Saudi Cultural Missions Theses & Dissertations
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Item Restricted Adaptive Resilience of Intelligent Distributed Applications in the Edge-Cloud Environment(Cardiff University, 2024-04) Almurshed, Osama; Rana, OmerThis thesis navigates the complexities of Internet of Things (IoT) application placement in hybrid fog-cloud environments to improve Quality of Service (QoS) in IoT applications. It investigates the optimal distribution of a Service Function Chain (SFC), the building blocks of an IoT application, across the fog-cloud infrastructure, taking into account the intricate nature of IoT and fog-cloud environments. The primary objectives are to define a platform architecture capable of operating IoT applications efficiently and to model the placement problem comprehensively. These objectives involve detailing the infrastructure's current state, execution requirements, and deployment goals to enable adaptive system management. The research proposes optimal placement methods for IoT applications, aiming to reduce execution time, enhance dependability, and minimise operation costs. It introduces an approach to effectively manage trade-offs through the measurement and analysis of QoS metrics and requires the implementation of specialised scheduling and placement strategies. These strategies employ concurrency to accelerate the planning process and reduce latency, underscoring the need for an algorithm that best corresponds to the specific requirements of the IoT application domain. The study's methodology begins with a comprehensive literature review in the area of IoT application deployment in hybrid fog-cloud environments. The insights gained inform the development of novel solutions that address the identified limitations, ensuring the proposal of robust and efficient solutions.21 0Item Restricted Application Placement Approaches to Improve Quality of Service in Fog Computing(University of Manchester, 0024-06-25) Aljohani, Aisha; Sakellariou, RizosFog Computing (FC) addresses Cloud Computing's (CC) limitations by utilizing distributed computational devices, known as fog devices, near the Internet of Things (IoT) environment to support a wide range of IoT applications. In FC, to ensure Quality of Service (QoS), users need to specify a placement plan for distributing IoT applications among fog devices for processing; this is known as the application placement problem (APP). With a potentially huge number of fog devices and applications, solving the APP can be decentralized, i.e., independent optimization can be performed in parallel for different clusters of fog devices, thus mitigating the networking and computing overhead and enhancing the QoS consequently. In this approach, clusters lacking sufficient fog devices may propagate undeployed applications to other clusters, potentially leading to uncertain fulfillment of QoS constraints, i.e., delay bounds on response time. Moreover, deploying applications based on available resources at the placement decision time might result in an increased number of propagated applications among clusters. Additionally, the heterogeneity in fog devices' capabilities and the variations in IoT application characteristics, such as computing and networking intensity, and delay sensitivity, pose challenges in choosing competent applications for powerful fog devices. Assigning specific applications to these powerful devices may result in delay violations for other applications on less powerful ones, potentially leading to propagating the latter ones to other clusters. A raise in the number of propagated applications, especially those with data streams, might lead to increased networking congestion, resulting in extended response time and potential violation in delays, particularly for delay-sensitive applications. This thesis proposes three approaches aiming to improve the QoS of IoT applications, i.e., delays. First, an improved application placement approach through parallel collaboration (ParColl) is proposed to increase the probability of placing propagated applications within their delays, incorporating algorithms to enable parallel searching and manage the searching process. Second, an improved application placement approach through postponement (PostP) is proposed to maximize the number of non-propagated applications meeting their delays, employing algorithms to postpone placement of undeployed applications, instead of propagating them, if such postponement ensures their delays. Third, an application placement approach maximizing response times for applications while meeting delays through cluster-wide resource selection CWRS) is proposed. CWRS ensures that powerful fog devices are reserved for applications needing them to meet their delays, minimizing violations on other devices whenever possible. Experimental results of implementing the proposed approaches in iFogSim show an improvement in the percentage of applications processed within their QoS constraints and a reduction in average delay violation times compared to existing approaches.32 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