Blockchain and Reinforcement Neural Network for Trusted Cloud-Enabled IoT Network

The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAWMOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency

Towards Autonomous Computer Networks in Support of Critical Systems

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Digital Twin Technology: A Review of Its Applications and Prominent Challenges

Digital twin is a virtual representation of physical product that is used as benchmark to evaluate, diagnose, optimize and supervise operational performance of products before venturing into mass full production in accordance with global standard. Digital twin merges virtual and physical objects together via sensors and IoT to transmit data and keep traces of objects interactivity within present environments. In virtual model environment, digital twin permits product troubleshooting and testing to minimize rate of failure and product defects during product manufacturing to enhance effectiveness and customers’ satisfaction. Digital twin is utilized throughout product life-cycle to simulate, optimize and predict product quality before final production is financed. Digital twin is beneficial to modern digital society because attitude of modern factory workers can be boosted to improve motivation to work. Digital twin has come to stay, future product suppliers may be required to put forward digital twin of their products beforehand for virtual lab testing before making order while suppliers that fail to comply may be left over. With emergence of digital twin, virtual testing can be conducted on proposed products before finding their ways into physical marketplaces. Business sector remains most beneficiaries of digital twin to predict present and future state of physical product via digital peer analysis. Today, digital twin application can support enterprises by improving product performances, decision making and customers’ satisfactions on logistic and operational workflow. However, in this survey of digital twin research, efforts have been made to review in detail about digital twin, its impact and benefits to modern society, its architecture; security challenges and how solutions are proffered. It is believed that ICT experts, manufacturers and industries will leverage on this research to improve QoS (Quality of Service) for new and future products to take full advantage of profits on investment returns via digital twin

Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Data-Driven Methods for Data Center Operations Support

During the last decade, cloud technologies have been evolving at an impressive pace, such that we are now living in a cloud-native era where developers can leverage on an unprecedented landscape of (possibly managed) services for orchestration, compute, storage, load-balancing, monitoring, etc. The possibility to have on-demand access to a diverse set of configurable virtualized resources allows for building more elastic, flexible and highly-resilient distributed applications. Behind the scenes, cloud providers sustain the heavy burden of maintaining the underlying infrastructures, consisting in large-scale distributed systems, partitioned and replicated among many geographically dislocated data centers to guarantee scalability, robustness to failures, high availability and low latency. The larger the scale, the more cloud providers have to deal with complex interactions among the various components, such that monitoring, diagnosing and troubleshooting issues become incredibly daunting tasks. To keep up with these challenges, development and operations practices have undergone significant transformations, especially in terms of improving the automations that make releasing new software, and responding to unforeseen issues, faster and sustainable at scale. The resulting paradigm is nowadays referred to as DevOps. However, while such automations can be very sophisticated, traditional DevOps practices fundamentally rely on reactive mechanisms, that typically require careful manual tuning and supervision from human experts. To minimize the risk of outages—and the related costs—it is crucial to provide DevOps teams with suitable tools that can enable a proactive approach to data center operations. This work presents a comprehensive data-driven framework to address the most relevant problems that can be experienced in large-scale distributed cloud infrastructures. These environments are indeed characterized by a very large availability of diverse data, collected at each level of the stack, such as: time-series (e.g., physical host measurements, virtual machine or container metrics, networking components logs, application KPIs); graphs (e.g., network topologies, fault graphs reporting dependencies among hardware and software components, performance issues propagation networks); and text (e.g., source code, system logs, version control system history, code review feedbacks). Such data are also typically updated with relatively high frequency, and subject to distribution drifts caused by continuous configuration changes to the underlying infrastructure. In such a highly dynamic scenario, traditional model-driven approaches alone may be inadequate at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust data-driven methods to support their decisions based on historical information. For instance, effective anomaly detection capabilities may also help in conducting more precise and efficient root-cause analysis. Also, leveraging on accurate forecasting and intelligent control strategies would improve resource management. Given their ability to deal with high-dimensional, complex data, Deep Learning-based methods are the most straightforward option for the realization of the aforementioned support tools. On the other hand, because of their complexity, this kind of models often requires huge processing power, and suitable hardware, to be operated effectively at scale. These aspects must be carefully addressed when applying such methods in the context of data center operations. Automated operations approaches must be dependable and cost-efficient, not to degrade the services they are built to improve. i

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

Overlay virtualized wireless sensor networks for application in industrial internet of things : a review

Abstract: In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin