No / The merger of the Internet of Things (IoT) with cloud computing has given birth to a Web of Things (WoT) which hosts heterogeneous and rapidly varying data. Traditional access control mechanisms such as Role-Based Access schemes are no longer suitable for modelling access control on such a large and dynamic scale as the actors may also change all the time. For such a dynamic mix of applications, data and actors, a more distributed and flexible model is required. Token-Based Access Control is one such scheme which can easily model and comfortably handle interactions with big data in the cloud and enable provisioning of access to fine levels of granularity. However, simple token access models quickly become hard to manage in the face of a rapidly growing repository. This paper proposes a novel token access model based on a cascading permissions policy model which can easily control interactivity with big data without becoming a menace to manage and administer.

Yes / Next-generation cellular networks such as fifth-generation (5G) will experience tremendous growth in traffic. To accommodate such traffic demand, there is a necessity to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in the cloud, i.e., the computing servers or baseband processing units (BBUs) are located in the cloud. With such an arrangement, more energy saving gains can be achieved by reducing the number of BBUs used. This paper proposes a bin packing scheme with three variants such as First-fit (FT), First-fit decreasing (FFD) and Next-fit (NF) for minimizing energy consumption in 5G C-RAN. The number of BBUs are reduced by matching the right amount of baseband computing load with traffic load. In the proposed scheme, BS traffic items that are mapped into processing requirements, are to be packed into computing servers, called bins, such that the number of bins used are minimized and idle servers can then be switched off to save energy. Simulation results demonstrate that the proposed bin packing scheme achieves an enhanced energy performance compared to the existing distributed BS architecture.

Yes / By 2020, next generation (5G) cellular networks are expected to support a 1000 fold traffic increase. To meet such traffic demands, Base Station (BS) densification through small cells are deployed. However, BSs are costly and consume over half of the cellular network energy. Meanwhile, Cloud Radio Access Networks (C-RAN) has been proposed as an energy efficient architecture that leverage cloud computing technology where baseband processing is performed in the cloud. With such an arrangement, more energy gains can be acquired through statistical multiplexing by reducing the number of BBUs used. This paper proposes a green Intelligent Traffic and Resource Elastic Energy (iTREE) scheme for C-RAN. In iTREE, BBUs are reduced by matching the right amount of baseband processing with traffic load. This is a bin packing problem where items (BS aggregate traffic) are to be packed into bins (BBUs) such that the number of bins used are minimized. Idle BBUs can then be switched off to save energy. Simulation results show that iTREE can reduce BBUs by up to 97% during off peak and 66% at peak times with RAN power reductions of up to 27% and 18% respectively compared with conventional deployments.

Large scale enterprise applications are known to observe dynamic workload; provisioning correct capacity for these applications remains an important and challenging problem. Predicting high variability fluctuations in workload or the peak workload is difficult; erroneous predictions often lead to under-utilized systems or in some situations cause temporarily outage of an otherwise well provisioned web-site. Consequently, rather than provisioning server capacity to handle infrequent peak workloads, an alternate approach of dynamically provisioning capacity on-the-fly in response to workload fluctuations has become popular. Cloud platforms are particularly suited for such applications due to their ability to provision capacity when needed and charge for usage on pay-per-use basis. Cloud environments enable elastic provisioning by providing a variety of hardware configurations as well as mechanisms to add or remove server capacity. The first part of this thesis presents Kingfisher, a cost-aware system that provides a generalized provisioning framework for supporting elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimize cost. Majority of these enterprise applications, deployed as web applications, are distributed or replicated with a multi-tier architecture. SLAs for such applications are often expressed as a high percentile of a performance metric, for e.g. 99 percentile of end to end response time is less than 1 sec. In the second part of this thesis I present a model driven technique which provisions a multi-tier application for such an SLA and is targeted for cloud platforms. Enterprises critically depend on these applications and often own large IT infrastructure to support the regular operation of these applications. However, provisioning for a peak load or for high percentile of response time could be prohibitively expensive. Thus there is a need of hybrid cloud model, where the enterprise uses its own private resources for the majority of its computing, but then "bursts" into the cloud when local resources are insufficient. I discuss a new system, namely Seagull, which performs dynamic provisioning over a hybrid cloud model by enabling cloud bursting. Finally, I describe a methodology to model the configuration patterns (i.e deployment topologies) of different control plane services of a cloud management system itself. I present a generic methodology, based on empirical profiling, which provides initial deployment configuration of a control plane service and also a mechanism which iteratively adjusts the configuration to avoid violation of control plane's Service Level Objective (SLO).

The significant advances of technology in the ICT sector the last decades with the most important the improvements on the internet services and virtualization techniques have led to the emergence of several computing paradigms, with the most recent cloud computing. There are several major international cloud service providers which deliver a variety of cloud services and solutions to individuals or companies. As a result, more and more companies are moving to the cloud leading to a growth of cloud services market. Cloud technologies can offer various benefits to organizations but at the same time there are risks and challenges associated with this term. This study examines the benefits of cloud computing on entrepreneurship and startup companies focusing in a specific country, Greece. Greece is inside a long period of economic crisis and access to financing is one on the most problematic factors for doing business. However, the last three years there is a huge increase in the number of startup companies and at the same time an increase in investments in Greek startups. In order to estimate the adoption of cloud computing from Greek startup companies and the potential benefits it may offer to Greek entrepreneurs an online survey was conducted. The analysis of the primary data indicates that Greek entrepreneurs are likely to use cloud computing and they are aware of its potential benefits and risks. Based on the findings of this study, there are serious indications that cloud computing has played a catalytic role in this recent increase of the entrepreneurial activity in Greece, offering multiple benefits to Greek entrepreneurs who are struggling to be more competitive, increase the value of their products and services and decrease costs.

