LEEWAY – Lean Efficient Edge-based Workload Deployment on the Internet

  • LEEWAY – Lean Efficient Edge-based Workload Deployment on the Internet

LEEWAY – Lean Efficient Edge-based WorkloAd DeploYment on the Internet

Principal Supervisor: Dr Blesson Varghese

Second Supervisor: Dr Ivor Spence

+ Project Description

The emerging Internet-of-Things (IoT) envisions connecting billions of people and things including mobile devices and sensors to the internet. To facilitate this nodes located at the edge of the network, referred to as edge nodes, will need to be leveraged for deploying latency sensitive workloads that service requests from mobile devices or sensors. Edge nodes may take the form of dedicated compute resources, such as a cluster of low power devices or traffic routing nodes, such as routers, switches and gateways, which offer their spare resources for computing. Currently, edge nodes are not included in the computing ecosystem and are not employed for general purpose computing.

It is challenging using current technologies to deploy workloads in a heterogeneous, multi-tenant and resource constrained environment that is typical of edge nodes. This is because current technologies that make use of virtualisation techniques for workload deployment, such as virtual machines or containers have a relatively large resource footprint and operation overhead. A radically new and alternate technology will need to be developed that can operate with limited overheads in resource constrained environments.

This project will explore how workloads can be deployed in a lightweight and efficient manner across multiple hardware architectures. The project will develop by setting up a test-bed using cloud data center resources (typical of Intel Xeon servers or the like) and edge-based resources (such as Odroid Boards or Raspberry Pis) initially. A literature analysis on workload deployment techniques will be carried out to identify the gaps in current technologies. Then the primary goal of the project to design, develop and test cutting edge workload deployment techniques suited for edge-based IoT systems will be pursued.

+ How to Apply

Applicants should apply electronically through the Queen’s online application portal at:

+ Contact Details

Supervisor Name: Dr Blesson Varghese

Room 01.003,
18 Malone Road,
Computer Science Building        



+44 (0)28 9097 5431