Deliverable D2.2 State of the Art Report (I)
Total Page：16
File Type：pdf, Size：1020Kb
Ref. Ares(2020)6187138 - 31/10/2020 Adaptive edge/cloud compute and network continuum over a heterogeneous sparse edge infrastructure to support nextgen applications Deliverable D2.2 State of the art report (I) ACCORDION receives funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 871793 ACCORDION – G.A. 871793 DOCUMENT INFORMATION PROJECT PROJECT ACRONYM ACCORDION Adaptive edge/cloud compute and network continuum over a heterogeneous PROJECT FULL NAME sparse edge inFrastructure to support nextgen applications STARTING DATE 01/01/2020 (36 months) ENDING DATE 31/12/2022 PROJECT WEBSITE http://www.accordion-project.eu/ TOPIC ICT-15-2019-2020 Cloud Computing GRANT AGREEMENT N. 871793 COORDINATOR CNR DELIVERABLE INFORMATION WORKPACKAGE N. | TITLE WP2: Requirements & System Design WORKPACKAGE LEADER HUA WORKPACKAGE PARTICIPANTS All DELIVERABLE N. | TITLE D2.2: State of the art report (I) Ioannis Korontanis (HUA), Emanuele Carlini (CNR), Hanna Kavalionak (CNR), Patrizio Dazzi (CNR), Vangelis Psomakelis (ICCS), Lorenzo Blasi (HPE), Zinelaabidine Nadir (AALTO), John Violos (ICCS), Andrea Toro (HPE), Marco Russo (HPE), Eduard CONTRIBUTOR(S) Marin Fabregas (TID), George Vasios (HUA), Konstantinos Tserpes (HUA), Saman Zadtootaghaj (TUB), Bartlomiej Lipa (BSOFT), Maria Pateraki (OVR), Marco Di Girolamo (HPE) EDITOR(S) Lorenzo Blasi (HPE) REVIEWER(S) Maria Pateraki (OVR) CONTRACTUAL DELIVERY DATE 10/2020 ACTUAL DELIVERY DATE 30/10/2020 VERSION V1.0 TYPE Report DISSEMINATION LEVEL Public TOTAL N. PAGES 82 KEYWORDS Edge, resource pooling, orchestration, machine learning, models D2.2 State of the art report (I) Page 2 of 82 ACCORDION – G.A. 871793 EXECUTIVE SUMMARY The present document is the result of the collaborative effort of all ACCORDION partners participating to Task 2.2. The document offers a review of the state of the art for a series of topics strictly related to the work performed in the ACCORDION project. There is actually a strict correlation between the topics analyzed in this document and the Tasks that are part of the three research Work Packages of ACCORDION (WP3, WP4, and WP5). The main part of this document is section 2, in which all the state of the art analysis results have been reported. Section 2 has a subsection for each of the topics researched in the project, which includes: a description of the objectives, a list of outcomes expected from the research work, and an analysis of the state of the art. Section 2.1 (Resource monitoring & characterization) reports on monitoring, characterization and classification of Edge resources, identifying Prometheus, TOSCA and the automatic creation of taxonomies, respectively, as the best solutions for each of the three fields. Section 2.2 (Resource indexing & discovery) focuses on discussing solutions and data structures for organizing data in Resource Discovery Services. Section 2.3 (Edge storage, availability, reliability and performance) presents the advantages and disadvantages of both block and object storage, and then discusses some solutions, identifying OpenStack and MinIO as the most promising ones, even if not completely suitable. Some open research issues are also summarized. Section 2.4 (Pooling Edge resources), after listing some orchestration challenges typical of Edge computing and the techniques to cope with them, reports on several solutions to be considered as possible baselines for the ACCORDION Minicloud. Section 2.5 (ΑΙ-based network orchestration) first lists the main machine learning techniques, then explores both Federated Learning techniques and further evolutions such as Meta-Learning Framework and Multi-Agent Reinforcement Learning. Section 2.6 (Resilience policies & mechanisms over heterogeneous edge resources) starts by discriminating between reactive and proactive protection strategies and describing some of them. Then other Fault Tolerance approaches are explored both reported in the literature and adopted in common distributed computing frameworks (Openstack, Cloudstack, Kubernetes, Openshift, and Mesos). Finally techniques for movement behaviour and resource utilization prediction are analysed, with a particular focus on the the LSTM model for Neural Networks. The conclusion is that the most promising solution to efficiently adapt the deep learning topologies for the fault tolerance needs is the hyper parameter optimization approach. Section 2.7 (Techniques for secure Edge application development & deployment) offers an analysis of the most common types of security attacks (Distributed Denial-of-Service, Malware Injection, and Authentication-based attacks) and their related countermeasures, along with some threat modelling methods, while DevSecOps methods and tools