1. Kick-off Meeting – Feb 20 2014 Stênio Fernandes SLA4CLOUD: Measurement and SLA Management of Heterogeneous Cloud Infrastructures

2. UFPE TEAM R&D Experience

3. UFPE Team Stenio Fernandes Stenio Fernandes –Associate Professor - Center of Informatics (CIn) – Federal University of Pernambuco (UFPE) –Adjunct Professor - Electrical Engineering and Computer Science – University of Ottawa –Brazilian Research Chair (CNPq PQ-2) –IEEE Senior Member Marcelo Santos Marcelo Santos –PhD Student at CIn/UFPE

4. Recent R&D projects in the scope of SLA4Cloud AltoStratus: Middleware Solutions for Service Composition, Execution and Management in Hybrid and Heterogeneous Clouds - 2011-2013 AltoStratus: Middleware Solutions for Service Composition, Execution and Management in Hybrid and Heterogeneous Clouds - 2011-2013 ReVir: Virtual Networks in the Future Internet - (QoS Provisioning) – 2011-2013 ReVir: Virtual Networks in the Future Internet - (QoS Provisioning) – 2011-2013

5. Current International R&D Projects Canada-Brazil Canada-Brazil –Satellite AIS Data Mining for Intelligent Marine Solutions - 2013-2015 Europe-Brazil: FP7 Consortium Europe-Brazil: FP7 Consortium –IMPRESS - Intelligent System Development Platform for Intelligent and Sustainable Society - 2013 – 2015 Argentina-Brazil –High-Performance Computing, Computational Intelligence, and Visualization and Interaction for the Future Internet - 2013-2016

6. Current National R&D Projects Traffic Monitoring and Analysis of Dependability in Virtualized Networks (CNPq - 2013-2015) Traffic Monitoring and Analysis of Dependability in Virtualized Networks (CNPq - 2013-2015) A Business Decision Service Platform based on Passive Data Collecting and Mobility Analysis of Traceable Mobile Devices (FINEP/FAPESP - 2014- 2016) A Business Decision Service Platform based on Passive Data Collecting and Mobility Analysis of Traceable Mobile Devices (FINEP/FAPESP - 2014- 2016) Mobile Devices Tracking and Positioning in the Context of Smart Cities (CNPq - 2013-2015) Mobile Devices Tracking and Positioning in the Context of Smart Cities (CNPq - 2013-2015) Traffic Identification for Advanced Network Services (Ericsson Research / UFPE - 2012-2014) Traffic Identification for Advanced Network Services (Ericsson Research / UFPE - 2012-2014)

7. STUDENT PROFILE PhD Internship at Evry (Marcelo Santos)

8. 8 Student skills Tools/Platforms/OS: Tools/Platforms/OS: –PlanetLab, NS-2, Linux  Programming Languages: –C, Java, TCL/TK, C#, Flex/Action Script Cloud Computing Cloud Computing –Hypervisors: KVM, VMware, VirtualBox –OpenStack –LibVirt  R&D Projects: –Location and Allocation Mechanisms for Distributed Cloud Servers (LAMCloud) (2011-2013) –Self-Organized Clouds (SON-Cloud) (2013 - 2014)

9. 9 Student’s published papers 1. 1. CloudML: An Integrated Language for Resource, Service and Request Description for D-Clouds. IEEE Cloud 2011 2. 2. D-CRAS:Distributed Cloud Resource Allocation System IEEE NOMS 2012 3. 3. Dependability Assessment of Virtualized Networks IEEE ICC’12 4. 4. Self-organizing strategies for resource management in Cloud Computing. IEEE LatinCloud 2013 5. 5. Balancing the load across virtual links from virtual machine requests in Distributed Clouds. IFIP/IEEE IM 2013 6. 6. On the load balancing of virtual networks in Distributed Clouds. ACM SAC 2013 7. 7. Joint Allocation of Nodes and Links with Load Balancing in Network Virtualization. IEEE AINA 2014

10. BRIEF SUMMARY OF RESEARCH IN CLOUD COMPUTING UFPE Team Experience

11. AN INTEGRATED COMPOSITION MODEL FOR COLLABORATION IN THE CLOUD

12. Integrated Composition Model for Collaboration in the Cloud Composition Classes Composition Classes –Service Composition –Information Composition –Resource Composition –Network Composition –Cloud Composition

13. Service Composition Orchestration, Choreography Ink-Merger (Fusion) Orchestration, Choreography Ink-Merger (Fusion) “Ink-merger is a composition mechanism where similar services merge for providing a single distributed virtual service”

14. Ink-Merger Service Composition Ink-Merger A B S1 S2 S3 S4S5 S6 S S S Cloud #1 Cloud #2

15. PROFILING CORE OPERATIONS FOR ELASTICITY IN CLOUD ENVIRONMENTS

16. Summary Optimizations in the platforms barely eliminate the load of VMs Optimizations in the platforms barely eliminate the load of VMs Platforms in general provide acceptable response time for elasticity of core operations Platforms in general provide acceptable response time for elasticity of core operations –Careful infrastructure design is needed to overcome platform weaknesses Decision depends on technical and strategic criteria Decision depends on technical and strategic criteria –Xen  Less abstraction layers  Better performance, but demands more expertise –OpenNebula  More hardware-demanding (lower performance)  Presents higher response times –EC2  Few or no platform expertise  Satisfying response time

17. Current work Extend the evaluation to other operations Extend the evaluation to other operations –Migrate, deploy Evaluate and compare other platforms Evaluate and compare other platforms –Heterogeneous scenarios Evaluate elasticity/load balancing themselves Evaluate elasticity/load balancing themselves –Virtual machines replicating and tearing down automatically according to demand Evaluate cloud burst Evaluate cloud burst –Extending service provision to external cloud provider environments

19. Enhanced Cloud Integrator Architecture

20. WORKPACKAGES

21. WPs - UFPE Theme 3 - SLA-aware Service Composition in Cloud Environments: Federal University of Pernambuco (UFPE), Brazil research challenge – –to develop automated or semi-automated methods for identifying general SLA inconsistencies in the existing policies – –provide mechanisms to evaluate the set of services (individually or composed) in a workflow – –find the optimal set of deployments over hybrid clouds, while ensuring SLA requirements are fulfilled

22. Theme 5: Applications in Mobile Cloud Computing Demonstrator – –Coordinators: UFPE / UEVE To properly negotiate the SLA requirements, it is important to consider mobile users, once mobile application execution is highly context-dependent in which several performance metrics impact at runtime

23. Kick-off Meeting – Feb 20 2014 Stênio Fernandes SLA4CLOUD: Measurement and SLA Management of Heterogeneous Cloud Infrastructures