it can process data closer to user devices. Diverse Fog no...
Preprint
Full-text available
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,...
Preprint
Full-text available
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...
Preprint
Full-text available
Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models without having direct access to the original data generated on devices. Since devices may be resource constrained, offloading can be used to improve FL performance by transferring computational workload from devices to edge servers. However, du...
Article
Although serverless computing generally involves executing short-lived “functions,” the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charg...
Preprint
Full-text available
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) execute on devices with limited computational capabilities, (ii) account for stragglers due to computat...
Article
Full-text available
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.
Preprint
Full-text available
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...
Preprint
Full-text available
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.
Preprint
Full-text available
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 a...
Preprint
Full-text available
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...
Article
Full-text available
5G and subsequent cellular network generations aim to extend ubiquitous connectivity of billions of Internet-of-Things (IoT) for their consumers. Security is a prime concern in this context as adversaries have evolved to become smart and often employ new attack strategies. Network defenses can be enhanced against attacks by employing behavior model...
Preprint
Full-text available
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 la...
Article
Blesson Varghese, Associate Professor in computer science at Queen's University Belfast and Flavio Bonomi, Founder of Nebbiolo Technologies and Board Technology Adviser for LYNX, explore how edge computing could foster a more ethical and fairer internet.
Article
Full-text available
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 t...
Article
Full-text available
Two fully funded PhD studentships available in Edge/Fog computing for next generation distributed systems at Queen's University Belfast, UK. More info available at: http://www.findaphd.com?pj=123209 http://www.findaphd.com?pj=123211 Closing date 11 September 2020
Article
Full-text available
Preprint
Full-text available
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 maximiz...
Preprint
Full-text available
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 t...
Preprint
Full-text available
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...
Article
Software-defined networking (SDN) has evolved as an approach that allows network administrators to program and initialise, control, change and manage networking components (mostly at L2-L3 layers) of the OSI model. SDN is designed to address the programmability shortcomings of traditional networking architectures commonly used in current datacenter...
Preprint
Full-text available
Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores for delivering more responsive services. Thus, it is envisioned that a large-scale, geographically dispersed and resource-rich distributed system will emerge and become the backbone of the future Inter-net. However, given...
Preprint
Full-text available
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 corre...
Preprint
Full-text available
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 applic...
Article
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 en...
Conference Paper
Full-text available
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...
Preprint
Full-text available
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...
Preprint
Full-text available
Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can improve the overall Quality-of-Service (QoS) of the application since it can process data closer to user device...
Conference Paper
Fog computing offloads latency critical services of a Cloud application onto resources located at the edge of the network that are in close proximity to end-user devices. The research in this paper is motivated towards characterising and estimating the time taken to offload a service using containers, which is investigated in the context of the 'Sa...
Conference Paper
Full-text available
The accelerated growth of data has made efficient query processing and data analytics more important than ever. While the Cloud has provided an excellent underpinning solution to store, manage and process data, it is becoming increasingly difficult, as the Cloud necessitates sending all data that is generated by billions of user devices and sensors...
Article
Full-text available
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and...
Preprint
Full-text available
Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The research in this paper is motivated towards characterising and estimating the time taken to offload a service using containers, which is investigated in the context of the 'S...
Preprint
Full-text available
Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The research in this paper is motivated towards characterising and estimating the time taken to offload a service using containers, which is investigated in the context of the `S...
Conference Paper
Full-text available
Fog computing envisions that deploying services of an application across resources in the cloud and those located at the edge of the network may improve the overall performance of the application when compared to running the application on the cloud. However , there are currently no benchmarks that can directly compare the performance of the applic...
Preprint
Full-text available
Fog computing envisions that deploying services of an application across resources in the cloud and those located at the edge of the network may improve the overall performance of the application when compared to running the application on the cloud. However, there are currently no benchmarks that can directly compare the performance of the applica...
Article
Blesson Varghese, Assistant Professor at Queen’s University Belfast, traces the footsteps of cloud technology, starting from its foundations five decades ago, through its development, to examine just where the cloud is heading in the future
Article
Full-text available
The Cloud has become integral to most Internet-based applications and user gadgets. This article provides a brief history of the Cloud and presents a researcher's view of the prospects for innovating at the infrastructure, middleware, and application and delivery levels of the already crowded Cloud computing stack.
Chapter
Full-text available
This chapter discusses two key challenges: networking and management in federating edge deployments. Additionally, it considers resource and modeling challenges that will need to be addressed for a federated edge. The chapter discusses potential avenues for resolving the networking challenges. Throughout the chapter, the general term edge refers to...
Article
Full-text available
The edge of the network has the potential to host services for supporting a variety of user applications, ranging in complexity from data preprocessing, image and video rendering, and interactive gaming, to embedded systems in autonomous cars and built environments. However, the computational and data resources over which such services are hosted,...
Article
Full-text available
Hardware accelerators are available on the Cloud for enhanced analytics. Next generation Clouds aim to bring enhanced analytics using accelerators closer to user devices at the edge of the network for improving Quality-of-Service by minimizing end-to-end latencies and response times. The collective computing model that utilizes resources at the Clo...
Article
Full-text available
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and...
Preprint
Full-text available
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 e...
Article
Full-text available
The Cloud computing paradigm has revolutionized the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytim...