On the dynamic resource availability in grids

Currently deployed grids gather together thousands of computational and storage resources for the benefit of a large community of scientists. However, the large scale, the wide geographical spread, and at times the decision of the rightful resource owners to commit the capacity elsewhere, raises serious resource availability issues. Little is known about the characteristics of the grid resource availability, and of the impact of resource unavailability on the performance of grids. In this work, we make first steps in addressing this twofold lack of information. First, we analyze a long-term availability trace and assess the resource availability characteristics of Grid'5000, an experimental grid environment of over 2,500 processors. The average utilization for the studied trace is increased by almost 5%, when availability is considered. Based on the results of the analysis, we further propose a model for grid resource availability. Our analysis and modeling results show that grid computational resources become unavailable at a high rate, negatively affecting the ability of grids to execute long jobs. Second, through trace-based simulation, we show evidence that resource availability can have a severe impact on the performance of the grid systems. The results of this step show evidence that the performance of a grid system can rise when availability is taken into consideration, and that human administration of availability change information results in 10-15 times more job failures than for an automated monitoring solution, even for a lowly utilized system

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Smart Grid challenges - Device Trustworthiness

The Power Grid development brings about technological design changes, resulting in increased connectivity and dependency on IoT devices. The changes offer opportunities to manipulate the IoT hardware as the root of trust. Although terrifying, hardware attacks are considered resource-demanding and rare. Nonetheless, Power Grids are attractive targets for resourceful attackers. As such, the Ukraine attacks boosted Power Grid cybersecurity focus. However, physical assurance and hardware device trustworthiness received less attention. Overhead Line Sensors are utilized in Dynamic Line Rating doctrines for Power Grids. They are potentially essential in the future to optimize conductor ampacity. Conductor optimization is crucial for Power Grids because future throughput volatility demands a high level of grid flexibility. However, there may be challenges to the integrity and availability of the data collected using Overhead Line sensors. We believe that in securing the future Smart Grid, stakeholders need to raise attention to device trustworthiness entailing the hardware layer. That said, integrated into cloud-enhanced digital ecosystems, Overhead Line Sensors can also be manipulated through the network, software, and supply chain to impact their trustworthiness

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor