Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). However, monitoring data has a high dimensionality, is hardware-dependent and difficult to interpret. This, coupled with the strict requirements of ODA, makes most traditional data mining methods impractical and in turn renders this type of data cumbersome to process. Most current ODA solutions use ad-hoc processing methods that are not generic, are sensible to the sensors' features and are not fit for visualization. In this paper we propose a novel method, called Correlation-wise Smoothing (CS), to extract descriptive signatures from time-series monitoring data in a generic and lightweight way. Our CS method exploits correlations between data dimensions to form groups and produces image-like signatures that can be easily manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a collection of datasets that we release with this work, and show that it leads to the same performance as most state-of-the-art methods while producing signatures that are up to ten times smaller and up to ten times faster, while gaining visualizability, portability across systems and clear scaling properties.

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem.

Today's HPC installations are highly-complex systems, and their complexity will only increase as we move to exascale and beyond. At each layer, from facilities to systems, from runtimes to applications, a wide range of tuning decisions must be made in order to achieve efficient operation. This, however, requires systematic and continuous monitoring of system and user data. While many insular solutions exist, a system for holistic and facility-wide monitoring is still lacking in the current HPC ecosystem.

The ever-increasing energy demands of modern High Performance Computing (HPC) platforms is undeniably one of the most critical aspects for the future design and evolution of such systems. The capability of managing their energy consumption not only allows for significant reduction in electricity costs but is also a step forward on the road towards the exascale. Powercapping is a widely studied technique that contributes to address this challenge by instantaneously setting and maintaining a predefined power threshold (power cap) that cannot be exceeded. However, the lack of a centralized mechanism responsible for efficiently allocating the available power among resources and jobs may ultimately yield to fragmentation, low system utilization and increased user waiting times. Additionally, power cap violations can lead to high risk scenarios and/or increase operational costs. This paper proposes to prevent such issues with the introduction of the Enhanced Power Adaptive Scheduling (E-PAS) algorithm. The E-PAS algorithm combines scheduling and resource management mechanisms, correlating estimated and real power consumption data in order to optimize the resource utilization of the platform under a predefined power cap. The algorithm has been implemented in the widely used open-source resource and job management system SLURM and is planned to be pushed in a future mainstream version. Its effectiveness has been evaluated through real-scale experiments respectively on an ARM- and an Intel-based cluster of comparable size. All experiments have been performed using synthetic workloads from a set of mini-applications.

High-performance computing (HPC) is recognized as one of the pillars for further progress in science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging architectural challenges in order to reach Exascale level of performance, projected for the year 2020. The much larger embedded and mobile market allows for rapid development of intellectual property (IP) blocks and provides more flexibility in designing an application-specific system-on-chip (SoC), in turn providing the possibility in balancing performance, energy-efficiency, and cost. In the Mont-Blanc project, we advocate for HPC systems being built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such an approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and network interface cards (NICs) from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling, and integration solutions. We present the system's architecture and evaluate both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability and estimate the maximum scalability point of this approach across a set of applications.

An expression is obtained for the variance of overflow traffic from a fully-accessible channel group, when offered Bernoulli-Poisson-Pascal (BPP) traffic. The formula is exact within the context of Delbrouck's BPP method and provides an alternative to approximations and numerical-based methods for evaluation of the variance. Applications include two-moment blocking network analysis methods, where switch overflow traffic is not lost but offered on alternative routes, for example in hierarchical cellular networks and optically switched networks with alternative routing schemes.

Information and Communication Technologies (ICTs) are contributing to a large amount of the global electricity consumption. Due to tremendous increase in the bandwidth demands and utilisation of non-renewable energy resources Greenhouse Gas Emissions are increasing proportionally with the increasing demand. Despite their advantages in terms of computing performance, distributed applications such as computational grids are major factors that increase the traffic volume in the Internet. In this paper, we propose a distributed framework to ensure energy savings in the optical WDM backbone which transport the traffic between nodes and several computational grids based on anycast routing. According to the proposed framework, the backbone nodes go to sleep mode and resume active mode in a distributed manner with the objective of maximum energy savings in the backbone. Each node maintains two thresholds which are adaptively adjusted based on the network performance, and these thresholds play the key role in determining the decision of a node whether to sleep or resume. Numerical results confirm that the proposed framework can ensure significant energy savings in the network when compared to the conventional energy-unaware operation mode. We further show that the adoption of the proposed network framework does not degrade significantly the network performance in terms of average blocking probability and end-to-end delay.

We propose an approximate modelling method for analysing networks of optical burst switches with shared fibre delay lines (FDLs). To achieve a good accuracy in evaluating burst loss probabilities in the network, and allow a tractable solution method, a two-moment analysis is applied, both to traffic flows within network nodes and on network links. We believe the method is useful for evaluating the performance of share-per-node buffer architectures, which are generally more cost-efficient in terms of switching hardware requirements. Results show good accuracy compared to discrete-event simulations.

We develop an approximate analytic model of an Optical Burst Switch with share-per-node fibre delay lines and tuneable wavelength converters by employing Equivalent Random Theory, an approach from circuit-switching analysis. Our model is formulated in terms of virtual traffic flows within the switch