OPTIMIS – towards holistic cloud management

1. OPTIMIS – towards holistic cloud management 2011-09-20 Johan Tordsson, Department of Computing Science & HPC2N, UmeåUniversitet

2. OPTIMIS: background and motivation What? IP, Call 5, 10.4 M€ budget, 13 partners (8 academic) www.optimis-project.eu Why? Multiple cloud models, definitions, etc. Our view: Private clouds are common practice within the next few years Additional resources to handle load peaks etc. are provided by public cloud(s) No one-size-fits-all solution to cloud provisioning Need for common abstractions, tools, and methods for various scenarios

3. Roles & Challenges • New challenges • New customers • New business models • New collaboration forms • New requirements

4. Five concerns for future clouds Dependable sociability Management based on non-functional aspects Foundation for eco-system of providers and consumers of cloud services Many cloud architectures Private, bursted, federated clouds, etc. Service life cycle management optimization Construction, deployment, operation Adaptive self-preservation Self-* management with respect to functional and non-functional aspects Market and legal issues Identify business opportunities and legislative concerns

5. 1. Dependable sociability Beyond cost-performance tradeoffs Tools for measuring & prediction of TREC: Trust Reputation-assessment of actors (SPs, IPs, etc.) Transitivityaspects Risk Probability of something (bad) happening … … and the consequences Identification, assessment, monitoring, treatment Eco-efficiency Monitor and predict power, PUE, CO2, etc. Compliance to standards and legislations Cost Need for economical models beyond list prices Required to balance the above 3 factors

6. 2. Multi-Clouds: Three Basic Scenarios Bursted Private Clouds Service Provider InfrastructureProvider Private infra-structure Federated Clouds Service Provider InfrastructureProvider InfrastructureProvider InfrastructureProvider Service Provider Broker Multi-clouds InfrastructureProvider InfrastructureProvider InfrastructureProvider

7. 3. Service life cycle Cloud providers Eco-System • Self-management • Risk Evaluation • Eco-efficiency • Data Management Internal Cloud Operation Optimization Construction • Programming Model • Services Composition • [Legacy & New] • Risk Assessment • Trust Circle • Eco-efficiency Evaluation • Economic factor Deployment Optimization • Plus: • Multi-clouds • Federated clouds • License Management • Eco-efficiency Evaluation • Security External Cloud Operation Optimization

8. 4. Adaptive Self-preservation Clouds are complex and environments change rapidly Weneed Automaticself-* management of infrastructure self-configuration self-healing self-optimization Holisticview Cannot do management of services, VMs, data, etc. in isolation Self-management basedalso on non-functionalaspects Trust, risk, eco-efficiency, and cost Policy-driven management Adaptable and replacablepolicies

9. 5. Market and Legal issues • Cloud eco-system new and currently evolving • Opportunities for new roles, business models, relationships, value chains, etc. • Legal concerns • Acquisition, location, and transfer of data • Across borders and legal domains • Data protection and security mechanisms needed • (CS) Research problem • How to design mechanisms to be used to implement currently not known policies?

10. Our approach – the OPTIMIS Toolkit • Addresses the fivechallenges • Generictoolset to support multiple cloudarchitectures • Reusable and configurablecomponents • IncorporatesTREC-management and self-* abilities • Supports full service life cycle • Data protectioncapabilities

11. OPTIMIS System model • What is a service (in OPTIMIS)? • Anyfunctionalityoffered to clients over a network • Deliveredthroughone or moreVMs • Elastic • #VMschangedynamicallyduring operation • Defined by SP in a service manifest • VM images (OVF) • SLAsw.r.t. elasticity (service-specificKPIs) • Tresholds with acceptable levels of trust, risk, eco-efficiency, and cost • Deployed by SP in IP(s) • Operated by IP(s)

12. OPTIMIS Toolkitoverview • Four maingroups of components • Basic Toolkit • SP tools • IP tools • Tools usable by bothSPs and IPs

