Maximizing Service Reliability in Distributed Computing Systems with Random Node Failures: Theory and Implementation

In distributed computing systems (DCSs) where server nodes can fail permanently with nonzero probability, the system performance can be assessed by means of the service reliability, defined as the probability of serving all the tasks queued in the DCS before all the nodes fail. This paper presents a rigorous probabilistic framework to analytically characterize the service reliability of a DCS in the presence of communication uncertainties and stochastic topological changes due to node deletions. The framework considers a system composed of heterogeneous nodes with stochastic service and failure times and a communication network imposing random tangible delays. The framework also permits arbitrarily specified, distributed load-balancing actions to be taken by the individual nodes in order to improve the service reliability. The presented analysis is based upon a novel use of the concept of stochastic regeneration, which is exploited to derive a system of difference-differential equations characterizing the service reliability. The theory is further utilized to optimize certain load-balancing policies for maximal service reliability; the optimization is carried out by means of an algorithm that scales linearly with the number of nodes in the system. The analytical model is validated using both Monte Carlo simulations and experimental data collected from a DCS testbed

Reliability of Heterogeneous Distributed Computing Systems in the Presence of Correlated Failures

While the reliability of distributed-computing systems (DCSs) has been widely studied under the assumption that computing elements (CEs) fail independently, the impact of correlated failures of CEs on the reliability remains an open question. Here, the problem of modeling and assessing the impact of stochastic, correlated failures on the service reliability of applications running on DCSs is tackled. The service reliability is modeled using an integrated analytical and Monte-Carlo (MC) approach. The analytical component of the model comprises a generalization of a previously developed model for reliability of non-Markovian DCSs to a setting where specific patterns of simultaneous failures in CEs are allowed. The analytical model is complemented by a MC-based procedure to draw correlated-failure patterns using the recently reported concept of probabilistic shared risk groups (PSRGs). The reliability model is further utilized to develop and optimize a novel class of dynamic task reallocation (DTR) policies that maximize the reliability of DCSs in the presence of correlated failures. Theoretical predictions, MC simulations, and results from an emulation testbed show that the reliability can be improved when DTR policies correctly account for correlated failures. The impact of correlated failures of CEs on the reliability and the key dependence of DTR policies on the type of correlated failures are also investigated

Performance and Reliability of Non-Markovian Heterogeneous Distributed Computing Systems

Average service time, quality-of-service (QoS), and service reliability associated with heterogeneous parallel and distributed computing systems (DCSs) are analytically characterized in a realistic setting for which tangible, stochastic communication delays are present with nonexponential distributions. The departure from the traditionally assumed exponential distributions for event times, such as task-execution times, communication arrival times and load-transfer delays, gives rise to a non-Markovian dynamical problem for which a novel age dependent, renewal-based distributed queuing model is developed. Numerical examples offered by the model shed light on the operational and system settings for which the Markovian setting, resulting from employing an exponential-distribution assumption on the event times, yields inaccurate predictions. A key benefit of the model is that it offers a rigorous framework for devising optimal dynamic task reallocation (DTR) policies systematically in heterogeneous DCSs by optimally selecting the fraction of the excess loads that need to be exchanged among the servers, thereby controlling the degree of cooperative processing in a DCSs. Key results on performance prediction and optimization of DCSs are validated using Monte-Carlo (MC) simulation as well as experiments on a distributed computing testbed. The scalability, in the number of servers, of the age-dependent model is studied and a linearly scalable analytical approximation is derived

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

Pengelolaan Limbah Tandan Kosong Kelapa Sawit Dan Aplikasi Biomassa Chromolaena Odorata Terhadap Pertumbuhan Dan Hasil Tanaman Padi Serta Sifat Tanah Sulfaquent

OIL PALM EMPTY BUNCHES WASTE MANAGEMENT AND APLICATION OF BIOMASS Chromolaena odorata ON GROWTH AND YIELD OF RICE PLANT AND SULFAQUENT SOIL PROPERTIES. Sulfaquent soil have prospects for the development of the rice crop area if managed properly. The limitations of this type of land, the use of biomass chromolaena odorata and compostable palm empty fruit bunches into an alternative to overcome the problems in this type of land. This study aimed to determine the role of biomass chromolaena odorata and compostable palm empty fruit bunches against sulfaquent soil properties (pH and soil puddling) as well as the growth of rice plants (plant height and number of chlorophyll). Method in the form of a factorial field experiment with completely randomized design (CRD), which consists of two factors used in this experiment. The first factor is the application of biomass Chromolaena odorata many as three levels: control = c1, c2 = 25 g / polybag, and c3 = 50 g / polybag. The second factor is compost oil palm empty fruit bunches (t) at a dose of t1 = control, t2 = 25 g / polybag, and t3 = 50 g / polybag, in order to obtain 9 combination treatment. The results showed the combination treatment c3t2 significant effect on soil pH that is equal to 6.22; treatment c2 significant effect on the amount of chlorophyll that is 39.52 units. Applications chromolaena odorata and empty fruit bunches of oil did not affect the puddling, plant height, and grain weight, but treatment c3t2 give puddling average value index (IP) of 17.92%, and treatment c1t1 produce a mean value of 90.03 cm plant height, and treatment c3t3 produce the highest grain weight is 71.3 grams / clum

Ultra-Fast Stark-Induced Control of Polaritonic States

A laser pulse, several meV red-detuned from the excitonic line of a quantum well, has been shown to induce an almost instantaneous and rigid shift of the lower and upper polariton branches. Here we demonstrate that through this shift, ultra-fast all-optical control of the polariton population in a semiconductor microcavity should be achievable. In the proposed setup a Stark field is used to bring the lower polariton branch in or out of resonance with a quasi-resonant continuous-wave laser, thereby favoring or inhibiting the injection of polaritons into the cavity. Moreover we show that this technique allows for the implementation of optical switches with extremely high repetition rates

MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images

Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future

Observing the Onset of Effective Mass

The response of a particle in a periodic potential to an applied force is commonly described by an effective mass which accounts for the detailed interaction between the particle and the surrounding potential. Using a Bose-Einstein condensate of 87-Rb atoms initially in the ground band of an optical lattice, we experimentally show that the initial response of a particle to an applied force is in fact characterized by the bare mass. Subsequently, the particle response undergoes rapid oscillations and only over timescales long compared to that of the interband dynamics is the effective mass observed to be an appropriate description