Managing complexity is an increasing challenge in the development of embedded systems (ES). Some of the factors contributing to the increase in complexity are the growing complexity of hardware and software, and the increased pressure to deliver full-featured products with reduced time-to-market. An attractive approach to manage the software complexity, reduce time-to-market and decrease development costs lies in the adoption of component-based development that has been proven as a successful approach in other domains. Another raising challenge, due to complexity increase, in ES, is predictability, i.e., the ability to anticipate the behavior of a system at run-time. The particular predictability requirements of ES call for a development framework equipped with techniques and tools that can be applied to deal with requirements, such as timing, and resource utilization, already at early-stage of development. Modeling and formal analysis play increasingly important roles in achieving predictability, since they can help us to understand how systems function, validate the design and verify some important properties. In this thesis, we present a resource-aware framework for designing predictable component-based ES. The proposed framework consists of (i) the formally specified ProCom component model that takes into account the characteristics of control-intensive ES, and (ii) the resource-aware timed behavioral language - REMES for modeling and reasoning about components’ and systems’ functional and extra-functional behavior that includes relevant resource types for ES, associated analysis techniques for various resource-wise properties, and a set of associated tools. To demonstrate the potential application of our framework, we present a number of case studies, out of which one is an industrial research prototype, where ProCom and REMES are applied. / PROGRESS

The pervasive availability of streaming data from various sources is driving todays’ enterprises to acquire low-latency big data streaming applications (BDSAs) for extracting useful information. In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud. For most enterprises, gaining insights from big data is immensely important for maintaining competitive advantage. However, majority of enterprises have difficulty managing the multitude of BDSAs and the complex issues cloud technologies present, giving rise to the incorporation of cloud service brokers (CSBs). Generally, the main objective of the CSB is to maintain the heterogeneous quality of service (QoS) of BDSAs while minimizing costs. To achieve this goal, the cloud, although with many desirable features, exhibits major challenges — resource prediction and resource allocation — for CSBs. First, most stream processing systems allocate a fixed amount of resources at runtime, which can lead to under- or over-provisioning as BDSA demands vary over time. Thus, obtaining optimal trade-off between QoS violation and cost requires accurate demand prediction methodology to prevent waste, degradation or shutdown of processing. Second, coordinating resource allocation and pricing decisions for self-interested BDSAs to achieve fairness and efficiency can be complex. This complexity is exacerbated with the recent introduction of containers. This dissertation addresses the cloud resource elasticity management issues for CSBs as follows: First, we provide two contributions to the resource prediction challenge; we propose a novel layered multi-dimensional hidden Markov model (LMD-HMM) framework for managing time-bounded BDSAs and a layered multi-dimensional hidden semi-Markov model (LMD-HSMM) to address unbounded BDSAs. Second, we present a container resource allocation mechanism (CRAM) for optimal workload distribution to meet the real-time demands of competing containerized BDSAs. We formulate the problem as an n-player non-cooperative game among a set of heterogeneous containerized BDSAs. Finally, we incorporate a dynamic incentive-compatible pricing scheme that coordinates the decisions of self-interested BDSAs to maximize the CSB’s surplus. Experimental results demonstrate the effectiveness of our approaches.