OpenIaC: open infrastructure as code - the network is my computer

Modern information systems are built fron a complex composition of networks, infrastructure, devices, services, and applications, interconnected by data flows that are often private and financially sensitive. The 5G networks, which can create hyperlocalized services, have highlighted many of the deficiencies of current practices in use today to create and operate information systems. Emerging cloud computing techniques, such as Infrastructure-as-Code (IaC) and elastic computing, offer a path for a future re-imagining of how we create, deploy, secure, operate, and retire information systems. In this paper, we articulate the position that a comprehensive new approach is needed for all OSI layers from layer 2 up to applications that are built on underlying principles that include reproducibility, continuous integration/continuous delivery, auditability, and versioning. There are obvious needs to redesign and optimize the protocols from the network layer to the application layer. Our vision seeks to augment existing Cloud Computing and Networking solutions with support for multiple cloud infrastructures and seamless integration of cloud-based microservices. To address these issues, we propose an approach named Open Infrastructure as Code (OpenIaC), which is an attempt to provide a common open forum to integrate and build on advances in cloud computing and blockchain to address the needs of modern information architectures. The main mission of our OpenIaC approach is to provide services based on the principles of Zero Trust Architecture (ZTA) among the federation of connected resources based on Decentralized Identity (DID). Our objectives include the creation of an open-source hub with fine-grained access control for an open and connected infrastructure of shared resources (sensing, storage, computing, 3D printing, etc.) managed by blockchains and federations. Our proposed approach has the potential to provide a path for developing new platforms, business models, and a modernized information ecosystem necessary for 5G networks.publishedVersio

Cloud native computing for Industry 4.0: Challenges and opportunities

Proceedings of: 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), 7-10 Sept. 2021, Vasteras, Sweden.Cloud-based architectures are advantageous in aspects such as scalability, reliability and resource utilization efficiency, to name just a few, thus being considered one of the pillars of Industry 4.0. However, in this domain, cloud computing platforms are subject to specific requirements, namely in what concerns real-time performance, determinism and fault-tolerance. This paper focuses on cloud native computing, which is an emerging and promising cloud-computing paradigm, specifically addressing its applicability to real-time systems. Firstly, it introduces the architecture of cloud native applications, discussing their principles, potential advantages and challenges. Then it addresses the opportunities and constraints of such technologies when applied to industrial real-time systems.This work has been supported by EC H2020 5GPPP 5Growth project (Grant 856709)

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

Применение облачных технологий при построении систем ситуационного управления

Розглядаються питання побудови та організації багатопрофільних систем ситуаційного управління, побудованих на принципах хмарних обчислень. Аналізуються особливості створення та ефективного використання таких систем. Запропоновано класифікацію корпоративних додатків з точки зору їх функціональності.The issues of multipurpose situation management systems design and organization with using of cloud computing principles are discussed in the paper. The features of development and effective exploitation such systems were analyzed. And the classification of corporate applications from functional point of view was proposed.В статье рассматриваются вопросы построения и организации многопрофильных систем ситуационного управления, построенных на принципах облачных вычислений. Анализируются особенности создания и эффективного использования таких систем. Предложена классификация корпоративных приложений с точки зрения их функциональности

Towards adaptive actors for scalable iot applications at the edge

Traditional device-cloud architectures are not scalable to the size of future IoT deployments. While edge and fog-computing principles seem like a tangible solution, they increase the programming effort of IoT systems, do not provide the same elasticity guarantees as the cloud and are of much greater hardware heterogeneity. Future IoT applications will be highly distributed and place their computational tasks on any combination of end-devices (sensor nodes, smartphones, drones), edge and cloud resources in order to achieve their application goals. These complex distributed systems require a programming model that allows developers to implement their applications in a simple way (i.e., focus on the application logic) and an execution framework that runs these applications resiliently with a high resource efficiency, while maximizing application utility. Towards such distributed execution runtime, we propose Nandu, an actor based system that adapts and migrates tasks dynamically using developer provided hints as seed information. Nandu allows developers to focus on sequential application logic and transforms their application into distributed, adaptive actors. The resulting actors support fine-grained entry points for the execution environment. These entry points allow local schedulers to adapt actors seamlessly to the current context, while optimizing the overall application utility according to developer provided requirements

