Title: Neoteric Frontiers in Cloud and Edge Computing


Prof. Rajkumar Buyya

Director, Cloud Computing and Distributed Systems (CLOUDS) Lab,

The University of Melbourne, Australia

CEO, Manjrasoft Pvt Ltd, Melbourne, Australia


Computing is being transformed to a model consisting of services that are delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted or how they are delivered. Cloud computing paradigm has turned this vision of "computing utilities" into a reality. It offers infrastructure, platform, and software as services, which are made available to consumers as subscription-oriented services. Cloud application platforms need to offer (1) APIs and tools for rapid creation of elastic applications and (2) a runtime system for deployment of applications on geographically distributed computing infrastructure in a seamless manner.

The Internet of Things (IoT) paradigm enables seamless integration of cyber-and-physical worlds and opening up opportunities for creating new class of applications for domains such as smart cities and smart healthcare. The emerging Fog/Edge computing paradigm is extends Cloud computing model to edge resources for latency sensitive IoT applications with a seamless integration of network-wide resources all the way from edge to the Cloud.

This keynote presentation will cover (a) 21st century vision of computing and identifies various IT paradigms promising to deliver the vision of computing utilities; (b) innovative architecture for creating elastic Clouds integrating edge resources and managed Clouds, (c) Aneka 5G, a Cloud Application Platform, for rapid development of Cloud/Big Data applications and their deployment on private/public Clouds with resource provisioning driven by SLAs, (d) a novel FogBus software framework with Blockchain-based data-integrity management for facilitating end-to-end IoT-Fog/Edge-Cloud integration for execution of sensitive IoT applications, (e) experimental results on deploying Cloud and Big Data/ IoT applications in engineering, and health care (e.g., COVID-19), deep learning/Artificial intelligence (AI), satellite image processing, natural language processing (mining COVID-19 research literature for new insights) and smart cities on elastic Clouds; and (f) directions for delivering our 21st century vision along with pathways for future research in Cloud and Edge/Fog computing.


Title: Time Series Anomaly Detection Toolkit for AI


Dr. Dhaval Patel

IBM TJ Watson Research, US


The talk is organized in a sequence of three sections: Introduction, Theory and Hands-on-demo. In part one, we will briefly discuss foundations of time series dataset with the help of real-world examples. We will also present a broad taxonomy of time series dataset. We will also present general definition of anomalies in time series data and discuss three common variants of Anomaly/Outlier Detection problems. Next, we discuss basic machine learning primitives such as Estimator, Transformer, Data Stationarizer, etc that are useful for building anomaly pipeline. In machine learning field, these components become a backbone for building a complex model learning pipelines. We will formally introduce the key API such as ``fit'', ``predict'', ``decision_function'', to the participant with the help of 30+ different anomaly detection algorithms. We provide the categorization of these algorithms, we will also discuss one algorithm namely Gaussian Graphical Model for interpretable anomaly detection. The access to the toolkit is made available via IBM API Hub Platform ( The example notebooks are accessible at IBM's public github (