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From IoT Devices to Cloud

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Talk at the Entretiens Jacques Cartier, Montréal, Oct. 18 2017

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From IoT Devices to Cloud

  1. 1. From IoT Devices to Cloud Computing Infrastructures When (bi)millions small entities should work with a few giants F. Desprez, INRIA Entretiens Jacques Cartier - Montréal October 2017
  2. 2. Introduction • Exponential improvement of – Electronics (energy consumption, size, cost) – Capacity of networks (WAN, wireless) • Prediction between 28 and 50 billions of connected devices by 2020 (Ericsson, CISCO) • Exponential growth of applications near users – Smartphones, tablets, connected devices, sensors, … • Large number of Cloud facilities to cope with generated data – Many platforms and infrastructures available around the world – Several offers for IaaS, PaaS, and SaaS platforms – Public, private, community, and hybrid clouds – Going toward distributed Clouds (FOG, Edge) Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 2
  3. 3. Entretiens Jacques Cartier - Oct. 2017 F. Desprez - From IoT devices to Cloud Computing Infrastructures - 3http://www.beechamresearch.com/article.aspx?id=4
  4. 4. Target Applications: Industrial Internet • Integration of complex physical machinery with networked sensors and software • Application examples – Self-driving cars, smart’* (health, cities, transportation, power grid, retail store, …) • Ingest data from machines, analyze it (often in real-time), and use it to adjust operations • Several fields need to collaborate – Internet of Things, Big Data, machine-to-machine communications, machine learning, Cyber-physical systems, … Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 4
  5. 5. Industrial Internet, contd Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 5 Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT, Sheng.Z., Mahapatra, C., Zhu, C., Leung, V.C.M., A., Kansakar, P., U.Kahn, S., IEEE, Jun. 2015.
  6. 6. Citylabs project @ Inria Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 6 • Privacy-aware Urban-scale Physical and Social Sensing (FUN, MiMove, SMIS, AGORA) • Energy-efficient wireless communication, Leveraging the IoT • Physical &/vs social sensing, Fixed &/vs mobile sensing • Ultra large scale & heterogeneous urban systems • Incentives & privacy for citizens • From Sensing to Modeling Cities (CLIME, DICE, MYRIADS, OAK, WILLOW) • Cloud-based management of semantic urban data • Data assimilation combining simulation models & available data to overcome uncertainties • Urban-scale quantitative visual analysis to leverage the visual records of urban environment • Next Generation City Services promoting citizen engagement (CLIME, MiMove, SMIS, WILLOW) • AppCivist Social App • City planning • Democratizing environmental data • Smart transportation systems • Overcoming the Smart City Challenge • Teams involved: AGORA, CLIME, DICE, FUN, MYRIADS, MIMOVE SMIS, WILLOW https://citylab.inria.fr/
  7. 7. Target Applications: Tactile Internet • Ability to deliver physical experiences remotely • The complete loop from the physical world, to the digital and back to the physical Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 7 http://www.zeitgeistlab.ca/doc/tactile_internet.html
  8. 8. Target Application: Disaster Resilience • Keep computing and network services running after a natural disaster or attack • Geographic redundancy of the components (over “small” devices?) • Network (re)-configuration, path restoration and protection • Backup VM for each working VM • Modeling the risk! Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 8 Network design requirements for disaster resilience in IaaS clouds, R. de Souza Couto, S. Secci, M. E. Mitre Campista, and L. H. Maciel Kosmalski Costa, IEEE Communications Magazine • October 2014
  9. 9. Needs and Performance Constraints • Performances – Big latency issues • Voice: 100 ms (upper latency limit for humans) • Video : 10 ms • Tactile internet : 1 ms – Bandwidth (upstream traffic mainly) – Real-time constraints – Scalability Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 9 • Other constraints – Security – Privacy – Availability – Durability control
  10. 10. Entretiens Jacques Cartier - Oct. 2017 F. Desprez - From IoT devices to Cloud Computing Infrastructures - 10 John Mc Carthy, Speaking at the MIT centennial in 1961 If computers of the kind I have advocated become the computers of the future, then computing may someday be organized as a public utility just as the telephone system is a public utility...
  11. 11. Current Situation Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 11 • Large off shore DCs to cope with the increasing UC demand while handling energy concerns • But • Jurisdiction concerns (data locality), PRISM NSA scandal, Patriot Act • Reliability (disaster recovery), single point of failure • Network overhead • Localization is a key element to deliver efficient as well as sustainable Utility Computing solutions
  12. 12. Cloud Evolution Not only mega data centres ! Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 12 Courtesy to Thierry Coupaye (Orange)
