Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Data Partitioning and Load Balancing in Parallel Disk Systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent, self-reliant file system that aims to optimize striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces. Keywords: parallel disk systems, performance tuning, file striping, data allocation, load balancing, disk cooling. 1 Introduction: Tuning Issues in Parallel Disk Systems Parallel disk systems are of great imp..

Closed-loop recycling of plastics enabled by dynamic covalent diketoenamine bonds.

Recycled plastics are low-value commodities due to residual impurities and the degradation of polymer properties with each cycle of re-use. Plastics that undergo reversible polymerization allow high-value monomers to be recovered and re-manufactured into pristine materials, which should incentivize recycling in closed-loop life cycles. However, monomer recovery is often costly, incompatible with complex mixtures and energy-intensive. Here, we show that next-generation plastics-polymerized using dynamic covalent diketoenamine bonds-allow the recovery of monomers from common additives, even in mixed waste streams. Poly(diketoenamine)s 'click' together from a wide variety of triketones and aromatic or aliphatic amines, yielding only water as a by-product. Recovered monomers can be re-manufactured into the same polymer formulation, without loss of performance, as well as other polymer formulations with differentiated properties. The ease with which poly(diketoenamine)s can be manufactured, used, recycled and re-used-without losing value-points to new directions in designing sustainable polymers with minimal environmental impact

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1

Visualization of Barrier Tree Sequences Revisited

The increasing complexity of models for prediction of the native spatial structure of RNA molecules requires visualization methods that help to analyze and understand the models and their predictions. This paper improves the visualization method for sequences of barrier trees previously published by the authors. The barrier trees of these sequences are rough topological simplifications of changing folding landscapes – energy landscapes in which kinetic folding takes place. The folding landscapes themselves are generated for RNA molecules where the number of nucleotides increases. Successive landscapes are thus correlated and so are the corresponding barrier trees. The landscape sequence is visualized by an animation of a barrier tree that changes with time. The animation is created by an adaption of the foresight layout with tolerance algorithm for dynamic graph layout problems. Since it is very general, the main ideas for the adaption are presented: construction and layout of a supergraph, and how to build the final animation from its layout. Our previous suggestions for heuristics lead to visually unpleasing results for some datasets and, generally, suffered from a poor usage of available screen space. We will present some new heuristics that improve the readability of the final animation

Barium isotopes in mid-ocean ridge hydrothermal vent fluids : a source of isotopically heavy Ba to the ocean

Funding: These field and related experimental studies were supported through US NSF grants: 0549547, 0751771, 0813861, 0961188 and 1736679 (WES).Mid-ocean ridge (MOR) hydrothermal vent fluids are enriched with dissolved barium, but due to barite (BaSO4) precipitation during mixing between Ba-bearing vent fluids and SO4-bearing seawater, the magnitude of hydrothermal Ba input to the ocean remains uncertain. Deep-ocean Ba isotopes show evidence for non-conservative behavior, which might be explained by input of isotopically heavy hydrothermal Ba. In this study we present the first Ba isotope data in mid-ocean ridge hydrothermal vent fluids and particles from systems on the Mid-Atlantic Ridge (Rainbow 36°N and TAG 26°N), the East Pacific Rise (EPR9–10°N and 13°N) and the Juan de Fuca Ridge (MEF and ASHES). The vent fluids display a wide range of dissolved Ba concentrations from 0.43 to 97.9 μmol/kg and δ138/134Ba values from −0.26 to +0.91‰, but are modified relative to initial composition due to precipitation of barite. Calculated endmember vent fluid δ138/134Ba values, prior to barite precipitation, are between −0.17 and +0.09‰, consistent with the values observed in oceanic basalts and pelagic sediments. Water-rock interaction at depth in the oceanic crust appears to occur without Ba isotope fractionation. During subsequent venting and mixing with seawater, barite precipitation preferentially removes isotopically light Ba from vent fluids with a fractionation factor of Δ138/134Bahyd-barite-fluid = −0.35 ± 0.10‰ (2SE, n = 2). Based on knowledge of barite saturation and isotope fractionation during precipitation, the effective hydrothermal Ba component that mixes with seawater after barite precipitation has completed can be calculated: δ138/134Bahyd = +1.7 ± 0.7‰ (2SD). This value is isotopically heavier than deep ocean waters and may explain the observed non-conservative of Ba isotopes in deep waters. These new constraints on hydrothermal Ba compositions enable the hydrothermal input of Ba to Atlantic deep waters to be assessed at ≈3–9% of the observed Ba. Barium isotopes might be used as a tracer to reconstruct the history of hydrothermal Ba inputs and seawater SO4 concentrations in the past.PostprintPeer reviewe

Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches

Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer-by-layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi-parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML-based approach allowing to quickly identify optimized growth conditions for HKUST-I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films

LncRNA RUS shapes the gene expression program towards neurogenesis

The evolution of brain complexity correlates with an increased expression of long, noncoding (lnc) RNAs in neural tissues. Although prominent examples illustrate the potential of lncRNAs to scaffold and target epigenetic regulators to chromatin loci, only few cases have been described to function during brain development. We present a first functional characterization of the lncRNA LINC01322, which we term RUS for RNA upstream of Slitrk3. The RUS gene is well conserved in mammals by sequence and synteny next to the neurodevelopmental gene Slitrk3. RUS is exclusively expressed in neural cells and its expression increases during neuronal differentiation of mouse embryonic cortical neural stem cells. Depletion of RUS locks neuronal precursors in an intermediate state towards neuronal differentiation resulting in arrested cell cycle and increased apoptosis. RUS associates with chromatin in the vicinity of genes involved in neurogenesis, most of which change their expression upon RUS depletion. The identification of a range of epigenetic regulators as specific RUS interactors suggests that the lncRNA may mediate gene activation and repression in a highly context-dependent manner