Autonomic Computing System Research Articles

Adaptive Energy-Optimized Consolidation Algorithm

We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud. 
 The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance.

The autonomous choice architect

Choice architecture describes the environment in which choices are presented to decision-makers. In recent years, public and private actors have looked at choice architecture with great interest as they seek to influence human behaviour. These actors are typically called choice architects. Increasingly, however, this role of architecting choice is not performed by a human choice architect, but an algorithm or artificial intelligence, powered by a stream of Big Data and infused with an objective it has been programmed to maximise. We call this entity the autonomous choice architect. In this paper, we present an account of why artificial intelligence can fulfil the role of a choice architect and why this creates problems of transparency, responsibility and accountability for nudges. We argue that choice architects, be them autonomous computational systems or human-beings, at a most basic level select, from a range of designs, the design which is most likely to maximise a pre-determined objective. We then proceed to argue that, given the growing demand for targeted, personalised choice architecture and for faster, dynamic reconfigurations of choice architecture, as well as the ever-expanding pool of data from which feedback can be drawn, the role of the human choice architect is increasingly obscured behind algorithmic, artificially intelligent systems. We provide a discussion of the implications of autonomous choice architects, focusing on the importance of the humans who programme these systems, ultimately arguing that despite technological advances, the responsibility of choice architecture and influence remains firmly one human beings must bear.

The deployment of artificial intelligence in alternative dispute resolution: the AI augmented arbitrator

ABSTRACT Artificial Intelligence (AI) is reshaping the way legal services are delivered, which includes arbitrators in Alternative Dispute Resolution (ADR) proceedings. This trend is being accelerated by the coronavirus (COVID-19) pandemic, with many disputes being conducted online. Previously, legal tasks have usually been associated with human lawyers; however, increasingly autonomous computer systems may soon replace arbitrators in the delivery of legal services in ADR proceedings. This article evaluates the extent to which the deployment of AI may augment arbitrators in ADR proceedings and provides an analysis of how much legal work may be effectively performed by AI in ADR proceedings. The central focus is on the capabilities of AI systems against human arbitrators and the requirement for human intervention and supervision of AI systems deployed in ADR proceedings. This article extends the debate on AI and arbitrators where the focus is on the augmentation of legal services in ADR with AI systems and the implications therein. The doctrinal approach is employed to conduct the study. The primary objective of this article is to evaluate the extent to which the deployment of AI systems may augment the efficiency and productivity of legal services in ADR and to highlight the proposed recommendations.

An efficient metrics based self‐adaptive design model by multiobjective gray wolf optimization with extreme learning machine for autonomic computing system application

AbstractAutonomic computing refers to the capability of the software system to monitor environmental changes by itself. The primary objective of the proposed approach is to identify the quality of software components concerning complexity metrics. In this article, extreme learning machine (ELM) supported multiobjective gray wolf optimization (MOGWO) is proposed to predict the quality products. The proposed methodology considers four component‐based system metrics such as failure response time, throughput rate, interface surface consistency (ISC) and bounded interface complexity metrics (BICM) to resolve the complexity issues. The ELM is utilized in this article to conduct a prediction in the complexity metrics on the context‐awareness self‐adaptive autonomous system application. MOGWO is then utilized to optimize the cost metric. The evaluation results are compared with the existing approaches such as ELM, ANN, and ANN‐GWO. From the simulations, the overall accuracy rate of the prediction model is obtained to be 95%.

Autonomic computing system: Threats, security issues and an efficient approach for self protection

Autonomic computing frameworks are the self overseeing frameworks as indicated by the objectives outlined by the administrator of the system. Incorporation of new elements in framework supported by Autonomic Computing happens as simple as in human body a new cell recreates itself. Autonomic Computing Systems (ACS) are relied upon to accomplish an indistinguishable level of self-direction and inescapability from human autonomic frameworks. Due to the highlights of ACS, the customary security model cannot be connected to ACS any more. The objective of our exploration is to build up a setting based security model and engineering for ACS. Our attention is on self-security highlight of ACS. The self- assurance include is implemented through security settings that we characterize. By considering security settings, security arrangements would dynamic is able to change so as to adapt to new condition.

