Fault aware hybrid harmony search technique for optimal resource allocation in cloud

The evolution of cloud computing is increasing exponentially which provides everything as a service. Clouds made it possible to move a huge amount of data over the networks on-demand. It removed the physical necessity of resources as resources are available virtually over the networks. Emerge of new technologies improvising the cloud system and trying to overcome cloud computing challenges like resource optimization, securities etc. Proper utilization of resources is still a primary target for the cloud system as it will increase the cost and time efficiency. Cloud is a pay-per-uses basis model which needs to perform in a flexible manner with the increase and decrease in demand on every level. In general, cloud is assumed to be non-faulty but faulty is a part of any system. This article focuses on the hybridization of Neural networks with the harmony Search Algorithm (HSA). The hybrid approach achieves a better optimal solution in a feasible time duration in the faulty environment to improve the task failure and improve reliability. The harmony Search approach is inspired from the music improvisation technique, where notes are adjusted until perfect harmony is matched. HS (Harmony search) is chosen, as it is capable to provide an optimal solution in a feasible time, even for complex optimization problems. An ANN-HS model is introduced to achieve optimal resource allocation. The presented model is inspired by Harmony Search and ANN. The proposed model considers multi-objective criteria. The performance criteria include execution time, task failure count and power consumption(Kwh).

Hydrometeor Classification of Winter Precipitation in Northern China Based on Multi-Platform Radar Observation System

Hydrometeor classification remains a challenge in winter precipitation cloud systems. To address this issue, 42 snowfall events were investigated based on a multi-platform radar observation system (i.e., X-band dual-polarization radar, Ka-band millimeter wave cloud radar, microwave radiometer, airborne equipment, etc.) in the mountainous region of northern China from 2016 to 2020. A fuzzy logic classification method is proposed to identify the particle phases, and the retrieval result was further verified with ground-based radar observation. Moreover, the hydrometeor characteristics were compared with the numerical simulations to clarify the reliability of the proposed hydrometeor classification approach. The results demonstrate that the X-/Ka- band radars are capable of identifying hydrometeor phases in winter precipitation in accordance with both ground observations and numerical simulations. Three particle categories, including snow, graupel and the mixture of snow and graupel are also detected in the winter precipitation cloud system, and there are three vertical layers identified from top to bottom, including the ice crystal layer, snow-graupel mixed layer and snowflake layer. Overall, this study has the potential for improving the understanding of microphysical processes such as freezing, deposition and aggregation of ice crystal particles in the winter precipitation cloud system.

Robust Image-Based Streamflow Measurements for Real-Time Continuous Monitoring

The volumetric flow rate in rivers is essential to analyze hydrological processes and at the same time it is one of the most difficult variables to measure. Image based discharge measurements possess several advantages, one of them being that the sensor (camera) is not in contact with the water, it can be placed safe of floods, its mounting position is very flexible and there is no need of expensive structures/constructions. During the last years several image-based methods for measuring the surface velocity in rivers and canals have been proposed and successfully tested under different conditions. However, these methods have been used and configured to perform well under the particular conditions of a single recording or single site. The objective of this paper is to present a system which has reached a Technology Readiness Level (TRL) 9. The system is able to measure the volumetric flow under different conditions day and night and all year long, the system is able to perform in rivers or canals of different sizes and flow velocities and under different conditions of visibility. In addition, the system is capable of measuring the river stage optically without the need of a stage, but it can also integrate external level sensor. Important for a wide set of customers, the system must be able to interface with the various common signal input and output standards, such as 4–20 mAmp, modbus, SDI-12, ZRXP, and even with customer specific formats. Additionally, the developed technology can be implemented as an edge or as a cloud system. The cloud system only needs a camera with Internet connection to send videos to the cloud where they are processed, while the edge systems have a processing unit installed at the site where the processing is done. This paper presents the key aspects needed to move from prototype with TRL5-7 and lower toward the presented field proven system with a TRL 9.