Abstract

Construction companies, petrochemical enterprises, and airports are examples of large-scale organizational–technical systems (OTSs) and are characterized by a distributed structure, numerous parallel technological and business processes, and substantial energy consumption. The control of such systems is implemented through hierarchical distributed systems that require the regular collection, synchronization, and analysis of large volumes of heterogeneous data. This paper proposes a methodology for performance analysis and energy consumption optimization in OTSs based on the combined use of hierarchical control, business process modeling in BPMN and DRAKON notations, and the use of snapshots—consistent global states of a distributed system captured at specified time instants. The specifics of snapshot generation algorithms are discussed, including copy-on-write, the Chandy–Lamport algorithm, cloud orchestration, and log-based point-in-time recovery. A snapshot acquisition optimization problem is formulated, which minimizes the deviation of the captured state from the actual state under constraints on frequency, synchronization delay, and cost. The feasibility of the approach is illustrated by a numerical example of energy redistribution between the levels of a hierarchical control system using distributed model predictive control (DMPC). The advantages of the method include obtaining an objective “as is” picture, the applicability of control-theoretic methods for distributed systems based on big data processing, the ability to localize faulty subsystems, and its utility in assessing a company’s condition for stakeholders.

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