13. Basic Toolkit • Monitoring • Corefunctionality for self-managed systems • 3 levels • Services • Virtualinfrastructure • Physicalinfrastructure • Tools for measurement and prediction of • Trust • Risk • Cost • Eco-efficiency • Security • Identity management, etc. to handleinterconnection of clouds

14. SP Tools • Programmingmodel • Implement new service components • Integrateexistingones • IDE + runtime for workflow styleapplications • License management • Integratelicense-protected software in services • Challenges: • Elastic services • Migrating services

15. SP Tools (cont.) • Service Optimizer (SO) • Overall management of services • Trackingstate and deployment(s) • Performance monitoring • Re-deployment • Contextualizationmechanisms • Dynamicruntimesetup of VMs and services, with respect to networking etc. • Two step process: • Preparation • Attachboot-scripts in ISO image and couple this with VM image • Self-contextualization • Booting from ISO-image

16. IP Tools • Admission Control (AC) • Capacityplanning and safeoverbooking • Accept incoming service request or not? + Increased revenue - Added provisioning costs ? Implications for already hosted services • Services are elastic • Degree of elasticity differ • Time and duration of spikes differ • Similar problems • Network bandwidth multiplexing • Selling airline seats • Long-term capacityplanning (cf. scheduling)

17. IP Tools (Cont.) • VM Management • VM lifecycle management • Scheduling: optimal mapping of VMs • to physical hosts in an IP • across multiple clouds • Federation and bursting • When? • Admission of new service, upon elasticity, faults, periodically • Optimal? • SP perspective: • Performance (hosts, VMs), cost, guarantees, TREC, etc. • IP perspective: • Provisioning cost, consolidation, isolation, SLA violations, etc.

18. IP Tools (cont.) • FaultTolerance Engine • Automatic VM checkpointing and restart • Intervals configurable • Cloud Optimizer (CO) • Combinesmonitoring and prediction with IP-levelengines to performself-management • Overall decisionsrelated to local vs. bursted/federated VM placement etc. • Policy reconfiguration

19. Commontools for SPs and IPs • Service DeploymentOptimizer (SDO) • Coordinates service deployment process • Discovers and filters IPs, negotiatesSLAs, assessesTREC-factors, contextualizes services, uploads data, deploys services • Service deployment (SP to IP) • Private cloud + multi-cloud service deployment • VM placement (IP to IP) • Cloud bursting + federation • Data Management • Transfer of VM images and service data for deployment; SP to IP, and IP to IP • Managesdistributedfile system for service applications • Automaticre-location of service data acrossfederatedIPs

20. Common Tools (cont.) • SLA Management (CloudQoS) • Creation and monitoring of SLAs • WS-Agreement term extensions for TREC • Negotiation primitives (WS AgreementNegotiation) • Elasticity Engine • Feedback controller for automatic and proactive VM allocation to meetpeaks and lows in demand • Moreabout this one later…

21. Example: Service deployment (SP side)

22. Example: Service deployment (IP side)

23. Example: service operation (IP side)

24. OPTIMIS Toolkit deployment illustrations Bursted private clouds Federated clouds Private Cloud SP SP SP SDO SDO SDO SO SO SO IP IP IP IP IP IP IP AC SDO AC AC AC AC AC AC SDO CO SO CO CO CO CO CO CO SO Multi-clouds

25. FurtherFutureDirections • Usecases: • Programmingmodel • Service construction/composition • Examplesin ERP/CRM (SAP) and bio-informatics • Cloud bursting • Outsourcing based on TREC • Interoperation with OPTIMISand non-OPTIMISIps • E-Education test cases • Cloud brokering, a broker: • Acts as IP to SPs • Acts as SP to IPs • Is independent? • Provides value-addedservices? Service Provider Broker InfrastructureProvider InfrastructureProvider InfrastructureProvider

26. Current & Futuredirections (cont.) • OPTIMIS (the project) : June 2010 … May 2013 • Basic plumbing in place • Algorithmicalimprovementsnextfocus • TREC-awareself-* management policies • Holistic management, BLO-driven IP- and SP- operation • Experimentationneeded • Open for collaborations

27. Acknowledgments: the OPTIMIS consortium