The computation of select eigenvalues and eigenvectors of large, sparse matrices is fundamental to a wide range of applications. Accordingly, evaluating the numerical performance of emerging alternatives to the IEEE 754 floating-point standard, such as OFP8 (E4M3 and E5M2), bfloat16, and the tapered-precision posit and takum formats, is of significant interest. Among the most widely used methods for this task is the implicitly restarted Arnoldi method, as implemented in ARPACK.

Control groups (cgroups) are a crucial isolation mechanism in containerized environments, but they don't fully prevent performance interference (noisy neighbors). This paper presents a novel, workload-agnostic approach for detecting noisy neighbors within CPU-isolated cgroups. Using in-kernel profiling with Extended Berkeley Packet Filter (eBPF), we instrument the Linux process scheduler to capture scheduling latencies and preemption frequencies. We introduce a detection method based on these metrics to identify noisy neighbors online without requiring workload profiles or offline analysis. Evaluations across various workload scenarios demonstrate the effectiveness of our approach in accurately identifying performance degradation caused by noisy neighbors.

Industry and academia have strong incentives to adopt virtualization technologies. Such technologies can reduce the total cost of ownership or facilitate business models like cloud computing. These options have recently grown significantly with the rise of Kubernetes and the OCI runtime specification. Both enabled virtualization technology vendors to easily integrate their solution into existing infrastructures, leading to increased adoption. Making a detailed decision on a technology selection based on objective characteristics is a complex task. This specifically includes the instrumentation of performance characteristics that are an important aspect for a fair comparison. Moreover, a subsequent quantification of the isolation capability based on performance metrics is not readily available.

The capability to isolate system resources is an essential characteristic of virtualisation technologies and is therefore important for research and industry alike. It allows the co-location of experiments and workloads, the partitioning of system resources and enables multi-tenant business models such as cloud computing. Poor isolation among tenants bears the risk of noisy-neighbour and contention effects which negatively impacts all of those use-cases. These effects describe the negative impact of one tenant onto another by utilising shared resources. Both industry and research provide many different concepts and technologies to realise isolation. Yet, the isolation capabilities of all these different approaches are not well understood; nor is there an established way to measure the quality of their isolation capabilities. Such an understanding, however, is of uttermost importance in practice to elaborately decide on a suited implementation. Hence, in this work, we present a novel methodology to measure the isolation capabilities of virtualisation technologies for system resources, that fulfils all requirements to benchmarking including reliability. It relies on an immutable approach, based on Experiment-as-Code. The complete process holistically includes everything from bare metal resource provisioning to the actual experiment enactment.

Abstract Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is currently an open question to what extent they support scaling at run-time. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. This paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for write-heavy and read-heavy workloads of different intensities. The results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at run-time may even decrease the experienced performance.

Cloud data centres with providers on physical level and customers on virtual level both monitor their hard-and software infrastructure to understand load patterns and to detect malfunctions and bottlenecks. The motivation for cloud monitoring on both the virtual and physical level can be summarized to the three occasions alerting, resource allocation, and visualization. Typical cloud monitoring solutions transfer and store all metrics of all systems under observation in central stores like time series databases. Applications then query, aggregate and compute their result out of these monitoring data. In large data centres, the amount of data scales up and leads to a reasonable overhead. In addition monitoring on virtual and physical level duplicates the overhead. We present an approach for monitoring resource statistics on the physical level only, and provide resource utilisation profiles to cloud middleware and customers, instead of storing the raw time series data. The approach first revisits the necessary metrics for hardware independent resource profiles, considering overbooked physical servers as well. A profile consists of a static (e.g. CPU cores) and dynamic (e.g. changing utilisation) part, and is based on statistical computations like histograms and Markov chains.

We present a new hierarchical approximation and scheduling approach for applications and tasks with multiple modes on a single processor. Our model allows for a temporal and spatial distribution of the feasibility problem for a variable set of tasks with non-deterministic and fluctuating costs at runtime. In case of overloads an optimal degradation strategy selects one of several application modes or even temporarily deactivates applications. Hence, transient and permanent bottlenecks can be overcome with an optimal system quality, which is dynamically decided. This paper gives the first comprehensive and complete overview of all aspects of our research, including a novel CBS concept to confine entire applications, an evaluation of our system by using a video-on-demand application, an outline for adding further resource dimension, and aspects of our protoype implementation based on RTSJ.

Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.

SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

Reasoning is one of the essential application areas of the modern Semantic Web. Nowadays, the semantic reasoning algorithms are facing significant challenges when dealing with the emergence of the Internet-scale knowledge bases, comprising extremely large amounts of data. The traditional reasoning approaches have only been approved for small, closed, trustworthy, consistent, coherent and static data domains. As such, they are not well-suited to be applied in data-intensive applications aiming on the Internet scale. We introduce the Large Knowledge Collider as a platform solution that leverages the service-oriented approach to implement a new reasoning technique, capable of dealing with exploding volumes of the rapidly growing data universe, in order to be able to take advantages of the large-scale and on-demand elastic infrastructures such as high performance computing or cloud technology.

The rise in practical Virtual Organisations (VOs) requires secure access to data and interactions between their partners. Ad hoc solutions to meet these requirements are possible, but Web services hold out the potential for generic security solutions whose cost can be spread across several short lived dynamic VOs. This paper identifies trust and security requirements throughout the VO lifecycle and analyse current Web Services specifications to show their suitability to meet these requirements. Although they demonstrate the potential for generic security support, there are uncertainties concerning different level of interoperability and stability of implementation for different specifications, which may slow down their exploitation for security-critical business applications. However, research in Web services developments are well timed to avoid losing first adopter advantage when they become stable. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this article we examine the integration of three emerging trends in information technology (utility computing, grid computing, and Web services) new computing paradigm (grid-based application service provision) that is taking place in the context of the European research project GRASP. In the first of the paper, we explain how the integration of emerging trends can support enterprises in creating competitive advantage. In the second part, we focus on grid-based application service provision (GRASP), which builds a new technology-driven business paradigm on top of such integration. We conclude by outlining a plan for prototyping a GRASP platform in the context of an ongoing European research project.