Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential equations, and set-theoretic notation, have been used as the main tools. The outcome is a model of a game-based optimization process in a two-element logistics system and an algorithm applied to find optimal steering strategies. The algorithm has been initially verified with the use of simulation based on a Bayesian network (BN) and a structured set of possible strategies (OP/TO) calculated with the use of QGeNie Modeller, finally prepared for Python. It has been proved the algorithm at this stage has no deadlocks and unforeseen loops and is ready to be challenged with the original big set of learning data from a drone-operating company (as the next stage of the planned research).

In this paper we present the results of our studies focused on enabling predictable performance for functions executing in scalable, multi-tenant Function-as-a-Service environments. We start by analyzing QoS and performance requirements and use cases from the point of view of End-Users, Developers and Infrastructure Owners. Then we take a closer look at functions’ resource utilization patterns and investigate functions’ sensitivity to those resources. We specifically focus on the CPU microarchitecture resources as they have significant impact on functions’ overall performance. As part of our studies we have conducted experiments to research the effect of co-locating different functions on the compute nodes. We discuss the results and provide an overview of how we have further modified the scheduling logic of our containers orchestrator (Kubernetes), and how that impacted functions’ execution times and performance variation. We have specifically leveraged the low-level telemetry data, mostly exposed by the Intel® Resource Director Technology (Intel® RDT) [1]. Finally, we provide an overview of our future studies, which will be centered around node-level resource allocations, further improving a function’s performance, and conclude with key takeaways.

The presented paper comes as a summary of a theoretical work presenting an original algorithm describing International Multimodal Transport (IMT) mechanisms and processes. Since containers appeared in early 1960s on the regional, and later on, on global scale in logistic operations, the IMT has become the fastest growing combination of an international through-transport. With use of various modes of transport IMT has significant influence on global trade development. Due to its characteristic the IMT process is exposed on the global unitization, standardization, safety, security and economization (optimization) challenges. Thus, there is a need for scientific approach in building a standardized conceptual model of IMT referred in this paper to CAIMT. CAIMT is an original, deterministic algorithm, presented in the block notation, identifying typical procedures as a logical continuation of the programmed sequence of the basic IMT operations. The suggested CAIMT has a potential in further standardization of IMT procedures and can be scientifically developed through the modeling of the IMT processes and safety/security procedures. In addition, in terms of empirical aspects, algorithmization of the main IMT processes can lead to their application in the various expert systems. Summing up, this method (CAIMT) comes as an optimal instrument that can be utilized in order to build further pillars for IMT development, standardization and optimization.