Middleware for Combining Heterogeneous IoT Devices
The Internet of Things (IoT) extends connectivity beyond traditional computing devices to different types of smart objects, equipped with various sensors and actuators. Due to device heterogeneity, the complexity of developing applications that require the collection and sharing of data across multiple IoT devices is high, as developers need to be familiar with a diverse set of supported services and APIs. Cuttlefish is a flexible and lightweight middleware that offers a unified API to help with the development of applications that utilize multiple heterogeneous IoT devices. It abstracts away much of the complexity involved with orchestrating different devices at runtime. At the same time, it avoids the caveats of existing approaches through a simple and efficient design, yet one that offers a rich set of capabilities to developers. This project is done in collaboration with Dr. Andreas Pamboris from UCLan Cyprus.
Towards a Unified Platform for Multi-Wearable Apps
Wearable technology has recently become an ubiquitous part of everyday life. Smartwatches, activity trackers, and clothing embedded with sensors are used for monitoring personal fitness data, detecting health disorders, and offering real-time feedback. However, the current landscape of wearable devices suffers from two main issues: (i) each device currently offers only a portion of all the combined capabilities of all the devices, and (ii) most devices do not share data with each other and are tied to certain ecosystems. Hence, there is a strong need for a unified framework that will change the current collection of standalone devices to a fully networked technology. This project is done in collaboration with Dr. Andreas Pamboris and Dr. Panayiotis Andreou from UCLan Cyprus.
Sea Traffic Management Validation Project
The primary goal of this research programme is the innovative optimization of processes and services within and between ports based on enhanced collaboration and regulated information sharing among port actors. Sea Traffic Management will create significant added value for the maritime transport chain, in particular for ship and cargo owners. This will be achieved through real-time and efficient information exchange among various parties such as ships, service providers, and shipping companies. The project is funded by the European Commission, Innovation and Networks Executive Agency (INEA), Connecting Europe Facility (CEF) with a total budget of 42.9M euros.
Scalable Near Real-Time Failure Localization of Data Center Networks
Despite the built-in redundancy in data center networks, performance issues and device or link failures in the network can lead to user-perceived service interruptions. Therefore, determining and localizing user-impacting availability and performance issues in the network in near real time is crucial. Our key idea is to use statistical data mining techniques on large-scale active monitoring data to determine a ranked list of suspect causes, which we refine with passive monitoring signals.
Starfish: A Self-tuning System for Big Data Analytics
The Hadoop MapReduce platform is a popular choice for big data analytics. Unfortunately, Hadoop's performance out of the box leaves much to be desired, causing suboptimal use of resources, time, and money. Starfish is a self-tuning system for big data analytics that builds on Hadoop while adapting to system workloads and user needs to provide good performance automatically; without any need for users to understand and manipulate the many tuning knobs in the Hadoop platform.
Query Optimization Techniques for Partitioned Tables
Table partitioning has evolved into a powerful mechanism but is currently not utilized effectively during query optimization. We have developed new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. The techniques are designed for easy incorporation into bottom-up query optimizers and have been prototyped in PostgreSQL.
Automating the Process of SQL Tuning
zTuned is a new system that automates SQL tuning using an experiment-driven approach. The nontrivial challenge is to plan the best set of experiments to conduct so that a satisfactory (new) plan can be found quickly. A novel feature of zTuned is a SQL-tuning-aware query optimizer, called Xplus, capable of executing plans proactively, collecting monitoring data from the runs, and iterating. Xplus has been prototyped using PostgreSQL.