volving technology, has kept drawing a significant attention from both the computing industry and academia for nearly a decade.

The need for high computing resources is on the rise, despite the exponential increase of the computing capacity of workstations, the proliferation of mobile devices, and the omnipresence of data centers with massive server farms that housed tens (if not hundreds) of thousands of powerful servers. This is mainly due to the unprecedented increase in the number of Internet users worldwide and the Internet of Things (IoTs). So far, Cloud Computing has been providing the necessary computing infrastructures for applications, including IoT applications. However, the current cloud infrastructures that are based on dedicated datacenters are expensive to set-up; running the infrastructure needs expertise, a lot of electrical power for cooling the facilities, and redundant supply of everything in a data center to provide the desired resilience. Moreover, the current centralized cloud infrastructures will not suffice for IoT's network intensive applications with very fast response requirements. Alternative cloud computing models that depend on spare resources of volunteer computers are emerging, including volunteer cloud computing, in addition to the conventional data center based clouds. These alternative cloud models have one characteristic in common -- they do not rely on dedicated data centers to provide the cloud services. Volunteer clouds are opportunistic cloud systems that run over donated spare resources of volunteer computers. On the one hand, volunteer clouds claim numerous outstanding advantages: affordability, on-premise, self-provision, greener computing (owing to consolidate use of existent computers), etc. On the other hand, full-fledged implementation of volunteer cloud computing raises unique technical and research challenges: management of highly dynamic and heterogeneous compute resources, Quality of Service (QoS) assurance, meeting Service Level Agreement (SLA), reliability, security/trust, which are all made more difficult due to the high dynamics and heterogeneity of the non-dedicated cloud hosts. This dissertation investigates the resource management aspect of volunteer cloud computing. Due to the intermittent availability and heterogeneity of computing resource involved, resource management is one of the challenging tasks in volunteer cloud computing. The dissertation, specifically, focuses on the Resource Discovery and VM Placement tasks of resource management. The resource base over which volunteer cloud computing depends on is a scavenged, sporadically available, aggregate computing power of individual volunteer computers. Delivering reliable cloud services over these unreliable nodes is a big challenge in volunteer cloud computing. The fault tolerance of the whole system rests on the reliability and availability of the infrastructure base. This dissertation discusses the modelling of a fault tolerant prediction based resource discovery in volunteer cloud computing. It presents a multi-state semi-Markov process based model to predict the future availability and reliability of nodes in volunteer cloud systems. A volunteer node is modelled as a semi-Markov process, whose future state depends only on its current state. This exactly matches with a key observation made in analyzing the traces of personal computers in enterprises that the daily patterns of resource availability are comparable to those in the most recent days. The dissertation illustrates how prediction based resource discovery enables volunteer cloud systems to provide reliable cloud services over the unreliable and non-dedicated volunteer hosts with empirical evidences. VM placement algorithms play crucial role in Cloud Computing in fulfilling its characteristics and achieving its objectives. In general, VM placement is a challenging problem that has been extensively studied in conventional Cloud Computing context. Due to its divergent characteristics, volunteer cloud computing needs a novel and unique way of solving the existing Cloud Computing problems, including VM placement. Intermittent availability of nodes, unreliable infrastructure, and resource constrained nodes are some of the characteristics of volunteer cloud computing that make VM placement problem more complicated. In this dissertation, we model the VM placement problem as a \textit{Bounded 0-1 Multi-Dimensional Knapsack Problem}. As a known NP-hard problem, the dissertation discusses heuristic based algorithms that takes the typical characteristics of volunteer cloud computing into consideration, to solve the VM placement problem formulated as a knapsack problem. Three algorithms are developed to meet the objectives and constraints specific to volunteer cloud computing. The algorithms are tested on a real volunteer cloud computing test-bed and showed a good performance results based on their optimization objectives. The dissertation also presents the design and implementation of a real volunteer cloud computing system, cuCloud, that bases its resource infrastructure on donated computing resource of computers. The need for the development of cuCloud stems from the lack of experimentation platform, real or simulation, that specifically works for volunteer cloud computing. The cuCloud is a system that can be called a genuine volunteer cloud computing system, which manifests the concept of ``Volunteer Computing as a Service'' (VCaaS), with a particular significance in edge computing and related applications. In the course of this dissertation, empirical evaluations show that volunteer clouds can be used to execute range of applications reliably and efficiently. Moreover, the physical proximity of volunteer nodes to where applications originate, edge of the network, helps them in reducing the round trip time latency of applications. However, the overall computing capability of volunteer clouds will not suffice to handle highly resource intensive applications by itself. Based on these observations, the dissertation also proposes the use of volunteer clouds as a resource fabric in the emerging Edge Computing paradigm as a future work.

Non-conventional cloud computing models such as volunteer and mobile clouds have been increasingly popular in cloud computing research. Volunteer cloud computing is a more economical, greener alternative to the current model based on data centers in which tens of thousands of dedicated servers facilitate cloud services. Volunteer clouds offer numerous benefits: no upfront investment to procure the many servers needed for traditional data center hosting; no maintenance costs, such as electricity for cooling and running servers; and physical closeness to edge computing resources, such as individually owned PCs. Despite these benefits, such systems introduce their own technical challenges due to the dynamics and heterogeneity of volunteer computers that are shared not only among cloud users but also between cloud and local users. The key issues in cloud computing such as security, privacy, reliability, and availability thus need to be addressed more critically in volunteer cloud computing.Emerging paradigms are plagued by security issues, such as in volunteer cloud computing, where trust among entities is nonexistent. Thus, this study presents a zero-trust model that does not assign trust to any volunteer node (VN) and always verifies using a server-client topology for all communications, whether internal or external (between VNs and the system). To ensure the model chooses only the most trusted VNs in the system, two sets of monitoring mechanisms are used. The first uses a series of reputation-based trust management mechanisms to filter VNs at various critical points in their life-cycle. This set of mechanisms helps the volunteer cloud management system detect malicious activities, violations, and failures among VNs through innovative monitoring policies that affect the trust scores of less trusted VNs and reward the most trusted VNs during their life-cycle in the system. The second set of mechanisms uses adaptive behavior evaluation contexts in VN identity management. This is done by calculating the challenge score and risk rate of each node to calculate and predict a trust score. Furthermore, the study resulted in a volunteer computing as a service (VCaaS) cloud system using undedicated hosts as resources. Both cuCloud and the open-source CloudSim platform are used to evaluate the proposed model.The results shows that zero-trust identity management for volunteer clouds can execute a range of applications securely, reliably, and efficiently. With the help of the proposed model, volunteer clouds can be a potential enabler for various edge computing applications. Edge computing could use volunteer cloud computing along with the proposed trust system and penalty module (ZTIMM and ZTIMM-P) to manage the identity of all VNs that are part of the volunteer edge computing architecture.