Coordinating Large Distributed Process Structures

Steinau, Sebastian; Andrews, Kevin; Reichert, Manfred

doi:10.1007/978-3-030-20618-5_2

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…This paper extends a previous conference publication [40] in several ways. First, it is shown that the decentralization of coordination constraints over multiple coordination processes not only has conceptual benefits for modelers.…”

Section: A Proof-of-concept Prototype and A Validation Whichsupporting

confidence: 73%

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Coordinating large distributed relational process structures

2020

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Representing a business process as a collaboration of interacting processes has become feasible with the emergence of data-centric business process management paradigms. Usually, these interacting processes have relations and, thereby, form a complex relational process structure. The interactions of processes within this relational process structure need to be coordinated to arrive at a meaningful overall business goal. However, relational process structures may become arbitrarily large. With the use of cloud technology, they may additionally be distributed over multiple nodes, allowing for scalability. Coordination processes have been proposed to coordinate relational process structures, where processes may have one-to-many and many-to-many relations at run-time. This paper shows how multiple coordination processes can be used in a decentralized fashion to more efficiently coordinate large, distributed process structures. The main challenge of using multiple coordination processes is to effectively realize the coordination responsibility of each coordination process. Key components of the solution are the subsidiary principle and the hierarchy of the relational process structure. Finally, an implementation of the coordination process concept based on microservices was developed, which allows for fast and concurrent enactment of multiple, decentralized coordination processes in large, distributed process structures.

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Section: A Proof-of-concept Prototype and A Validation Whichsupporting

confidence: 73%

“…Several variants exist on how to realize decentralized process coordination. As these variants are built on top of each other, they are denoted as stages [40]. Stage-0 Decentralized Coordination corresponds to central coordination and Stage-2 Decentralized Coordination to fully decentralized coordination.…”

Section: Problem Statementmentioning

confidence: 99%

Coordinating large distributed relational process structures

2020

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“…Catalog nets [23] approach the problem from the side databases and model entity behavior by describing database updates through transitions; entity synchronization is similar to synchronous proclets. Process structures [24] integrate relational modeling and behavioral modeling but are using dedicated behavioral model without existing analysis techniques. None of these works so far considered system-level entities such as queues and resources as part of the model to study how system-level entities impact process behavior.…”

Section: Related Workmentioning

confidence: 99%

Inferring Unobserved Events in Systems With Shared Resources and Queues

Fahland¹,

Denisov²,

Aalst³

2021

Preprint

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To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. For example, to reduce costs, the number of sensors is minimized or parts of the system are not connected. To understand and analyze the behavior of processes with shared resources, we aim to reconstruct bounds for timestamps of events that must have happened but were not recorded. We present a novel approach that decomposes system runs into entity traces of cases and resources that may need to synchronize in the presence of many-to-many relationships. Such relationships occur, for example, in warehouses where packages for N incoming orders are not handled in a single delivery but in M different deliveries. We use linear programming over entity traces to derive the timestamps of unobserved events in an efficient manner. This helps to complete the event logs and facilitates analysis. We focus on material handling systems like baggage handling systems in airports to illustrate our approach. However, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

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Improved NSGA-II based process dynamic multi-objective algorithm

Zhang

2023

2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT)

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Coordinating Large Distributed Process Structures

Cited by 4 publications

References 12 publications

Coordinating large distributed relational process structures

Coordinating large distributed relational process structures

Inferring Unobserved Events in Systems With Shared Resources and Queues

Improved NSGA-II based process dynamic multi-objective algorithm

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