CloudLightning Consortium Develops Heterogeneous Cloud Architecture
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This is a PowerPoint presentation delivered by Prof John Morrison (UCC) on 9 December 2016 at the IC4 and Host in Ireland Workshop: Data Centres in Ireland.
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CloudLightning Consortium Develops Heterogeneous Cloud Architecture
- 1. Prof. John Morrison (UCC)
- 3. Partners CloudLightning comprises of eight partners from academia and industry and is coordinated by University College Cork. Industrial partners: • Intel Ireland (IE) • Maxeler (UK) Academic partners: • University College Cork (IE) • Norwegian University of Science and Technology (NO) • Institute e-Austria Timisoara (RO) • Democritus University of Thrace (GR) • The Centre for Research & Technology, Hellas (GR) • Dublin City University (IE)
- 5. Specific Challenge CloudLightning was funded under Call H2020- ICT-2014-1 Advanced Cloud Infrastructures and Services. The aim is to develop infrastructures, methods and tools for high performance, adaptive cloud applications and Services that go beyond the current capabilities. • Cloud computing is being transformed by new requirements such as - heterogeneity of resources and devices - software-defined data centres - cloud networking,security,and - the rising demands for better quality of user experience. • Cloud computing research will be oriented towards - new computational and data management models (at both infrastructure and services levels) that respond to the advent of faster and more efficient machines, - rising heterogeneity of access modes and devices, - demand for low energy solutions, - widespread use ofbig data, - federated clouds and - secure multi-actor environments including public administrations.
- 6. EU Use Case Motivations CloudLightning’s use cases support the European Union HPC strategy and specific industries identified by IDC in their recent report on the progress of the EU HPC Strategy (IDC, 2015). 1 The health sector represents 10% of EU GDP and 8% of the EU workforce (EC, 2014).HPC is increasingly centralto genome processing and thus advanced medicine and bioscience research. 2 The oil and gas industry is responsible for 170,000 European jobs and €440 billion of Europe's GDP (IDC, 2015).HPC improves discovery performance and exploitation. 3 Ray tracing is a fundamental technology in many industries and specifically in CAD/CAE,digital content and mechanical design, sectors dominated by SMEs. 4 European ROI in HPC is very attractive - each euro invested in HPC on average returned€867 in increased revenue/income (IDC, 2015).
- 7. The HPC Market Although the EU has the largest GDP in the world (€13.2 trillion), the U.S. has substantially outspent the EU region in high performance computing which has a knock-on effect in scientific discovery, innovation and competitiveness. IDC estimate the HPC market at €21bn. IDC forecasts that European HPC ecosystem spending will increase by 37.8% (6.6% CAGR) to reach about €5.2 billion in 2018, or 24.9% of worldwide HPC ecosystem spending (€21.3 billion).
- 8. HPC Challenges “The challenge is less about educating users about cloud computing and more about the ability of clouds to handle more types of HPC jobs over time.” IDC, 2015 1 Hard to use without deep IT knowledge 2 Expensive 3 Inaccessible to individuals and SMEs Traditional High Performance Computing is… 4 Inflexible Most HPC workloads are not ready to run on today’s cloud architectures.
- 9. The Market for HPC in the Cloud Cloud segment is the one of the smallest but fastest growing segments in the HPC market. Spending on HPC in the cloud and Hybrid-custom HPC clouds is forecast to grow from US$1.7bn in 2015 to US$5.2bn in 2017 (IDC, 2015). The proportion of HPC sites employing cloud computing has grown from 13.8% in 2011, to 23.5% in 2013,to 34.1% in 2015 (IDC, 2015). CloudLightning primary researchsuggests 48% of sites are using cloud computing although for relatively less complex workloads. $1.5 billion $3.7 billion $15.4 billion Hybrid-Custom HPC Clouds (2017) HPC Public Clouds (2017) Traditional HPC Servers and Private Clouds (2017)
- 10. Drivers and Barriers to HPC in the Cloud Adoption Our primary research (n=92) confirms our desk research which suggests that there are significant economic and capacity- related drivers but both general cloud and HPC- specific barriers to HPC in the cloud adoption. 1 Access to extra capacity for overflow or surge workloads 2 Reduced capital costs 3 Access to a datacentre or specialised software Drivers 1 Data protection and control 2 3 Complexity and difficulties migrating and integrating existing systems with the Cloud Barriers Communication speed concerns
- 11. CloudLightning Objectives CloudLightning seeks to address the challenges in the HPC market through 9 technical, commercial and societal objectives. Build Prototype Management System and Delivery Model (WP4, WP5, WP6) Competitive Advantage through Infrastructure Efficiencies (WP4, WP8) Energy Efficiency (WP3, WP7) Validate Approach with Use Cases (WP5, WP6) Competitive Advantage through Improved Accessibility (WP5, WP6, WP8) Improved Accessibility to Cloud Resources (WP2, WP5, WP6) Demonstrate Scalability (WP7) Opportunities in Use Case Domains (WP2, WP8) Scientific Advancement (WP8) Technical Objectives CommercialObjectives Societal Objectives
- 12. CloudLightning Approach CloudLightning proposes a novel architecture for provisioning heterogeneous cloud resources to deliver services, specified by the user, using a bespoke service description language. 01 Complexity CloudLightning uses self- organisation and self- management to manage complexity effectively. 02 Heterogeneous Resources CloudLightning was specifically for heterogeneous hardware 03 IaaS Access 04 Energy Efficiency 05 Resource Utilisation CloudLightning uses dynamic workload and resource management to increase the efficiency of resource utilisation. 06 Service Deployment The CloudLightning deployment mechanism simplifies the operational overhead for non- technical users Achieved through heterogeneous resources, reducing overprovisioning, maximising VM/server density and turning off idle servers Clear service interface through separation of concerns between consumer and provider.
