Abstract: The objective of this chapter is to introduce the concept of cloud computing by presenting a quick refresh of traditional computing model, namely monolithic computing, client–server computing, distributed computing, cluster computing, grid computing, etc., as well as comparing and contrasting it with other computing models, including recent advancements.

Abstract: This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

Abstract: High performance computing makes many contemporary pervasive computing interactions possible such as increasingly accurate weather forecasts, market-scale financial services on demand, and big data analysis for highly personalized services. However, access to high performance computing resources is not necessarily equitable. Not everyone is uplifted by this computational power. Rather, high performance computing access can be seen as another aspect of the digital divide. We acknowledge the ways in which equitable access to high performance computing resources has improved over time while also identifying potential threats to access of these resources. We provide considerations from the perspective of two popular ethical theories (contractarianism and utilitarianism) for reasoning about how these threats may be overcome or prevented from coming to pass. These perspectives can be extended to inform policy created by high performance computing providers.

Abstract: Recent years have witnessed the emergence of big services, as a large‐scale big data‐centric service model, that resulted from the synergy between powerful computing paradigms (big data processing, service and cloud computing, Internet of Things, etc.). Big services are seen as a heterogeneous combination of physical and virtualized domain‐specific resources, with a huge volume of data and complex functionalities, all encapsulated and offered as services. This complexity of big services (composition units' heterogeneity, cross‐domain orientation, data massiveness), coupled with other environmental factors (cloud dynamicity, providers' policies, customer requirements) makes their management tasks beyond humans' capability. Therefore, endowing big service ecosystems with self‐adaptive behavior is a natural solution. To achieve this goal, this article models big services as autonomic computing systems, and structures their behavioral aspects (functional behavior, quality of service/data levels, management policies) as a multi‐view knowledge graph. To infer useful knowledge (e.g., conflicts between policies) for the autonomic big service's management tasks, we process the big service's knowledge graph (BSKG) via a graph neural network‐based graph embedding model. This latter is reinforced by an incremental learning method, that helps capturing the big services' frequent changes (e.g., QoS deviations, service failures, new policies), and drives autonomic managers to continuously update and enrich their knowledge w.r.t. the managed big service's current state. Finally, a flexible decision mechanism explores the BSKG structure and the latent knowledge, to locate and trigger the appropriate management policies, according to the big service's produced events.

Abstract: Autonomic Networking has long been a topic for standardization at the IETF. The first phase of standardization was mostly concerned with providing infrastructural foundations: Autonomic and secure enrollment of devices into a network domain and establishment of an autonomic control plane to support secure communication between autonomic service agents, on top of which actual autonomic functionality could subsequently be built. At the same time, Intent-Based Networking has been emerging as a new management paradigm, concerned with managing networks by defining outcomes without specifying how they might be achieved, which could in turn require autonomic functionality. In this position paper we outline ways in which Autonomic and Intent-Based Networking can complement one another and explore the role that interoperability and standards can play in this space. From this, we outline gaps and identify opportunities in current IETF standardization and offer our conclusions regarding which directions IETF standardization may take in the future.

Abstract: COPYRIGHT © 2023 Zhu and Zhong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Hybrid intelligent algorithms based learning, optimization, and application to autonomic control systems, volume II

Abstract: Autonomic computing is the most useful technology which is utilized in many engineering domains to automate operations and to reduce human intervention to avoid delay and human errors. The main issue is the unawareness of this technology to most of the research community. Many researchers are using this technique unknowingly and getting good results in their research. The objective of this study is to present the status of recent publications related to autonomic computing from 2019 to 2022. In this bibliometric article, the impact of autonomic computing in the research community has been focused. In this study, the contribution of researchers from different countries, and their collaborations with other co-authors. A keywords analysis has been also done which shows how autonomic computing is related to other technologies, and how it is now popular among researchers. However, in this analysis, the study about citations of the publications related to autonomic computing is also discussed, and moreover, how many articles have cited the recent publication is also presented.

