Saudi Cultural Missions Theses & Dissertations
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Item Restricted Simplifying IoT Application Deployment in Edge Computing Environment(newcastle university, 2019) Daghistan, iIsmail; Ranjan, RajivCurrently, deploying IoT Application in Edge Computing Environment is facing many challenges. For instance, the limited capabilities of Edge device alongside distributed nature of its Environment where all Edge devices needs to be programmed separately. As a result, edge Environment deployment tools needs to be lightweight and able to orchestrate the deployment effectively. Therefore. I decided to deploy Edge Computing Environment by using docker. Docker proved to be the most suitable tool. This is because Docker is lightweight and ability to run on the Edge . Then, I decided to simplify the deployment by using scripting, to allow the deployment to take place in on device rather than programming several devices. Finally, I will use REST api to capture the user’s requirements to generate and run a script based the that requirements. This will the user to deploy the Edge Environment with a single URL request.8 0Item Restricted Building Secure and Trustworthy Stream Analytics Systems Using Trusted Execution Environment(Monash University, 2024) Bagher, Kassem; Yuan, Xingliang; Cui, ShujieThe exponential growth in data generated by interconnected IoT devices has accelerated the adoption of cloud platforms for near-real-time analytics in various applications, such as smart grids and healthcare. However, cloud centralization presents both security and latency challenges. While edge computing and cryptographic solutions like homomorphic encryption offer partial remedies, they either fail to adequately protect code or incur substantial computational overhead. A promising alternative lies in leveraging a Trusted Execution Environment (TEE), such as Intel SGX, which creates an isolated and secure region in the memory, called enclaves, to protect the confidentiality and integrity of both code and data. Nonetheless, SGX has several limitations, including limited memory size and is vulnerable to information leakage through side-channel attacks. This thesis advances secure and efficient data analytics on hybrid cloud-edge platforms through the integration of Trusted Execution Environments (TEEs), specifically Intel SGX, with cryptographic protocols. It comprises three interrelated studies that collectively enhance data integrity, privacy, and operational efficiency in real-time analytics. The first study introduces a framework that minimises data transmission to the cloud by processing initial data clustering at the edge, significantly reducing latency and enhancing data security. The second study develops a secure stream processing framework within SGX that efficiently handles large data streams, priorities tasks, and minimises query latency, thus enhancing both security and operational efficiency. The third study addresses the mitigation of side-channel attacks in time-series data processing, introducing a novel approach that decouples data operations to improve security and system performance. Each component of this research contributes to building robust, scalable, and secure real-time data analytics solutions, ensuring comprehensive data protection and operational efficiency across various sectors.38 0Item Restricted Optimizing Task Allocation for Edge Compute Micro-Clusters(2023-07-24) Alhaizaey, Yousef; Singer, JeremyThere are over 30 billion devices at the network edge. This is largely driven by the unprecedented growth of the Internet-of-Things (IoT) and 5G technologies. These devices are being used in various applications and technologies, including but not limited to smart city systems, innovative agriculture management systems, and intelligent home systems. Deployment issues like networking and privacy problems dictate that computing should occur close to the data source at or near the network edge. Edge and fog computing are recent decentralised computing paradigms proposed to augment cloud services by extending computing and storage capabilities to the network's edge to enable executing computational workloads locally. The benefits can help to solve issues such as reducing the strain on networking backhaul, improving network latency and enhancing application responsiveness. Many edge and fog computing deployment solutions and infrastructures are being employed to deliver cloud resources and services at the edge of the network — for example, cloudless and mobile edge computing. This thesis focuses on edge micro-cluster platforms for edge computing. Edge computing micro-cluster platforms are small, compact, and decentralised groups of interconnected computing resources located close to the edge of a network. These micro-clusters can typically comprise a variety of heterogeneous but resource-constrained computing resources, such as small compute nodes like Single Board Computers (SBCs), storage devices, and networking equipment deployed in local area networks such as smart home management. The goal of edge computing micro-clusters is to bring computation and data storage closer to IoT devices and sensors to improve the performance and reliability of distributed systems. Resource management and workload allocation represent a substantial challenge for such resource-limited and heterogeneous micro-clusters because of diversity in system architecture. Therefore, task allocation and workload management are complex problems in such micro-clusters. This thesis investigates the feasibility of edge micro-cluster platforms for edge computation. Specifically, the thesis examines the performance of micro-clusters to execute IoT applications. Furthermore, the thesis involves the evaluation of various optimisation techniques for task allocation and workload management in edge compute micro-cluster platforms. This thesis involves the application of various optimisation techniques, including simple heuristics-based optimisations, mathematical-based optimisation and metaheuristic optimisation techniques, to optimise task allocation problems in reconfigurable edge computing micro-clusters. The implementation and performance evaluations take place in a configured edge realistic environment using a constructed micro-cluster system comprised of a group of heterogeneous computing nodes and utilising a set of edge-relevant applications benchmark. The research overall characterises and demonstrates a feasible use case for micro-cluster platforms for edge computing environments and provides insight into the performance of various task allocation optimisation techniques for such micro-cluster systems.12 0Item Restricted Self-adaptive System Supporting Elasticity and Quality of Service in Edge Computing(2023-05-30) Aljulayfi, Abdullah Fawaz A; Djemame, Karim; Xu, JieThe Edge Computing (EC) paradigm is seen as a promising paradigm to address the Internet of Things’ (IoT) application requirements, such as low latency to support responsiveness. It is a complementary paradigm of the Cloud Computing (CC) which leverages CC’s resources to the network proximity closer the data source in a distributed fashion. EC is a complex operational environment due to its nature which consists of limited resources and experiences a highly dynamic workload. This complexity is also augmented by the massive growth of the number of end devices, e.g., IoT. Such complexity requires efficient resource and task management to support both the elasticity, which aims to provision and deprovision the resources in order to adapt with the workload dynamicity and cope with the massive growth of the number of IoT devices in such resource restricted environment, and the Quality of service (QoS) in terms of the latency, which aims to manage the tasks by avoiding resource overutilisation to support the fulfilment of the latency requirement as EC has limited resources, experiences a high workload, and emerged to support such requirement. Such management requires a continuous monitoring of the operation environment, including the behaviour of the end users and their applications’ requirements, in order to have a full control over the EC infrastructure. This can be performed using a Self-adaptive System (SAS) which enables the system to monitor the operational environment, hence, adapt to response to the environment changes without human interaction. To this end, this thesis proposes a novel SAS for EC environment. The proposed SAS consists of three frameworks which are the elasticity framework, that aims to provision and deprovision the containerised applications in accordance to the workload dynamicity using Machine Learning (ML) algorithms, the QoS framework, whereby it is responsible for performing efficient task management by avoid resource overutilisation to support the latency requirement, and the offloading framework, which aims to consider the cloud layer by offloading some workload to extend the edge capability as it has limited resources. Moreover, a simulation-based environment is used to implement and evaluate the proposed SAS under different scenarios to demonstrate its effectiveness. The performance evaluation results show that it is essential to study and understand the operational environment, such as workload and applications scenarios, in order to have a robust SAS that can support both elasticity and QoS. For instance, the improvement of the SAS performance in the acceptance rate can reach ~70% in supporting the elasticity once a suitable adaptive approach is selected. Additionally, the internal design of the SAS to support the latency requirement can significantly improve the system objectives fulfilment, which can reach 50%.35 0