Integration of system and software architecture techniques in support of autonomic cloud management

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Perspective on the field. Techniques in requirements engineering (RE) and system/software architecture (SSA) are crucial to achieve a certain level of quality and to support effective management and efficient evolution. Partitioning plays a central role for RE and SSA. Various RE and SSA partitioning techniques have been proposed in the literature. Nonetheless, those partitioning techniques mostly generate a static system structure based on elicited requirements. A few approaches have been proposed to consider dynamic software architecture in response to changes in requirements, techniques, or environments. However, as the advent of emerging technologies, such as Cloud Computing (CC), virtualization approaches (e.g., virtual networks, virtual machines, and virtual storage), and software-defined paradigms (e.g., self-defined networking, self-defined data centres, and self-defined environments), there are new challenges in SSA, as SSA may be dynamically changed as a result of those emerging technologies. For instance, parts of a system may be dynamically migrating and running on different clouds (multiple clouds or public vs. private clouds) that are managed by the cloud service provider (CSP) or even multiple CSPs for resource management, service-level agreements (SLAs) quality-of-service (QoS) reason, which may violate some user constraints or policies, e.g., privacy, security, trust (PST), or cost. (Users could be enterprises or organizations.) Existing RE and SSA models are identified inadequate for CC, especially Software-as-Service (SaaS). Therefore, traditional RE and SSA techniques have to be adapted to accommodate the evolving technologies. Research Objectives. The overall goal of the research is to investigate emerging CC paradigms, and adapt existing RE and SSA related technologies to address the challenges in SaaS. Specifically, I intend to investigate emerging software-defined paradigms that are closely related to SaaS and the modeling techniques that are suitable for SIMT. To do that, there is a need to first systematically investigate traditional RE and SSA techniques for CC. Those RE and SSA techniques include partitioning, feature modeling, model-driven engineering (MDE), aspect-oriented design (AOD), autonomic computing, software performance engineering (SPE), resource management, and distributed computing. I have been working on those RE and SSA areas. Those technologies, however, have to be expanded and/or adapted to meet emerging technologies and challenges of CC. The main short-term objectives of this proposal are listed as follows. Note that those objectives are inter-related and they do not have to be carried out in a strict sequential manner. (1) Characterize the traditional RE and SAA modeling methods for SaaS and expand those methods to accommodate SaaS demands; (2) Develop an approach that integrates software-defined technologies and the enhanced RE and SAA modeling technologies for SIMT; and (3) Profile run-time behaviors for SaaS and develop an approach for efficient cross-layer (SaaS, PaaS, and IaaS) cloud management and constraint satisfaction (e.g., constraints for QoS and PST). In addition, the primary longer term objectives include: (4) Investigate model notations and develop model transformation methods to support MDE in the area of SaaS; and (5) Develop a mechanism that facilitates mapping of user constraints to cloud system constraints. Both longer term objectives require firm fundamental research and investigation in formal methods. In the longer term, the proposed research will contribute to the knowledge base and HQP opportunities that support efficient and effective SaaS development and ease integration of different CC technologies, including IaaS, PaaS, and SaaS.