- 13. Gateway Service Self Organizing Self Management System Plug & Play Service Blueprint Creator End User Services Catalogue Blueprint Catalogue Enterprise Cloud Operator Gateway Service UI Heterogeneous Resources New Hardware Deploy Service Service User Perspective Monitor Request to join CL-Resource Discover Resource Extract / Modify Blueprints Request Resource CL-Resources Deploy Blueprint Running Service Extract Blueprint Get Services Create Blueprints Get Status Resource Handler
- 14. Progress Beyond the State of the Art CloudLightning is, and will, contribute to progress beyond the state of the art across all technical work packages and primary use cases. We are, and will, contribute to: 1. The expected impacts listed in the call topic 2. The innovative capacity of the consortium members 3. The innovative capacity of European industry 4. Other European environmental and societal priorities Cloud Architecture Service Description Languages Local Decision Strategy Framework Resource Coalitions Ray Tracing Oil & Gas Genome Processing Large Scale Simulation 1 5 37 2 6 4 8
- 15. JOHN MORRISON | j.morrison@cs.ucc.ie THANK YOU
- 17. Design Requirements Create a Heterogeneous Service-Oriented Cloud Architecture to Support HPC Workloads 1 2 3 4 Ease of Use Improve Resource Utilization compared to current Cloud deployments Support Heterogeneity Improve Service Delivery
- 18. Blueprints, Service Catalogue and Implementation Library Self Organizing Self Management Framework Blueprint Physical Resources Services Catalogue Blueprint Creator End User • A Blueprint is a composition of services. • A service describes the features of many different hardware types and executable code for the same task. • An implementation is an executable code on a hardware type of a task. Gateway Service Blueprint Catalogue Plug & Play Service Coalition Coalition Coalition Deployed Blueprint Blueprint Catalogue Enterprise Cloud Operator Gateway Service
- 19. Service 1 Service Catalogue Service 2 Service 3 Implementation Library Implementation 1 Implementation 2 Implementation 3 id: unique identifier definition: concrete SW/HW (...) Implementation id: unique identifier definition: service specification constraints: logical expressions metrics: atomic values parameters: atomic values Service id: unique identifier constraints: logical expressions metrics: atomic values parameters: atomic values Blueprint No implementation Blueprint 1 Blueprint Catalogue Blueprint 2 Blueprint 3 Composition of services Blueprints, Service Catalogue and Implementation Library • A Blueprint is a composition of services. • A service describes the features of many different hardware types and executable code for the same task. • An implementation is an executable code on a hardware type of a task.
- 20. CloudLightning API Flow The main CL system components,APIs, communication protocols and a sequence of documents that maintains the state of each,and every, interaction has been defined.
