Virtual Sensor Middleware: Managing IoT Data for the Fog-Cloud Platform

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern to allow virtual sensors to receive data from other virtual sensors for seamless sensor data consumption without tight integration among virtual sensors, which reduces application development efforts. Furthermore, VSM enhances the design of virtual sensors with additional components that support sharing of data in dynamic environments where data receivers may change over time, data aggregation is required, and dealing with missing data is essential for the applications

Formal Generation of Executable Assertions for Application-Oriented Fault Tolerance

Executable assertions embedded into a distributed computing system can provide run-time assurance by ensuring that the program state, in the actual run-time environment, is consistent with the logical stage specified in the assertions; if not, then an error has occurred and a reliable communication of this diagnostic information is provided to the system such that reconfiguration and recovery can take place. Application- oriented fault tolerance is a method that provides fault detection using executable assertions based on the natural constraints of the application. This paper focuses on giving application-oriented fault tolerance a theoretical foundation by providing a mathematical model for the generation of executable assertions which detect faults in the presence of arbitrary failures. The mathematical model of choice was axiomatic program verification. A method was developed that translates a concurrent verification proof outline into an error-detecting concurrent program. This paper shows the application of the developed method to several applications

Differentiating Web Service Offerings

The advent of Service Oriented Architecture (SOA) paradigm and increasing use of Web Services (WS) implies that the future will see a large number of services transferred between providers and consumers, using many applications or agents working on behalf of humans. Discovering and using the services is the easy part. Negotiating and selecting the best services from amongst the plethora of similar ones, depending on their cost and quality, is the challenging issue. However, existing WS-I standards neither cater to provision of Service Level Agreements (SLAs), nor their exchange between parties. These standards are confined merely to WS description (WSDL). Once WS are discovered and selected, SLAs are merely used to monitor service compliance. We propose a novel method that allows service-providers to dynamically generate the SLAs, and then transfer them to clients for selection amongst competitive service providers. The clients use Application to Application (A2A) communication to choose the best service provider at run time, and then bind to it to available services. Our method complies with all WS-I standards, and hence does not require any modifications to the UDDI or WSDL. Instead of using the SLA as just a contractual document for compliance monitoring of the service, we also use it as a means of service selection. We demonstrate and validate our method using a prototype developed in laboratory settings, which uses multiple ‘Weather Service Providers’ to obtain various indicators for weather forecasting

Optimizing Management Functions in Distributed Systems

With the increased availability and complexity of distributed systems comes a greater need for solutions to assist in the management of distributed systems. Despite the significant contributions made towards the development of management tools that monitor and control distributed systems, little has been done to address issues such as optimizing the execution of management functions with respect to system and management requirements. This paper presents a management optimization model in which management agents and managed objects are efficiently configured on the basis of a set of system and management requirements. We illustrate our model and describe its implementation through a Branch- and Bound-based algorithm and a web-based interface. The latter enables users to specify the requirements used by the optimization algorithm to determine efficient management configurations. It also includes an XML-based interface through which management agents can be started independent of the underlying platforms. Performance characteristics of the proposed algorithm as well as experimental results to illustrate the validity of the model are also described.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45003/1/10922_2004_Article_454093.pd

A distributed approach to dynamic vm management

Abstract-Computing today is increasingly moving into largescale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management

Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.Comment: Preprint submitted to IoTCPS Elsevier (2023

Transformative effects of ChatGPT on modern education: emerging era of AI chatbots

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial

Multi-microservice migration modelling, comparison, and potential in 5G/6G mobile edge computing: A non-average parameter values approach

Cloud, fog, and edge computing integration with future mobile Internet-of-Things (IoT) devices and related applications in 5G/6G networks will become more practical in the coming years. Containers became the de facto virtualization technique that replaced Virtual Memory (VM). Mobile IoT applications, e.g., intelligent transportation and augmented reality, incorporating fog-edge, have increased the demand for a millisecond-scale response and processing time. Edge Computing reduces remote network traffic and latency. These services must run on edge nodes that are physically close to devices. However, classical migration techniques may not meet the requirements of future mission-critical IoT applications. IoT mobile devices have limited resources for running multiple services, and client-server latency worsens when fog-edge services must migrate to maintain proximity in light of device mobility. This study analyzes the performance of the MiGrror migration method and the pre-copy live migration method when the migration of multiple VMs/containers is considered. This paper presents mathematical models for the stated methods and provides migration guidelines and comparisons for services to be implemented as multiple containers, as in microservice-based environments. Experiments demonstrate that MiGrror outperforms the pre-copy technique and, unlike conventional live migrations, can maintain less than 10 milliseconds of downtime and reduce migration time with a minimal bandwidth overhead. The results show that MiGrror can improve service continuity and availability for users. Most significant is that the model can use average and non-average values for different parameters during migration to achieve improved and more accurate results, while other research typically only uses average values. This paper shows that using only average parameter values in migration can lead to inaccurate results