Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

Secure Cloud-Edge Deployments, with Trust

Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a non-trivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

Simplifying Internet of Things (IoT) Data Processing Work ow Composition and Orchestration in Edge and Cloud Datacenters

Ph. D. Thesis.Internet of Things (IoT) allows the creation of virtually in nite connections into a global array of distributed intelligence. Identifying a suitable con guration of devices, software and infrastructures in the context of user requirements are fundamental to the success of delivering IoT applications. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users' subjective and objective opinions with IoT work ow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised to lower latency in data transformation and increase reusability. To address the composition and orchestration of IoT applications in the cloud and edge environments, this thesis presents IoT-CANE (Context Aware Recommendation System) as a high-level uni ed IoT resource con guration recommendation system which embodies a uni ed conceptual model capturing con guration, constraint and infrastructure features of Edge/Cloud together with IoT devices. Second, I present an IoT work ow composition system (IoTWC) to allow IoT users to pipeline their work ows with proposed IoT work ow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT work ow that satis es the requirements of any IoT application. Besides, the users are be tted with recommended IoT work ow con gurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT work ow activity abstract patterns and a real-world scenario for smart buildings. Last, I propose a fault-tolerant automation deployment IoT framework which captures the IoT work ow plan from IoTWC to deploy in multi-cloud edge environment with a fault-tolerance mechanism. The e ciency and e ectiveness of the proposed fault-tolerant system are evaluated in a real-time water ooding data monitoring and management applicatio

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique