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Monitoring of High Performance Computing (HPC) platforms is critical to successful operations, can provide insights into performance-impacting conditions, and can inform methodologies for improving science throughput. However, monitoring systems are not generally considered core capabilities in system requirements specifications nor in vendor development strategies. In this paper we present work performed at a number of large-scale HPC sites towards developing monitoring capabilities that fill current gaps in ease of problem identification and root cause discovery. We also present our collective views, based on the experiences presented, on needs and requirements for enabling development by vendors or users of effective sharable end-to-end monitoring capabilities.

Most of the container transports by sea and land from the Republic of Korea to other countries or continents take more than a week. Under such environment, cargoes in the reefer containers must be kept at the required temperatures. To prevent any spoilage of cargoes during the long period of navigation, these containers need to be inspected daily, but it is often difficult to check and maintain them regularly with limited manpower on a ship as they are scattered around the individual cargo holding area. To deal with such situation, a reefer container monitoring system that utilizes the existing onboard power line(s) and can acquire data from the device sensors is proposed. As the advantage of this system, no additional wiring is necessary, so shipping companies can install the system on their ships at low cost. The system is applicable to the railway system in the future. Expected effects include the reduction in logistics/operation costs and accidents while abiding by the safety guidelines set by the International Maritime Organization. [ABSTRACT FROM AUTHOR]

Concerning energy consumption and monitoring architectures, our goal is to develop a sustainable declarative monitoring architecture for lower energy consumption taking into account the monitoring system itself. Our second is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon like energy consumption of smart building vs inhabitants' social behaviours. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multidimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand relationship between physical phenomena and user behaviors. The aim of this paper is to give a presentation of our methodology and results concerning architecture and user-centric tools. [ABSTRACT FROM AUTHOR]