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

在球场上的观点。需求工程（RE）和系统/软件体系结构（SSA）中的技术对于实现一定的质量水平以及支持有效的管理和有效的演化至关重要。分区对可再生能源和特别敏感区域起着核心作用。在文献中已经提出了各种RE和SSA划分技术。尽管如此，这些划分技术大多基于引出的需求生成静态系统结构。已经提出了一些方法来考虑动态软件体系结构，以响应需求，技术或环境的变化。然而，随着诸如云计算（CC）之类的新兴技术的出现，虚拟化方法（例如，虚拟网络、虚拟机和虚拟存储），以及软件定义的范例（例如，自定义网络、自定义数据中心和自定义环境），但由于这些新兴技术，SSA可能会发生动态变化，因此SSA面临新的挑战。例如，系统的部分可以动态地迁移并运行在不同的云（多个云或公共云与私有云）上，这些云由云服务提供商（CSP）或甚至多个CSP管理，用于资源管理、服务级别协议（SLA）服务质量（QoS）原因，这可能违反一些用户约束或策略，例如，隐私、安全、信任（PST）或成本。（用户可以是企业或组织。现有的RE和SSA模型被认为不适合CC，特别是软件即服务（SaaS）。因此，必须对传统的可再生能源和SSA技术进行调整，以适应不断发展的技术。 研究目的。研究的总体目标是调查新兴的CC范例，并调整现有的RE和SSA相关技术，以应对SaaS的挑战。具体来说，我打算研究与SaaS密切相关的新兴软件定义范例以及适用于SIMT的建模技术。要做到这一点，有必要首先系统地研究传统的RE和SSA CC技术。这些RE和SSA技术包括分区、特征建模、模型驱动工程（MDE）、面向方面设计（AOD）、自主计算、软件性能工程（SPE）、资源管理和分布式计算。我一直在这些RE和SSA领域工作。然而，这些技术必须加以扩大和/或调整，以满足气候变化的新技术和挑战。 这项建议的主要短期目标如下。请注意，这些目标是相互关联的，它们不必严格按顺序执行。 (1)分析了传统的面向SaaS的RE和SAA建模方法，并对这些方法进行了扩展以适应SaaS需求; (2)为SIMT开发一种集成软件定义技术和增强的RE和SAA建模技术的方法; (3)分析SaaS的运行时行为，并开发一种有效的跨层（SaaS、PaaS和IaaS）云管理和约束满足（例如，QoS和PST的约束）。 此外，主要的长期目标包括：（4）研究模型符号并开发模型转换方法，以支持SaaS领域的MDE;以及（5）开发一种机制，便于将用户约束映射到云系统约束。这两个较长期的目标需要坚定的基础研究和正式的方法调查。从长远来看，拟议的研究将有助于知识库和HQP机会，支持高效和有效的SaaS开发，并简化不同CC技术的集成，包括IaaS，PaaS和SaaS。

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.