In this thesis we consider a set of mobile users that employ cloud-based computation offloading. In computation offloading, user energy consumption can be decreased by uploading and executing jobs on a remote server, rather than processing the jobs locally. In order to execute jobs in the cloud however, the user uploads must occur over a base station channel which is shared by all of the uploading users. Since the job completion times are subject to hard deadline constraints, this restricts the feasible set of jobs that can be remotely processed, and may constrain the users ability to reduce energy usage. The system is modelled as a competitive game in which each user is interested in minimizing its own energy consumption. The game is subject to the real-time constraints imposed by the job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The thesis shows that for a variety of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel method for determining this equilibrium is introduced. Results are presented which illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method. Data is also presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions. We find that the solutions perform well compared to a lower bound on total energy performance. / Thesis / Master of Applied Science (MASc)

Computer systems can fail for a vast range of reasons, and handling failures is crucial to any critical computer system. Many modern computer systems are migrating to public clouds, which provides more flexible resource consumption and in many cases reduced costs, while the migration can also require system changes due to limitations in the provided cloud environment. This thesis evaluates a few methods of achieving failover when migrating a system to a public cloud, with the main goal of finding a replacement for failover mechanisms that can only be used in self-managed infrastructure. A few different failover methods are evaluated by looking into different aspects of how each method would change an existing system. Two methods using \textit{etcd} and \textit{Apache ZooKeeper} are used for experimental evaluation where failover time is measured in two simulated scenarios where the primary process terminates and a standby process needs to be promoted to the primary status. In one scenario, the primary process is not able to notify other processes in the system before terminating, and in the other scenario, the primary process can release the primary status to another instance before terminating. The etcd and ZooKeeper solutions are shown to behave quite similarly in the testing setup, while the ZooKeeper solution might be able to achieve lower failover time in low-latency environments.

The purpose of this study was to investigate whether a migration of data and applications towards the cloud entailed any detectable consequences. After that, our goal was to find some kind of solution to these possible problems. We did literature study and to gain a deeper understanding of these issues this study was supplemented with four semi-structured interviews. Our results show that a generally positive perception of cloud services emerges, where availability appears as the primary driving force. Different organizations have different needs, which is why it is important to determine these needs before a migration.Our conclusion is that one should undertake thorough and well-structures preparatory work. Subsequently, it is recommended to consciously choose and follow one or several established migration strategies.

This thesis aims to explore the use of a predictive scaling algorithm to scale a microservices-based system according to a predicted system load. A scalable system along with a predictive scaling algorithm is developed and tested by applying a periodic load to the system. The developed scaling algorithm is a combination of a reactive and a predictive algorithm, where the reactive algorithm is used to scale the system when no significant load changes are predicted. The results show that the periodical load is predicted by the algorithm, that the system can be scaled preemptively, and that the algorithm has room for improvement in terms of accuracy. / Detta examensarbete siktar på att utforska möjligheten att använda förutsägande skalningsalgoritmer för att skala ett microservices-baserat system enligt en förutspådd belastning på systemet. Ett skalbart system utvecklas tillsammans med en förutsägande skalningsalgoritm, och testas genom att applicera en periodisk belastning på systemet. Den utvecklade skalningsalgoritmen är en kombination av en reaktiv och förutsägande algoritm, där den reaktiva algoritmen används för att skala systemet då inga signifikanta belastningar förutspås. Resultaten visar att systemets belastning kan förutspås och att systemet kan skalas med hjälp av den förutspådda belastningen, samt att algoritmen har utrymme för förbättringar.

Containers are increasingly used across many different types of computing to isolate and control apps while efficiently sharing computing resources. By using lightweight operating system virtualization, they can provide apps with a virtual computing abstraction while imposing minimal hardware requirements and a small footprint. My thesis is that new container architectures can provide additional functionality, better resource utilization, and stronger security for mobile, drone, and cloud computing. To demonstrate this, we introduce three new container architectures that enable new mobile app migration functionality, a new notion of virtual drones and efficient utilization of drone hardware, and stronger security for cloud computing by protecting containers against untrusted operating systems. First, we introduce Flux to support multi-surface apps, apps that seamlessly run across multiple user devices, through app migration. Flux introduces two key mechanisms to overcome device heterogeneity and residual dependencies associated with app migration to enable app migration. Selective Record/Adaptive Replay to record just those device-agnostic app calls that lead to the generation of app-specific device-dependent state in services and replay them on the target. Checkpoint/Restore in Android (CRIA) to transition an app into a state in which device-specific information the app contains can be safely discarded before checkpointing and restoring the app within a containerized environment on the new device. Second, we introduce AnDrone, a drone-as-a-service solution that makes drones accessible in the cloud. AnDrone provides a drone virtualization architecture to leverage the fact that computational costs are cheap compared to the operational and energy costs of putting a drone in the air. This enables multiple virtual drones to run simultaneously on the same physical drone at very little additional cost. To enable multiple virtual drones to run in an isolated and secure manner, each virtual drone runs its own containerized operating system instance. AnDrone introduces a new device container architecture, providing virtual drones with secure access to a full range of drone hardware devices, including sensors such as cameras and geofenced flight control. Finally, we introduce BlackBox, a new container architecture that provides fine-grain protection of application data confidentiality and integrity without the need to trust the operating system. BlackBox introduces a container security monitor, a small trusted computing base that creates separate and independent physical address spaces for each container, such that there is no direct information flow from container to operating system or other container physical address spaces. Containerized apps do not need to be modified, can still make full use of operating system services via system calls, yet their CPU and memory state are isolated and protected from other containers and the operating system.