are also described. Section 2.8 (Privacy preserving mechanisms) starts by analysing Machine Learning techniques with a focus on privacy preserving ones, and then lists a number of works analysing how cookie synchronization techniques adopted for web advertising can expose users to privacy leaks. D2.2 State of the art report (I) Page 3 of 82 ACCORDION – G.A. 871793 Section 2.9 (Application model for automatic deployment / migration of components) looks for application description models suitable for ACCORDION, i.e. with a machine-processable syntax, able to represent resource capacity requirements, containerization, and recovery policies. Three available solutions, TOSCA, Juju charms and CAMP, are compared along with the projects that are using them. Furthermore, tools supporting the three above solutions are described, and works researching the interoperability among the solutions are also analysed. Section 2.10 (Modelling and assessing QoE for NextGen applications) reports on different types of objective models that can be used to estimate the Quality of Experience (QoE) perceived by users of multimedia applications, and about the latest ITU-T Recommendations on QoE models and methodologies that can be applied to Next Generation Applications. For the ACCORDION project it has been decided to follow the standardized approach to build models for QoE assessment of ACCORDION applications. Section 2.11 (DevOps tools to automate Edge applications' deployment) sets the context and reports the starting points for the evaluation of Continuous Integration and Continuous Deployment tools. The identified state-of-the art solutions are Jenkins for the CI/CD pipeline and Kubernetes as the runtime deployment environment. Section 2.12 (Collaborative VR), starting from the general requirements for Virtual Reality applications, reports considerations about the still limited power of the available HMDs and discusses the trade-offs conditioning the possibility to offload computation from the end devices to the Edge. Finally Section 2.13 (Resource federation models) describes the main features of the federation model proposed by the H2020 5GeX project and lists the additional constraints and issues raised by an Edge providers' federation, which have to be further investigated. Not all project’s research Tasks have a related section in this document about their main topics, yet. Monitoring the State of the Art is an ongoing activity in ACCORDION, and the next version of this document foreseen in M22 will improve the State of the Art analysis by adding further details, covering more topics and reporting on possible new approaches that appeared in the meantime. D2.2 State of the art report (I) Page 4 of 82 ACCORDION – G.A. 871793 DISCLAIMER ACCORDION (871793) is a H2020 ICT project funded by the European Commission. ACCORDION establishes an opportunistic approach in bringing together edge resource/infrastructures (public clouds, on-premise infrastructures, telco resources, even end-devices) in pools defined in terms of latency, that can support NextGen application requirements. To mitigate the expectation that these pools will be “sparse”, providing low availability guarantees, ACCORDION will intelligently orchestrate the compute & network continuum formed between edge and public clouds, using the latter as a capacitor. Deployment decisions will be taken also based on privacy, security, cost, time and resource type criteria. This document contains information on ACCORDION core activities. Any reference to content in this document should clearly indicate the authors, source, organisation and publication date. The document has been produced with the funding of the European Commission. The content of this publication is the sole responsibility of the ACCORDION Consortium and its experts, and it cannot be considered to reflect the views of the European Commission. The authors of this document have taken any available measure in order for its content to be accurate, consistent and lawful. However, neither the project consortium as a whole nor the individual partners that implicitly or explicitly participated the creation and publication of this document hold any sort of responsibility that might occur as a result of using its content. The European Union (EU) was established in accordance with the Treaty on the European Union (Maastricht). There are currently 27 members states of the European Union. It is based on the European Communities and the member states’ cooperation in the fields of Common Foreign and Security Policy and Justice and Home Affairs. The five main institutions of the European Union are the European Parliament, the Council of Ministers, the European Commission, the Court of Justice, and the Court of Auditors (http://europa.eu.int/). Copyright