52,306 research outputs found
Resource Cube:Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks
Industrial Internet of Things (IIoT) has found wider research, and satellite-terrestrial network (STN) can provide large-scale seamless connections for IIoT. With virtualization, we design resource cube to describe the integration and state of multi-dimensional virtual resources. To achieve higher resource utilization and smarter connections, we design a matching considered preferences (MCPR) algorithm to match IIoT nodes with service sides. The matching design considers the resource cube (MCRC) algorithm based on MCPR algorithm to lower the total system delay. In addition, in order to simplify the analysis of resource management, we adopt a layered architecture and multiple M/M/1 queuing models. We analyze the resource utilization and the total system delay for three different combinations of arrival rate and service rate of each resource cube. With MCRC algorithm, the utilization of resources is slightly reduced, while the total system delay is greatly reduced compared with MCPR algorithm. © 1967-2012 IEEE
Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects
While monolithic satellite missions still pose significant advantages in terms of accuracy and
operations, novel distributed architectures are promising improved flexibility, responsiveness,
and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite
networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance
satellites are becoming feasible and advantageous alternatives requiring the adoption
of new operation paradigms that enhance their autonomy. While autonomy is a notion that
is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic
in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations
for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy
is also presented as a necessary feature to bring new distributed Earth observation functions
(which require coordination and collaboration mechanisms) and to allow for novel structural
functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission
Planning and Scheduling (MPS) frameworks are then presented as a key component to implement
autonomous operations in satellite missions. An exhaustive knowledge classification explores the
design aspects of MPS for DSS, and conceptually groups them into: components and organizational
paradigms; problem modeling and representation; optimization techniques and metaheuristics;
execution and runtime characteristics and the notions of tasks, resources, and constraints.
This paper concludes by proposing future strands of work devoted to study the trade-offs of
autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that
consider some of the limitations of small spacecraft technologies.Postprint (author's final draft
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
Anomaly Detection in Cloud Components
Cloud platforms, under the hood, consist of a complex inter-connected stack
of hardware and software components. Each of these components can fail which
may lead to an outage. Our goal is to improve the quality of Cloud services
through early detection of such failures by analyzing resource utilization
metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood
function to detect anomalies in various multi-dimensional time series and
achieved high performance.Comment: Accepted for publication in Proceedings of the IEEE International
Conference on Cloud Computing (CLOUD 2020). Fix dataset descriptio
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