Reliability analysis of station autonomous computer system based on fuzzy dynamic fault tree and Markov model

AbstractStation autonomous computer system is the core of the centralized traffic control (CTC) system. The working mode is dual computer hot standby, and its reliability is very important. Due to the dynamic and fuzzy issue with the reliability analysis of station autonomous computer system, a reliability analysis method is formulated based on fuzzy dynamic fault tree (DFT) and Markov model. First, the structure and working principle of the dual computer hot standby autonomous computer system and the double 2‐vote‐2 autonomous computer system are analyzed, and two autonomous computer systems based on DFT are formulated. Then, the failure rates and the fault detection probabilities are considered as fuzzy variables and represented as triangular fuzzy variables. The fuzzy state transition matrix of the system is obtained by conversion of DFT to the Markov model. Finally, the fuzzy reliability of the system is calculated by Laplace transform. Simulation results show that the reliability of the dual computer hot standby autonomous computer system is higher than that of the double 2‐vote‐2 autonomous computer system. Besides, the reliability of the hardware system is not only related to the structure of the system, but also affected by the uncertain unit failure rate, fault detection probabilities, and so on.

The Autonomous Choice Architect

Increasingly, the entity which is meaningfully designing the environments in which individuals make decisions and interact is not a fellow human-being, but an algorithm or artificial intelligence, powered by a stream of Big Data and infused with an objective it has been programmed to maximise. This entity I call the autonomous choice architect. In this paper, I present a conceptual discussion of the autonomous choice architect, relating this concept to nudge theory and behavioural science more broadly. I argue that choice architects, be them autonomous computational systems or human-beings, at a most basic level merely select – from a range of designs – the design with is most likely to maximise a pre-determined objective. I then proceed to argue that, given the growing demand for targeted, personalised choice architecture and for faster, dynamic reconfigurations of choice architecture, as well as the ever-expanding pool of data from which feedback can be drawn, the role of the human choice architect is increasingly redundant. To illustrate this argument, I then provide a discussion of three contemporary studies which point towards the concept of the autonomous choice architect, before concluding.

HADEM-MACS: a hybrid approach for detection and extraction of objects in movement by multimedia autonomous computer systems

Nowadays, we assist in the multiplication of applications that need to exactly identify objects crossing a field watched by cameras. Multimedia information thus becomes an inescapable medium for the validation of applications such as identification, localisation and object tracking. This gives rise to many processing methods that, after collecting multimedia data (images, videos), continue with a pre-processing in order to reduce noise, finally it finishes with processing in order to extract objects, more precisely the form of object that capture our domain of interest. The first stage consists of detecting objects in movement in the scene. This detection passes through a background modelling. Model based on mixture of Gaussian is commonly used and it is classified in the categories of parametric approaches. However, many other approaches exist today in literature each of them having its advantages and drawbacks. We present in this paper a hybrid approach of movement detection which combines an improved version of mixture of Gaussian with a neural network-based approach.

Introduction to the Special Issue with Selected Papers of The International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) 2020

Introduction to the Special Issue with Selected Papers of The International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) 2020

HADEM-MACS: a hybrid approach for detection and extraction of objects in movement by multimedia autonomous computer systems

Nowadays, we assist in the multiplication of applications that need to exactly identify objects crossing a field watched by cameras. Multimedia information thus becomes an inescapable medium for the validation of applications such as identification, localisation and object tracking. This gives rise to many processing methods that, after collecting multimedia data (images, videos), continue with a pre-processing in order to reduce noise, finally it finishes with processing in order to extract objects, more precisely the form of object that capture our domain of interest. The first stage consists of detecting objects in movement in the scene. This detection passes through a background modelling. Model based on mixture of Gaussian is commonly used and it is classified in the categories of parametric approaches. However, many other approaches exist today in literature each of them having its advantages and drawbacks. We present in this paper a hybrid approach of movement detection which combines an improved version of mixture of Gaussian with a neural network-based approach.

CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment

The operating system (OS) is now being widely used in many of the various modern applications in recent years, ranging from diabetic monitoring to other strategic planning. The main function of any OS is to control and coordinate all hardware resources among the commands of the same/different user, which increases the efficiency of advanced comprehensive applications. In an autonomous computer system, the CPU is one of the important resources to manage and process all activities which require scheduling techniques on a processor. Since the early days of computing and other multi-programming OS, various studies have been assigned to CPU scheduling techniques based on processes management and performance evaluation. Thus, outlining the many issues related to scheduling methodologies and the weaknesses that need to be addressed. This review paper is organized based on two distinct perspectives: the implement strategies of CPU scheduling technique and criteria-based measures used, which assess how the strategies are analyzed and used under performance evaluation.