- 22. CloudLightning Protocol Specification Default request content- types: application/json Default response content- types: application/json Schemes: http, https
- 23. Gateway Service Self Organizing Self Management Framework Blueprint Physical Resources Services Catalogue Blueprint Catalogue Coalition Coalition Coalition Deployed Blueprint Coalition Coalition Coalition Deployed Blueprint Plug & Play Service • Use service characteristics to determine best implementation hardware type. • Locate resources of the appropriate type. • Return resource handlers to the Gateway via the Blueprint. • Invoke the deployment mechanism. Creating a Resourced Blueprint
- 24. We assume a Cloud with a Resource Fabric far greater than that currently available. Adding structure to the Cloud Fabric by creating virtual partitions and grouping them together. Management of physical resources • The resource fabric is partitioned into vRacks. • Each vRack is managed by a vRack Manager. • A vRack Manager can form Coalitions of its resources to support services. • vRack Managers self organize to optimize service delivery Heterogeneous Physical Resources
- 25. • A vRack is a homogeneous partition of the resource fabric. • Each vRack is managed by a dedicated vRack Manager. • vRack Managers of different types exist based on the resource types being managed. vRacks and vRack Managers Svr Svr Svr Svr Svr Svr Svr Svr Svr Resources Fabric vRack vRackvRack vRack vRack vRack Manager Specialized HW Specialized HW vRack vRack Svr Svr Svr Svr vRack Manager Dedicated High-speed Interconnection Svr Svr vRack vRack Manager
- 26. • Groups of vRack Managers can be formed to simplify access to resources and to enable self- organization • There are three types of vRack Manager Groups. vRack Manager Groups vRack Manager Specialized HW Specialized HW vRack vRack Manager Specialized HW Specialized HW vRack vRack Svr Svr Svr Svr vRack Manager Dedicated High-speed Interconnection vRack Svr Svr Svr Svr vRack Manager Dedicated High-speed Interconnection Type A Type B Type C Svr Svr vRack vRack Manager Svr Svr vRack vRack Manager
- 27. To generically manipulate resources of different types, the SOSM system introduces the conceptof a CL-Resource. CL-Resources refer to different hardware types and to different configurations ofthose type. Thus heterogeneity can be introduced dynamically. CL-Resources Local Resource Manager Svr MIC Svr Svr Svr MIC MIC MIC MIC-World MIC Cluster of Servers Container/VM Resource Partitioning Posibilities
- 28. Advanced architecture support • Dynamic VPN creation for Blueprint Service Execution • Autoscaling • High availability • Data locality Blueprint S1 S3 S2 vRack Server Server Server Server vRack Server Server Server Server Virtual Network Connection
- 29. Gateway Service Self Organizing Self Management Framework Blueprint Services Catalogue Blueprint Catalogue Coalition Coalition Coalition Deployed Blueprint Plug & Play Service • Use service characteristics to determine best implementation hardware type. • Locate resources of the appropriate type. • Return resource handlers to the Gateway via the Blueprint. • Invoke the deployment mechanism. Creating a Resourced Blueprint Physical Resources
- 30. A Framework for Hosting and Executing SOSM Strategies A framework for hosting and executing SOSM strategies associated with any hierarchical architecture to achieve their local goals, eventually the whole system evolves to the ideal global goal state. Perception Metrics Assessment Functions Impetus Weights Suitability Index Directed Evolution
- 31. Architecture showing the components and their relationships. The conceptual architecture
- 32. Augmented CloudLightning Architecture The CL architecture is expressed as a hierarchical architecture, introducing pRouters and pSwitches pSwitch pSwitch pSwitch
- 33. Customizing the self-organisation self-management framework with CL strategies The Assessment Functions and Directed Evolution are related to the CL specific objectives of: • Maximizing task throughput • Maximizing energy efficiency • Maximizing computational efficiency • Maximizing resource management efficiency Metrics Weights Perception Impetus Suitability Index Local goal: maximize its Suitability Index
- 34. Visualisation of Self-organisation self-management framework
- 35. Self-organisation framework augmentations in support of virtualization Goals: • Support for virtualization • Increase resource utilization • Decrease job rejection rate Add new assessment function reflecting Memory consumption Two-stage self-organisation strategy introduced: CPU and vCPU Resource over-commitment is addressed
- 36. • Coalitions are used to supportthe process parallelism within a service. • Coalitions existentirely inside a vRack. • The CL-Resources ofa Coalition may span multiple servers within the same vRack. WP 3 Coalitions Server Server Server Server Server Server vRack
- 37. Coalition Formation Strategies Task Compaction Isotropy Preservation Dependency Minimization Machine-based coalition formation strategies: • Task Compaction • Isotropy Preservation • Dependency Minimization
- 38. Coalition Formation Strategies Coalition Size Frequency Workload Execution Constraints Workload-based coalition formation strategies: • Coalition Size Frequency • Workload Execution Constraints
- 39. The Telemetry system provides updates to the SOSM system on the status of resources fabric. It is implemented by using InfluxDB and SNAP. Determining the local state Gateway Service Self Organizing Self Management Framework Blueprint Services Catalogue Blueprint Catalogue Plug & Play Service Coalition Coalition Coalition Deployed Blueprint Blueprint Creator End User Plug & Play Service Self Organizing Self Management Framework Physical ResourcesPhysical Resources Enterprise Cloud Operator
- 40. • The SOSM system supports the addition of new hardware by using a plug and play mechanism. • New hardware can register with SOSM and it is automatically added and managed. Support for new hardware Gateway Service Self Organizing Self Management Framework Blueprint Physical Resources Services Catalogue Blueprint Catalogue Plug & Play Service Coalition Coalition Coalition Deployed Blueprint Blueprint Creator End User Self Organizing Self Management Framework Physical Resources Enterprise Cloud Operator
- 41. SOSM Framework Cell Manager Physical Resources Resource Abstraction Layer Plug & Play Service vRackManager Self Organizing Self Management System vRackManager vRackManager New HW • The SOSM system supports the addition of new hardware by using a plug and play mechanism. • New hardware can register with SOSM and it is automatically added and managed. Support for new hardware