Spatio-temporal Composition of Sensor Cloud Services
Project Members: Ms. Azadeh Ghari Neiat and Professor Athman Bouguettaya
In this project, we propose a new framework for composing Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate spatio-temporal Sensor-Cloud services, new quality attributes are introduced. We present a heuristic algorithm based on A* to compose Sensor-Cloud services in terms of spatio-temporal aspects. In addition, a new spatio-temporal technique based on 3D R-tree to access Sensor-Cloud services is proposed.
Cloud Service Selection and Composition from IaaS Provders’ Perspective
Project Members: Mr. Sajib Kumar Mistry, Professor Athman Bouguettaya and Dr. Hai Dong
In this project, we propose a novel composition framework for an Infrastructure-as-a-Service (IaaS) provider that selects the optimal set of long-term service requests to maximize its
profit. Existing solutions consider an IaaS provider’s economic benefits at the time of service composition and ignore the dynamic nature of the consumer requests in a long-term
period. The proposed framework deploys a new multivariate HMM and ARIMA model to predict different patterns of resource utilization and Quality of Service fluctuation tolerance
levels of existing service consumers. The dynamic nature of new consumer requests with no history is modelled using a new community based heuristic approach. The predicted
long-term service requests are optimized using Integer Linear Programming to find a proper configuration that maximizes the profit of an IaaS provider.
Plain-text-described Service Discovery and Selection
Project Members: Dr. Hai Dong and Professor Athman Bouguettaya
In this project, we propose an approach for discovering and selecting plain-text-described services. Plain-text-described services advertisements account for a vast majority of available service advertisements over the internet. For example, up to 98% of Cloud Services are described in plain text. Since plain-text-described services are described in an unstructured and nonstandard way, these services are difficult to be retrieved, discovered, and comprehended by service consumers. Current research however rarely focuses on this problem. This research explores the theories across multiple research areas such as Service Computing, Web Mining, and Web Search.
Service Mining for Internet of Things
Project Members: Ms. Bing Huang, Professor Athman Bouguettaya, Dr. Hai Dong and Dr. Liang Chen
In this project, a service mining framework is proposed that enables discovering interesting relationships in Internet of Things services in a bottom-up manner.The service relationships are modeled based on spatial-temporal aspects,environment, people, and operation. An ontology-based service model is proposed to describe services. We present a set of metrics to evaluate the interestingness of discovered service relationships.