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RECAP Project Overview

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This a RECAP project overview slide deck prepared by Thang Le Duc (UMU), P-O Östberg (UMU) and Tomas Brännström (Tieto). It starts with an introduction and continues with a section on challenges for a self-orchestrated, self-remediated cloud system. It then presents the RECAP vision and use cases and finishes with a conclusion.

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RECAP Project Overview

  1. 1. Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications http://recap-project.eu recap2020 THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020 RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667 REliable CApacity Provisioning for Distributed Cloud/Edge/Fog Computing Applications (RECAP) Thang Le Duc @ Umeå University (thang@cs.umu.se) P-O Östberg @ Umeå University (p-o@cs.umu.se) Tomas Brännström @ Tieto (tomas.a.brannstrom@tieto.com)
  2. 2. Agenda • Introduction • Challenges for a Self-Orchestrated, Self-Remediated Cloud System • The RECAP Vision • Use Cases for RECAP System • Conclusion 2
  3. 3. Introduction 3
  4. 4. What is RECAP? • Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud/Edge/Fog Applications • Founded under European Horizon 2020 framework • Jan 2017 – Dec 2019 • Consortium comprises many academic and industrial partners ⁃ 9 partners from 5 countries (Germany, Ireland, Spain, Sweden, UK) • http://recap-project.eu 4
  5. 5. Partner Location 5
  6. 6. Project Motivation • Large-scale systems are typically built as distributed systems • Tradeoffs in the placement of application components: ⁃ Data center High latency, high power ⁃ Fog/Edge Low latency, low power • Cloud computing capacity is provisioned using best-effort models and coarsed- grained QoS mechanisms ⁃ Not a sustainable way as the number of connected ”things” increases 6
  7. 7. Realisation Approach (1/2) • An architecture for cloud-edge computing capacity provisioning and remediation ⁃ Fine-grained and accurate models of application behaviour and deployment ⁃ Model of QoS requirements at application component-level ⁃ Model of workloads • To understand and predict the behaviour of applications (users) • To enhance the proactive remediation of systems 7
  8. 8. Realisation Approach (2/2) 8
  9. 9. Challenges for a Self- Orchestrated, Self-Remediated Cloud System 9
  10. 10. Challenges • Resource Management • Data Science and Analytics • Intelligent Automation 10
  11. 11. Resource Management • Becomes more challenging due to the complexity and the large scale of cloud computing systems ⁃ Software-defined infrastructure: seamless distribution of components ⁃ Fully abstracted physical components ⁃ Virtualization technologies: high flexibility in resource management • Requires an understanding of diverse system factors ⁃ User behaviours ⁃ Workload characteristics and distribution ⁃ Interactions among components ⁃ Performance bottlenecks 11
  12. 12. Data Science and Analytics • Data Science and Analytics: collect, clean, integrate, analyze, visualize, and interact with data in order to construct structured knowledge bases • Understanding complex and large systems is challenging ⁃ Big data of diverse factors ⁃ Distributed and real-time orientied analytical models/techniques ⁃ Machine learning techniques 12
  13. 13. Intelligent Automation • Intelligent Automation: constructs intelligence in order to automatically and proactively provide orchestration and remediation ⁃ Automation in management ⁃ Optimization of the system ⁃ QoS • Challenges come from ⁃ The neglect of QoS of shared resource allocation schemes in cloud ⁃ The uncertainties related to QoS provisioning, automated management, and remediation in cloud systems 13
  14. 14. The RECAP Vision 14
  15. 15. Architecture • Feedback Loop ⁃ Collector ⁃ Application Modeler ⁃ Workload Modeler ⁃ Optimizer ⁃ Simulator 15
  16. 16. The RECAP Collector • Gathers, synthesizes and analyzes metrics to be monitored across the infrastructure • Acquires, characterizes, and analyzes data ⁃ Workload patterns and their relationship ⁃ Status of the infrastructure ⁃ … • Visualizes, annotates,archives and manages the collected data 16 Knowledge Discovery
  17. 17. The RECAP Application Modeler • With the knowledge provided by the Collector, the Modeler discovers and defines ⁃ The internal structure of cloud applications ⁃ The QoS requirements for each applications • To support intelligent decision making ⁃ Application/Component placement and autoscaling 17 Application Modeling
  18. 18. The RECAP Workload Modeler • Decomposes, classifies, and predicts the workloads in the network, and the load propagation in applications ⁃ CPU, memory, network traffic, … • Models the workload distribution and load propagation patterns • To improve planning decision and ensure QoS • To support the construction of an artificial workload generation tool ⁃ Validating and training the learning models 18 Workload Modeling
  19. 19. The RECAP Optimizer • With the models provided by the Modelers, the Optimizer performs optimization tasks ⁃ Application/Component placement: scaling vs. migration ⁃ Infrastructure management decisions: energy, utilization rate, load balancing • To improve the efficiency in resources utilzation while maintaining QoS 19 Optimization
  20. 20. The RECAP Simulator • Simulation is needed due to the size and complexity of the intended target systems ⁃ Simulateinteractions of distributed cloud application behaviors ⁃ Emulate data center and network systems • To assist the Optimizer with the evaluation of different deployments of applications and infrastructures • To feed back to the Data Collector with simulation results 20 Testing & Improvement
  21. 21. Use Cases 21
  22. 22. Use Cases from Industry • BT ⁃ NFV, QoS Management and Remediation • Linknovate ⁃ Complex Big Data Analytics Engine • Satec ⁃ Fog Computing and Large Scale IoT Scenario for supporting Smart Cities • Tieto ⁃ Infrastructure and Network Management 22
  23. 23. BT 23 • NFV infrastructure and virtual CDNs ⁃ Softwarization of network appliances ⁃ Mulitiple distributed applications per CDN operator • RECAP: ⁃ Automated decision making • Optimization of placement and scaling vCDN systems • Monitoring and Remediation ⁃ Improves resource utilization while guaranteeing SLAs
  24. 24. Linknovate • Big data analytics engine ⁃ Data acquisition, data aggregation, data processing, data visualization • RECAP: ⁃ Characterizes workload and models workload distribution ⁃ Automatically and dynamically allocates computing resources ⁃ To reduce costs and improve performance 24
  25. 25. Satec • IoT, smart city ⁃ Data management system: to collect sensory data, and to provide data (in a distributed manner) • RECAP: ⁃ Optimizes the placement of IoT resources such as computation, storage for cost and latency ⁃ Automated reallocation of resources ⁃ Automated deployment of IoT applications 25
  26. 26. Tieto 26 • Telecommunication infrastructure systems and applications • RECAP: ⁃ Automated profiling and simulating the infrastructure & VNFs ⁃ QoS and low latency
  27. 27. Conclusion 27
  28. 28. Expected Results / Summary • Distributed and Efficient Data Collection on Heterogeneous Infrastructures • Data Science Analysis for Automated Infrastructure and Application Modelling • Intelligent Automation by Automated Cloud Infrastructure Optimisation • End-to-end Component-level Quality Assurance by Capacity Provisioning • Application and Infrastructure Simulation for Cloud Optimisation • System Observability by Visualizing Data Collection and Modelling Results • Improved Resource Utilisation and User Satisfaction 28
  29. 29. THANK YOU http://recap-project.eu recap2020 RECAP Project ■ H2020 ■ Grant Agreement #732667 Call: H2020-ICT-2016-2017 ■Topic: ICT-06-2016 THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020 RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667 Q & A

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