Evaluation of Particle Swarm Optimization Applied to Grid Scheduling

The problem of scheduling independent users’ jobs to resources in Grid Computing systems is of paramount importance. This problem is known to be NP-hard, and many techniques have been proposed to solve it, such as heuristics, genetic algorithms (GA), and, more recently, particle swarm optimization (PSO). This article aims to use PSO to solve grid scheduling problems, and compare it with other techniques. It is shown that many often-overlooked implementation details can have a huge impact on the performance of the method. In addition, experiments also show that the PSO has a tendency to stagnate around local minima in high-dimensional input problems. Therefore, this work also proposes a novel hybrid PSO-GA method that aims to increase swarm diversity when a stagnation condition is detected. The method is evaluated and compared with other PSO formulations; the results show that the new method can successfully improve the scheduling solution

Service Evolution Patterns

Service evolution is the process of maintaining and evolving existing Web services to cater for new requirements and technological changes. In this paper, a service evolution model is proposed to analyze service dependencies, identify changes on services and estimate impact on consumers that will use new versions of these services. Based on the proposed service evolution model, four service evolution patterns are described: compatibility, transition, split-map, and merge-map. These proposed patterns provide reusable templates to encourage well-defined service evolution while minimizing issues that arise otherwise. They can be applied in the service evolution scenario where a single service is used by many, possibly unknown, consumers’ applications. In such a scenario, providers evolve their services independently from consumers, which might cause unexpected errors and incur unpredicted impact on the dependent consumers\u27 applications. Therefore, providers can use these patterns to estimate the impact that changes to be introduced to their services may cause on their consumers, and to allow consumers smoothly migrate to the newest version of the service

Network and Energy-Aware Resource Selection Model for Opportunistic Grids

Due to increasing hardware capacity, computing grids have been handling and processing more data. This has led to higher amount of energy being consumed by grids; hence the necessity for strategies to reduce their energy consumption. Scheduling is a process carried out to define in which node tasks will be executed in the grid. This process can significantly impact the global system performance, including energy consumption. This paper focuses on a scheduling model for opportunistic grids that considers network traffic, distance between input files and execution node as well as the execution node status. The model was tested in a simulated environment created using GreenCloud. The simulation results of this model compared to a usual approach show a total power consumption savings of 7.10%

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for electrical anomaly detection. To achieve semi-supervised learning, two sub-networks are used: the first performs reconstruction and uses unlabelled data, while the second performs classification with labelled data. The two sub-networks overlap: the encoder parameters are shared between the two. To quantify anomaly detection confidence, a valuable metric in anomaly detection, the network uses a dropout sampling method. The proposed approach has been evaluated with real-world electrical data from systems such as HVAC, lighting, and heat pumps. The results demonstrated the accuracy of the proposed anomaly detection solution

A Gamification Framework for Sensor Data Analytics

The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to gather labels, yet they cannot be directly used in the IoT because of poor human readability of sensor data. To overcome these limitations, this paper proposes a framework for sensor data analytics which leverages the power of crowdsourcing through gamification to acquire sensor data labels. The framework uses gamification as a socially engaging vehicle and as a way to motivate users to participate in various labelling tasks. To demonstrate the framework proposed, a case study is also presented. Evaluation results show the framework can successfully translate gamification events into sensor data labels

Query Analyzer and Manager for Complex Event Processing as a Service

Complex Event Processing (CEP) is a set of tools and techniques that can be used to obtain insights from high-volume, high-velocity continuous streams of events. CEP-based systems have been adopted in many situations that require prompt establishment of system diagnostics and execution of reaction plans, such as in monitoring of complex systems. This article describes the Query Analyzer and Manager (QAM) module, a first effort toward the development of a CEP as a Service (CEPaaS) system. This module is responsible for analyzing user-defined CEP queries and for managing their execution in distributed cloud-based environments. Using a language-agnostic internal query representation, QAM has a modular design that enables its adoption by virtually any CEP system

Graphene oxide-fullerene nanocomposite laminates for efficient hydrogen purification

Graphene oxide (GO) with its unique two-dimensional structure offers an emerging platform for designing advanced gas separation membranes that allow for highly selective transport of hydrogen molecules. Nevertheless, further tuning of the interlayer spacing of GO laminates and its effect on membrane separation efficiency remains to be explored. Here, positively charged fullerene C₆₀ derivatives are electrostatically bonded to the surface of GO sheets in order to manipulate the interlayer spacing between GO nanolaminates. The as-prepared GO-C₆₀ membranes have a high H₂ permeance of 3370 GPU (gas permeance units) and an H₂/CO₂ selectivity of 59. The gas separation selectivity is almost twice that of flat GO membranes because of the role of fullerene