Abstract: As data computing techniques continue to advance and change, distributed computing has become more and more mature and widely used, and it has become an important and effective method for computing data in today's era. Since human beings entered the information age, effective data processing has always been a topic of concern, in the face of complex and huge data, distributed computing has always played its own role, and gradually has a significant impact on other fields such as the Internet of Things, medical care, artificial intelligence and other fields, and has a positive effect on making today's human life more convenient and faster. Starting from the development background of distributed computing, starting from the three typical distributed frameworks of distributed computing, this paper not only introduces distributed computing as a data processing method, but also uses divergent thinking, describes different computing methods, and analyzes them together. In addition, this paper briefly points out the advantages and disadvantages of distributed computing, as well as the challenges and challenges that still exist in distributed computing and puts forward expectations for the future development of distributed computing.

Abstract: Autonomic computing is one of the emerging technology which provides some self-characteristics to manage the applications over cloud computing. Cloud computing platforms are being involved in many businesses to provide the business to business and business to customer services. This leads to the load over servers, and due to this, the automatic services monitoring and maintenance have become a big challenging issue over the internet. However, many researchers are focusing on this gap to overcome with their different research idea, but still, the need is to showcase the research gaps to the community for better understanding. Machine learning and optimization of Quality of Service QoS are the key parameters to automate the technology through self-learning activities. To automate the technological applications in industry demands, the use of a self-learning system is playing a vital role, and the knowledge of autonomic computing is required to produce to the research community. This paper is focused on randomized cloudlets management through autonomic computing characteristics and fault-tolerance technique (ARM-FT), and the experimental results found significant records to improve the systems based on resource utilization.

Abstract: Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability.

Abstract: In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with support to monitor and adapt itself to achieve specific concerns of interest. Communication in these systems is key given that in scenarios involving agent interaction, it enhances cooperation and reduces coordination challenges by enabling direct, clear information exchange. However, improving the expressiveness of the interaction communication with MASs is not without challenges. In this sense, the interplay between self-adaptive systems and effective communication is crucial for future MAS advancements. In this paper, we propose the integration of large language models (LLMs) such as GPT-based technologies into multiagent systems. We anchor our methodology on the MAPE-K model, which is renowned for its robust support in monitoring, analyzing, planning, and executing system adaptations in response to dynamic environments. We also present a practical illustration of the proposed approach, in which we implement and assess a basic MAS-based application. The approach significantly advances the state-of-the-art of self-adaptive systems by proposing a new paradigm for MAS self-adaptation of autonomous systems based on LLM capabilities.

Abstract: Future computing entities should be capable of accessing computing resources for data-intensive algorithm execution. This should be realizable in operational contexts where internet accessibility to cloud contexts becomes challenging. Such a scenario describes developing contexts. In addition, future computing entities also make use of multiple operating systems in a context where the computing resources are reduced due to the use of partitions. The use of partitions is recognized to reduce the number of accessible computing resources and increase the overhead associated with computing resource allocation. The presented research proposes an architecture where an operating system is logically stowed and selectively activated without involving the use of partition. This frees up the number of computing resources previously locked in different partition systems and reduces the computing resource overhead. Analysis shows that the proposed framework increases the accessible computing resources by 14.6% on average. In addition, the computing resource overhead is reduced by 21 % on average.

Abstract: Cyber threats, such as advanced persistent threats (APTs), ransomware, and zero-day exploits, are rapidly evolving and demand improved security measures. Honeypots and honeynets, as deceptive systems, offer valuable insights into attacker behavior, helping researchers and practitioners develop innovative defense strategies and enhance detection mechanisms. However, their deployment involves significant maintenance and overhead expenses. At the same time, the complexity of modern computing has prompted the rise of autonomic computing, aiming for systems that can operate without human intervention. Recent honeypot and honeynet research claims to incorporate autonomic computing principles, often using terms like adaptive, dynamic, intelligent, and learning. This study investigates such claims by measuring the extent to which autonomic principles principles are expressed in honeypot and honeynet literature. The findings reveal that autonomic computing keywords are present in the literature sample, suggesting an evolution from self-adaptation to autonomic computing implementations. Yet, despite these findings, the analysis also shows low frequencies of self-configuration, self-healing, and self-protection keywords. Interestingly, self-optimization appeared prominently in the literature. While this study presents a foundation for the convergence of autonomic computing and deceptive systems, future research could explore technical implementations in sample articles and test them for autonomic behavior. Additionally, investigations into the design and implementation of individual autonomic computing principles in honeypots and determining the necessary ratio of these principles for a system to exhibit autonomic behavior could provide valuable insights for both researchers and practitioners.