Multi-source imagery fusion using deep learning in a cloud computing platform

Given the high availability of data collected by different remote sensing instruments, the data fusion of multi-spectral and hyperspectral images (HSI) is an important topic in remote sensing. In particular, super-resolution as a data fusion application using spatial and spectral domains is highly investigated because its fused images is used to improve the classification and tracking objects accuracy. On the other hand, the huge amount of data obtained by remote sensing instruments represent a key concern in terms of data storage, management and pre-processing. This paper proposes a Big Data Cloud platform using Hadoop and Spark to store, manages, and process remote sensing data. Also, a study over the parameter \textit{chunk size} is presented to suggest the appropriate value for this parameter to download imagery data from Hadoop into a Spark application, based on the format of our data. We also developed an alternative approach based on Long Short Term Memory trained with different patch sizes for super-resolution image. This approach fuse hyperspectral and multispectral images. As a result, we obtain images with high-spatial and high-spectral resolution. The experimental results show that for a chunk size of 64k, an average of 3.5s was required to download data from Hadoop into a Spark application. The proposed model for super-resolution provides a structural similarity index of 0.98 and 0.907 for the used dataset

Quantifying load imbalance on virtualized enterprise servers

Virtualization has been shown to be an attractive path to increase overall system resource utilization. The use of live virtual machine (VM) migration has enabled more effective sharing of system resources across multiple physical servers, resulting in an increase in overall performance. Live VM migration can be used to load balance virtualized clusters. To drive live migration, we need to be able to measure the current load imbalance. Further, we also need to accurately predict the resulting load imbalance produced by any migration. In this paper we present a new metric that captures the load of the physical servers and is a function of the resident VMs. This metric will be used to measure load imbalance and construct a load-balancing VM migration framework. The algorithm for balancing the load of virtualized enterprise servers follows a greedy approach, inductively predicting which VM migration will yield the greatest improvement of the imbalance metric in a particular step. We compare our algorithm to the leading commercially available load balancing solution- VMware’s Distributed Resource Scheduler (DRS). Our results show that when we are able to accurately measure system imbalance, we can also predict future system state. We find that we can outperform DRS and improve performance up to 5%. Our results show that our approach does not impose additional performance impact and is comparable to the virtual machine monitor overhead

Gorilla: An Open Interface for Smart Agents and Real-Time Power Microgrid System Simulations

A recurring issue when studying agent-based algorithms and strategies for Power Microgrid Systems is having to construct an interface between the agent domain and the electrical model domain being simulated. Many different tools exist for such simulations, each with its own special external interface. Although many interfacing efforts have been published before, many of them support only special cases, while others are too complex and require a long learning curve to be used for even simple scenarios. This work presents a simple programming application interface (API) that aims to provide programming access to the electrical system model for any real-time simulation tool, from any agent-based platform, or programming language. The simplicity of the interface stems from the assumption that the simulation happens in real-time and the agent domain is not being simulated. We propose four basic operations for the API: read, write, call, and subscribe/call-back. We tested these by supporting two examples. In one of the examples, we present a creative way to have the model access libraries that are not available in the simulated environment