An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique

Quality assurance and evaluation has always been a key cause of concern for software developers. This problem has been further aggravated by the complete dependence of business enterprises, financial institutions and stock markets on computer hardware and software. It is therefore needed to propose and develop such software evaluation and quality assurance techniques that can fit into the business model’s domain and satisfy the customer needs and aspirations. Autonomic computation is an artificial intelligent based approach used to design and develop software systems which can fit into business model and also satisfy customer needs. These systems are built with self-managed policy system. To guarantee their customers a Total Quality Assurance on the business applications being developed, the paper presents some key aspects of domain-specific software and its quality estimation parameter. In this paper, the authors have analyzed the various aspects of quality metrics of autonomic computation suggested by enhanced ISO 9126 quality model. A universally acceptable approach to assure quality for autonomic computing system would be to measure the Autonomicity of a system to determine whether it is autonomic or not. If it is autonomic then “to what extent” is the next question? Autonomicity is an excellent indicator to assure quality of the autonomic software. The approach taken to measure the subjective attribute of Autonomicity is fuzzy theory with Analytic Hierarchy Process (AHP) integrated in it. Human assessment is qualitative and fuzzy technique is best candidate to quantify their opinions. For empirical analysis, three different query optimizers are examined to measure autonomicity. The result of the empirical analysis will be validated using the already proposed results of the research studies. The present study will provide a base for further research in terms of development of applications with autonomic features. It will also help in proposing new metrics for quality characteristics to estimate the overall quality of such application.

A formal approach to the engineering of domain-specific distributed systems

We review some results regarding specification, programming and verification of different classes of distributed systems which stemmed from the research of the Concurrency and Mobility Group at University of Firenze. More specifically, we examine the distinguishing features of network-aware programming, service-oriented computing, autonomic computing, and collective adaptive systems programming. We then present an overview of four different languages, namely Klaim, Cows, Sceland AbC. For each language, we discuss design choices, present syntax and semantics, show how the different formalisms can be used to model and program a travel booking scenario, and describe programming environments and verification techniques.

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.

Building Autonomic Elements from Video-Streaming Servers

HTTP Streaming is nowadays the main approach for delivering video-streaming on the Internet. As a consequence of that, the widely deployed HTTP infrastructures face new challenges posed by the sensitivity of video-streaming users to service quality degradation and the specificities of video-streaming workloads. Performance issues represent one main class of problems in the server infrastructure that can result into a significant deterioration of the end-users’ quality of experience (QoE), proportional to the upfront time spent by them watching the videos. This paper addresses the development of autonomic HTTP Streaming servers organized into Autonomic Elements (AEs), the building blocks of Autonomic Computing (AC) systems. AEs are structured using container-based virtualization and are provided with monitoring, failure prediction, failure diagnosis and repair features. These features are incorporated into SHStream, a self-healing framework developed by us. SHStream relies on online learning algorithms to build and evaluate classification models dynamically for prediction and diagnosis of performance anomalies. The results of our experimental analysis have shown that: (1) failure prediction can be performed with approximately $$98\%$$ of recall and $$99\%$$ of precision; (2) the diagnosis activity can localize and identify the resource responsible for performance failures, without misclassifications; (3) the classifiers’ performance stabilizes using a small number of learning instances; and (4) container-based virtualization technologies enable recovery times shorter than 1 s through rebooting and shorter than 3 s using server migration techniques.

К ПРОБЛЕМЕ КЛАССИФИКАЦИИ GENERATIVE ART: РАЗРАБОТКА ПОНЯТИЙНОГО АППАРАТА

The article refers to the problem of generative art taxonomy as a complex interdisciplinary phenomenon and analysis of its key features. The generative art is a relevant cultural and artistic phenomenon in Russian and foreign art that emerged in the 1910s within Russian and West European avant-garde painters and received a new boost for development thanks to contemporary computer technologies. Based on the classification model proposed by foreign researchers, M. Boden and E. Edmonds, the author of this article identifies concepts like Generative Art (G-art), Computer Art (C-art), Interactive Art (I-art), Interactive Computer Art (CI-art), and Computer Generative Art (CG-art) and introduces new scientific terms: Interactive Generative Art and Interactive Computer- Generated Art. The first one refers to a series of works of art created with the active participation of the audience and the use of traditional (non- computer) autonomous systems that provide a certain degree of randomness without the implementation of multimedia technologies. The second one refers to a series of artworks created by autonomous computer systems and involving active interaction with the audience. These phenomena are considered based on the art object’s possessing the features of generative (G), interactive (I) or computer (C) vectors that underlie the classification method of Gen Art and adjacent contextual phenomena developed by the author and visually modeled by him in the Cartesian coordinate system. Emphasis was made on Interactive Computer-Generated Art. A media installation by Stain art group called Mobile Interactive Multi-Parametric Image (MIMPI) is provided as an example; it is an interactive audio-visual environment first introduced in MEL Space gallery (Moscow) in May 2012 Finally, the author comes to the conclusion that granting the generative art the status of a natural transitional phase in the art history of the 20th — early 21st centuries, as proposed by him, is justified, since this art combines “dry” and “wet” technologies and the corresponding types of cultures.

