Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This paper proposes network-aware scheduling plugins that integrate heuristic, metaheuristic, and linear programming methods to optimize resource utilization and inter-zone communication latency for containerized workloads, particularly Apache Spark batch-processing tasks. Unlike the default scheduler, the presented approach incorporates inter-node latency constraints and prioritizes locality-aware scheduling, ensuring efficient pod distribution while minimizing network overhead. The proposed plugins are evaluated using the kube-scheduler-simulator, a tool that replicates Kubernetes scheduling behavior without deploying real workloads. Experiments cover multiple cluster configurations, varying in node count, region count, and inter-region latencies, with performance metrics recorded for scheduler efficiency, inter-zone communication impact, and execution time across different optimization algorithms. The obtained results indicate that network-aware scheduling approaches significantly improve latency-aware placement decisions, achieving lower inter-region communication delays while maintaining resource efficiency.

The paper presents a test bed platform for implementation of Cloud Computing in SCADA systems for Electrical Power Systems. The general idea of the experimental study is to complete operation tests of traditional SCADA systems on cloud computing platforms, and integrate the main application modules of SCADA systems for electrical power metering on the cloud platforms. The logical networking structure of the Control Center of SCADA systems is presented. The virtualization of the different type of servers in the Control Center together with physical networking solution of the cloud platform realization is described.

The paper presents an implementation of smart metering application in electrical power systems. It makes uses of cloud computing and M2M technologies. The use of cloud computing technologies increase reliability and protection of collected data and eases the software developers in management of remote devices - the actual devices are managed through web services and provided REST-based interfaces. The smart meter is based on PST power transducer, developed at the Technical University of Sofia, an embedded platform A20-0linuxino and DeviceHive M2M platform running on a private virtual infrastructure.

Driver monitoring systems (DMSs) are increasingly important for road safety, aiming to reduce driver-caused accidents. Traditional DMSs, focusing on behavioral and observable signals, lack the sensitivity to detect changes in the driver’s health status. Monitoring physiological parameters offers the opportunity to objectively assess the driver’s condition in real time and detect early signs of medical emergencies. After a brief overview of the physiological parameters that are critical for assessing the driver’s condition, we examine the different methods and sensors for obtaining the relevant physiological signals with their advantages and limitations. Based on this review, a taxonomy of methods, techniques, and sensors for acquisition of physiological signals in DMSs is proposed. It provides a systematically structured and detailed classification to understand the relationships between physiological parameters and the different methods and sensors for their measurement. This taxonomy can serve as a fundamental framework for researchers and developers to design and implement reliable next-generation DMSs based on physiological signals.