Provisions for Service Co-Evolution - Phase 2 (PROSECCO-2)

活跃使用的软件必须不断发展，以保持其实用性和质量。添加新功能、删除过时的功能、修复错误、关闭安全漏洞、提高性能——所有这些都需要不时更新软件产品。当然，对于通过计算机网络提供的服务也是如此。支持业务演进是大规模业务环境的必要条件。随着面向服务的计算系统的进一步扩散和重要性，对服务演进支持的需求将会增长。服务并不是孤立运作的。服务业与客户建立积极的服务提供关系。服务是业务流程的一部分，其中服务依赖于其他服务。对于微服务来说尤其如此。这些相互依赖性使得动态服务演进成为特别困难且具有挑战性的问题，因为服务的演进可能需要其他相关服务和客户端的改变。与生物学类比，我们称之为“服务共同进化”。 我们在第二阶段要回答的基本研究问题仍然与第一阶段相同：如何在由多种相互依赖的服务组成的复杂服务环境中支持协调的服务共同进化？ 在第一阶段，我们为多方服务协同演进奠定了基础。我们定义并分类了服务中可能发生的演化变化及其对依赖服务的潜在影响。我们开发了一种基于代理的服务协同演化架构，为每个服务配备了演化代理 (EVA)，该代理与其他 EVA 协作执行服务演化。接下来，我们提出了一种用于在 EVA 之间传达变化的语言和通信协议。此外，我们开发了一个名为 DiCORE 的框架，它可以自动化多智能体共同进化过程。最后但并非最不重要的一点是，我们在智能城市场景领域实现了初始原型。在第二阶段，我们将扩展第一阶段的成果，并将重点转移到技术、流程和软件的开发上，以实现以下研究目标：（i）关于参与和规划所涉及的 EVA 服务协同进化执行的分散式动态决策； (ii) 监控和运行时测试，以在服务协同演化之前、期间和之后验证功能和非功能服务属性； (iii) 多智能体协同进化配置和协调的动态优化； (iv) 智慧城市领域的开源原型实施和演示。这些解决方案扩展了第一阶段的成果，最终我们将为服务协同进化提供全面的软件基础设施。对于主题（iii），我们将与来自河内两所大学的越南合作伙伴合作。

Actively used software must evolve continuously in order to maintain its utility and quality. Adding new features, removing obsolete features, fixing bugs, closing security holes, improving performance – all require updating a software product from time to time. Certainly, this is also true for services provided via a computer network. Support for service evolution is a sine-qua-non for large-scale service environments. The need for service evolution support will grow with the further proliferation and criticality of service-oriented computing systems. Services do not work in isolation. Services have active service providing relationships with clients. Services are part of business processes where services depend on other services. This is particularly true for micro-services. These interdependencies make on-the-fly service evolution a particularly difficult and challenging problem because the evolution of a service may require changes in other dependent services and clients. In analogy to biology, we call this “service co-evolution”. The fundamental research question that we intend to answer in Phase 2 is still the same as in Phase 1: How can we support a coordinated service co-evolution in complex service landscapes composed of a variety of services that depend on each other? In Phase 1, we laid the foundations for multi-party service co-evolution. We defined and classified evolutionary changes that might happen in services and their potential impact on dependent services. We developed an agent-based architecture for service co-evolution that equips every service with an Evolution Agent (EVA) which performs the service evolution in collaboration with other EVAs. Next, we presented a language and communication protocols for communicating changes among EVAs. Additionally, we developed a framework named DiCORE that automates the multi-agent co-evolution process. Last but not least we implemented an initial prototype in the realm of a Smart City scenario. In Phase 2, we will extend the results of Phase 1 and shift our emphasis to the development of techniques, processes and software for the following research objectives: (i) decentralized on-the-fly decision making about the participation and planning of the execution of a service co-evolution for involved EVAs; (ii) monitoring and run-time testing to verify functional and non-functional service properties before, during and after service co-evolution; (iii) on-the-fly optimization of the multi-agent co-evolution configuration and coordination; (iv) an open source prototype implementation and demonstrators in the realm of Smart City. These solutions extend the results from Phase 1 such that in the end we will deliver a comprehensive software infrastructure for service co-evolution.For topic (iii), we will collaborate with Vietnamese partners from two universities in Hanoi.