Design Quality Metrics to Determine the Suitability and Cost-Effect of Self-* Capabilities for Autonomic Computing Systems

Every software in the universe requires maintenance and management during its life cycle. The manual management of software is costly and sometimes error-prone. The other solution is autonomic computing that induces self-management capabilities, “self-*services”, in software systems with the help of autonomic managers. The design quality of a self-management capability affects the computing infrastructure regarding processing load, the memory requirement, data channel demand and performance of perturbation restore. It is critical to assess the design quality of a self-management capability to determine its effect over the computing infrastructure when it gets invoke against some anomaly or perturbation. Moreover, there are two possible host environments for an autonomic manager to offer a self-management capability as a self-* service: the local environment and the cloud environment. A criterion is needed to decide which environment is more suitable and cost-effective to run the service. However, the literature lacks in the assessment of the design quality metrics on self-management capabilities and the suitability and cost-effectiveness of the execution environment. In this work, we have proposed a suite of design quality metrics to determine the design quality of self-management capabilities. We validate the proposed metrics with a stock trade & forecasting system that was designed as an autonomic computing system with self-management capabilities. The proposed metrics were applied to define functions that identify the suitable and cost-effective execution environment for the self-* service. The results proved that these metrics are useful in determining the design quality, suitability, and cost-effectiveness of a self-* capability for an autonomic computing system. The proposed metrics can be used to compare differently designed autonomic solutions for complexity, efficiency, performance, understandability, and maintainability.

Implementing Hy-IDS, Mobiles Agents and Virtual Firewall to Enhance the Security in IaaS Cloud

The growth in customer requirements, big data analysis and pressures on response time, high costs of network platforms pushed companies to migrate to Cloud Computing providing on demand internet hosted IT services. The increase of Cloud users and their interactions with the Cloud infrastructure raise the risk of resources faults. Such a problem can lead to a bad reputation of the Cloud environment, which slows down the evolution of this paradigm. The dynamic architecture and the complex system of the Cloud should be taken into account. In fact, this paradigm Cloud requires that resources protection and healing must be effective, transparent and without external intervention. Thus, it is essential the use fundamental aspects of autonomic Computing in the Cloud to deal with the self-healing of Cloud security. The high degree of match between autonomic Computing systems and multi-agent systems permit to create an intelligent architecture Cloud that support autonomic aspects. Therefore, we propose a cooperative framework based on Hybrid intrusion detection system (Hy-IDS), mobile Agents and Firewall, which permit to detect both insider and outsider attacks with high detection accuracy in Cloud environment. In this paper, we propose a Cloud Computing framework offering the access security, ease of resources management using mobile agents and service availability in a reliable structure with lower cost.

Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe

Many societies are on the brink of a robotic era. In the near future, various autonomous computer systems are expected to be part of many people's daily lives. Because attitudes influence the adoption of new technologies, we studied the attitudes towards robots in the European Union between 2012 and 2017. Using representative samples from 27 countries (three waves, total N = 80,396), these analyses showed that, within five years, public opinions regarding robots exhibited a marked negative trend. Respondents became more cautious towards the use of robots. This tendency was particularly strong for robots at the workplace, which are, despite the drop, still more positively evaluated than robots performing surgeries or autonomous cars. Attitudes were more positive among men and people in white-collar jobs. Moreover, countries with a larger share of older citizens evaluated robotic assistance more favorably. In general, these results highlight increasing reservations towards autonomous robotic systems in Europe.

Autonomous multimedia cluster computing based on Cooperative Cognition data behavior measurement under multi cloud computing

In order to meet the needs of multimedia communication in multi cloud environment and improve the experience quality of mobile multimedia users, based on multi cloud computing, based on Cooperative cognitive data behavior measurement, this paper proposes an autonomous multimedia cluster computer system and its architecture. First of all, dispersed edge clouds are distributed and integrated, and cooperate to provide multimedia storage and computing functions. In cloudy environment, a hierarchical access service point is designed between edge cloud and core cloud. On this basis, a multimedia cluster computing system suitable for cloudy environment is built. Secondly, a mulch-dimensional mapping mechanism is built between the link management interface array and the task scheduling array in the edge cloud array. Mulch-dimensional multimedia data and real-time task scheduling cooperation cognition, and core cloud are used to interact with DP vectors with dedicated channels. On this basis, we propose an autonomous computer system based on Cooperative Cognition and multimedia data behavior measure. Finally, it is analyzed by three groups of experiments. The resource utilization of the multimedia cluster computing system, the behavior measurement accuracy based on the cooperative cognitive multimedia data behavior measure and the performance of the proposed autonomous multimedia cluster computing (AMC-CCC) in large-scale real-time multimedia communication applications. The results show that the proposed AMC-CCC mechanism has excellent performance in multimedia QoS, resource management and data behavior measurement.