Retrofitting Frameworks for WfMSes

ify the expected outcome of the workflow executions. By analysis of goal-related data from additional external and internal sensors, mismatches, (cyber-physical) in- consistencies and unexpected situations can be determined (Requirement R5 ) and compensation strategies can be derived by planning components to remedy these issues (Requirements R6 and R7 ). Several approaches rely on adapting the pro- cess by finding alternative resources or services to handle errors with respect to the process resources. The selection or synthesis of new workflow fragments is another approach to adapt the respective process instances in case of undesired behaviour. While several approaches address self-healing and self-adaptation for software sys- tems, workflows and IoT applications in case of device errors and inconsistencies, the aspects of human interaction and also distributed process execution are rarely discussed. The broad spectrum of approaches targeting the realization of self-* capa- bilities for software and workflow systems–ranging from the MAPE-K loop, object- oriented models, semantics-based adaptations to complex planning algorithms using artificial intelligence–shows the complexity of this particular aspect and research field with approaches targeting more general concepts but also concrete domain and application specific solutions.

3.7. Retrofitting Frameworks for WfMSes

In Section 3.2, we evaluated several existing WfMSes used in industry and academia with respect to the requirements. We found that there is a large number of WfM- Ses with no or very limited support of CPS-related features. As it is infeasible to completely redesign and modify these BPMSes or to replace them by other systems in order to use them in the context of CPS and IoT, we investigate at this point if there are solutions and approaches to retrofit these existing WfMSes and software systems with respect to the required CPS-related capabilities (Requirement R8 ).

In [Mon13] Monnier talks about smart grids enabled by the IoT. He briefly dis- cusses the aspect of retrofitting existing meter infrastructures in houses with ad- ditional sensors and appliances to realise smart meters and smart grids. Also in the context of smart homes, Alur et al. mention the challenge of retrofitting exist- ing houses with IoT devices [ABD+16]. When discussing the vision of smart cities, Harmon et al. [HCLB15] briefly mention the aspect of retrofitting the existing in- frastructure with additional smart sensors and actuators [HCLB15]. In [AMT+12] Aswani et al. present a concrete example for retrofitting an air conditioner with a cyber-physical control system to improve energy efficiency. Camps et al. discuss the issues of adding autonomic agents based on control loops to increase the fault toler- ance of health monitoring systems in [SH05]. Moctezuma et al. show in [MJPL12] how to retrofit a factory automation system to address new market needs and soci- etal changes. They add new hardware components for monitoring and new software components based on web services to the existing production infrastructure in order to increase the self-awareness of the overall system and with that, to increase energy efficiency, reconfigurability, safety and other quality-related parameters.

A more general approach of retrofitting a legacy SCADA (Supervisory Control and Data Acquisition [DS99]) system with an external event-based coordination layer to add fault tolerance and protection against cyber attacks in cyber-physical environments is presented by Xiao et al. in [XRK08]. Kaiser et al. describe an

external infrastructure for monitoring distributed legacy systems and adding auto- nomic capabilities to these systems in [KPGV03]. Their approach relies on events to communicate asynchronously with probes and effectors of the legacy system. In a follow-up work, Parekh et al. developed a general methodology for retrofitting au- tonomic capabilities onto legacy systems [PKGV06]. Figure 3.11 gives an overview of their proposed retrofitting reference architecture. Sensors gather information (Probes) from the legacy systems that are collected and then interpreted and anal- ysed by Gauges. Decision and coordination of possible adaptation strategies based on the results of the interpretation are conducted by the controllers, which instruct the effectors of the legacy system in case reconfigurations are required.

Figure 3.11.: Reference Architecture of the Retrofitted Autonomic Software Infras- tructure from [KPGV03].

Dabholkar and Gokhale present in [DG09] an approach of specializing general pur- pose middleware systems with respect to specific requirements that CPS demand of a software system (e. g., real-time behaviour and dealing with constraint resources). They use feature-oriented software development to abstract required features and generative programming to realize the specialized middleware. A general architec- ture for the implementation of CPS that can also serve as a framework for retrofitting legacy systems is discussed by Lee et al. in [LBK15] and described in Section 2.4.3. In [BtHS01] Barros et al. show how to retrofit internal assembly processes with more high-level business processes based on components and services to facilitate synchronization across encapsulated workflows and external interactions. Lee et al. present a way to add adaptivity to an existing scientific workflow engine by implementing new components to realize the MAPE-K feedback loop [LPS+09]. Sensors provide data about the status of jobs running on the computation grid. Once resources are available, the workflow engine is instructed to execute new workflows.