Agreement technologies and their use in cloud computing environments

The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-012-0031-9[EN] Nowadays, cloud computing is revolutionizing the services provided through the Internet to adapt itself in order to keep the quality of its services. Recent research foresees the advent of a new discipline of agent-based cloud computing systems that can make decisions about adaption in an uncertain environment. This paper discusses the role of argumentation in the next generation of agreement technologies and its use in cloud computing environments.This work is supported by the Spanish government (MICINN), project reference: TIN2012-36586-C03-01.Heras Barberá, SM.; De La Piedra, F.; Julian Inglada, VJ.; Rodríguez, S.; Botti Navarro, VJ.; Bajo, J.; Corchado, JM. (2012). Agreement technologies and their use in cloud computing environments. Progress in Artificial Intelligence. 1(4):277-290. https://doi.org/10.1007/s13748-012-0031-9S27729014European Comission: The Future of Cloud Computing. Technical report (2010)Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: SOSP03 Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 164–177. ACM, New York (2003)Wang, L., et. al.: Scientific cloud computing: early definition and experience. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), pp. 825–830. IEEE Press (2008)Talia, D.: Clouds meet agents: toward intelligent cloud services. Internet Comput. IEEE 16(2), 78–81 (2012). doi: 10.1109/MIC.2012.28Heras, S.: Case-Based Argumentation Framework for Agent Societies. PhD thesis, Universitat Politècnica de València. http://hdl.handle.net/10251/12497 (2011)Ashton, K.: That ‘internet of things’ thing. RFID J. (2009). http://www.rfidjournal.com/article/view/4986Klusch, M.: Information agent technology for the Internet: a Survey. Data Knowl. Eng. 36, 337–372 (2001)Schaffer, H.E.: X as a Service. Cloud Computing, and the Need for Good Judgment IT Professional 11(5), 4–5 (2009). doi: 10.1109/MITP.2009.112Richardson, L., Ruby, S.: RESTful Web Services, Web services for the real world O’Reilly, Media, May, p. 454 (2007)GlusterFS Developers. The Gluster web site. http://www.gluster.org (2012)Chodorow, K., Dirolf, M.: The Definitive Guide. O’Reilly Media, MongoDB (2010)Fuentes-Fernandez, R., Hassan, S., Pavon, J., Galan, J.M., Lopez-Paredes, A.: Metamodels for role-driven agent-based modelling. Comput. Math. Organ. Theory 18(1), 91–112 (2012)Jordán, J., et al.: A customer support application using argumentation in multi-agent systems. In: 14th International Conference on, Information Fusion, pp. 772–778 (2011)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. Int. J. Approx. Reasoning (2012, in press)Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)Bench-Capon, T., Sartor, G.: A model of legal reasoning with cases incorporating theories and values. Artif. Intell. 150(1–2), 97–143 (2003)Dignum, F., Weigand, H.: Communication and deontic logic. In: Information Systems Correctness and Reusability, pp. 242–260. World Scientific, Singapore (1995)Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)Lopez-Rodriguez, I., Hernandez-Tejera, M.: Software agents as cloud computing services. In: 9th International Conference on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol. 88, pp. 271–276. Springer, Berlin (2011)Sim, K.M.: Towards complex negotiation for cloud economy. In: 5th International Conference on Advances in Grid and Pervasive Computing. LNCS, vol. 6104, pp. 395–406. Springer, Berlin (2010)Aversa, R., et al.: Cloud agency: a mobile agent based cloud system. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 132–137. IEEE Computer Society Press, Washington, DC (2010)Cao, B., et al.: A service-oriented qos-assured and multi-agent cloud computing architecture. In: 1st International Conference on Cloud Computing. LNCS, vol. 5931, pp. 644–649. Springer, Berlin (2009)Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Berlin (2009

Performance Evaluation of Serverless Applications and Infrastructures

Context. Cloud computing has become the de facto standard for deploying modern web-based software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new serverless services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Measuring these characteristics is difficult in dynamic cloud environments due to performance variability in large-scale distributed systems with limited observability.Objective. This thesis aims to enable reproducible performance evaluation of serverless applications and their underlying cloud infrastructure.Method. A combination of literature review and empirical research established a consolidated view on serverless applications and their performance. New solutions were developed through engineering research and used to conduct performance benchmarking field experiments in cloud environments.Findings. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that most studies do not follow reproducibility principles on cloud experimentation. Characterizing 89 serverless applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. A novel trace-based serverless application benchmark shows that external service calls often dominate the median end-to-end latency and cause long tail latency. The latency breakdown analysis further identifies performance challenges of serverless applications, such as long delays through asynchronous function triggers, substantial runtime initialization for coldstarts, increased performance variability under bursty workloads, and heavily provider-dependent performance characteristics. The evaluation of different cloud benchmarking methodologies has shown that only selected micro-benchmarks are suitable for estimating application performance, performance variability depends on the resource type, and batch testing on the same instance with repetitions should be used for reliable performance testing.Conclusions. The insights of this thesis can guide practitioners in building performance-optimized serverless applications and researchers in reproducibly evaluating cloud performance using suitable execution methodologies and different benchmark types

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. 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