  13. 13. Trends for Next Generation Clouds Centralized public clouds are in fact generally distributed over multiple (mega) data centres for availability reasons Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 13 Verizon (©) Orange (©)Microsoft (©) Amazon (©) Courtesy to Thierry Coupaye (Orange)
  14. 14. Trends for Next Generation Clouds • Hybrid and community clouds are by nature distributed over multiple data centres/clouds Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 14 Courtesy to Thierry Coupaye (Orange)
  15. 15. Convergence Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 15 ENIAC 1946 Transistor 1947 Computation Communication 1999 - Salesforces SaaS Concept micro processor 1971 1838 - Telegraph 1876 - Telephone 1896 - Radio 1957 - satellite 1969 - ARPANET 1973 - Ethernet 1985 - TCP/IP Adoption 1975 -Personal Computers SmartPhones 2007 2002- Amazon Initial Compute/Storage services 2006 - Amazon EC2 (IaaS) 2010 - Cloud democratisation 2015 Network/Computers Convergence Software Defined XXX 1999 - The Grid 1995 - Commodity clusters 2002 - Virtualised Infrastructure 1950/1990 - Mainframes 1950 - Batchmode 1960 - Interactive 1970 - Terminals (clients/server concepts) 1967 - First virtualisation attempt
  16. 16. Clouds, FOG, and Edge • From a Cloud model (centralized mega data-centers) to a set of micro/nano datacenters • Locality based utility computing infrastructures – Provide resources closer to the users • Leverage network backbones – Extend any point of presence of network backbones (aka PoP) with servers • Extend to the edge by including wireless backbones • Where should these micro-DC be deployed ? • Energy and cost issues • In the core network (POPs) Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 16 P a ul a B o b Al ic e D u k e Ch arle s P a m B o b core backbone
  17. 17. Clouds, FOG, and Edge • Cloud – (Quite) centralized, big data centers, large resources, WAN – Location depending on energy/taxes issues • FOG – First coined by CISCO – OpenFog consortium in 2015 (ARM, Cisco, Dell, Intel, Microsoft, and Princetown) – Geographically distributed computing architecture – Resource pool of ubiquitously connected heterogeneous devices at the edge of the network • Edge – Mobile Edge Computing (MEC) – Edge of the cellular network • Both Fog and Edge platforms push applications, data, and services away from centralized nodes Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 17 IFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for Future Internet of Things Munir, A., Kansakar, P., U.Kahn, S., arXiv, Jan. 2017.
  18. 18. Cloud-IoT Convergence • IoT is here (and growing) • Large Datacenters still efficient for large computations/data management • Micro/nano DCs to handle some computations closer to the users • How should they be managed ? Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 18 Stability Availability Latency Low latency Heterogeneity Low capacity
  19. 19. Research Issues • Resource management – Deployment, reconfiguration, location aware scheduling • Data management – User data, checkpoints, application images • Network operation – Virtualization • Energy monitoring and consumption optimization – Measures, resource management, multi-criteria, multiple sources, … • Resilience – Coping with failures (CPU, application, network, …) and attacks • Security • … Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 19 A Survey of Fog Computing: Concepts, Applications, and Issues? Yi, S., Li, C., Li, Q, Mobidata 2015, June. 2015.
  20. 20. Deployment and Reconfiguration • Provisioning resources where they are needed – Provisioning comes with a cost – Limited capacity (≠ mega data-center) • Zero-touch provisioning and reconfiguration – Being able to deploy/reconfigure an edge site without human interventions – Data and computation – Real-time elasticity • Resource discovery • Application image management • Heterogeneous (and dynamic platforms) • Network issues (SDN, NFV) Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 20
  21. 21. Locality Aware Resource Management • Mechanisms to manage the life cycle of applications (VM, containers, bare metal) and data (users, applications) taking locality into account • Several objective functions (multi-criteria scheduling) – Resource consumption – Network cost – Energy – $ • Classical scheduling/mapping problems revisited – Many papers using classical ILP solvers (scaling issues there !) • Placement of application graphs over infrastructure graphs – Static or dynamic • What’s about dynamicity ? – Clients moving from one place to an other – Failures Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 21
  22. 22. Locality Aware Resource Management • Problem of placing application graphs, which represent application components and the communication among these components, onto a physical graph, which represents the computing devices and communication links in the physical system – Tree topologies • Baseline algorithm that provides an optimal solution to the placement of a linear application graph (decomposable into multiple small building blocks) • Simplification of the problem to make it tractable, NP-harness proof • Off-line algorithm Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 22 Online Placement of Multi-Component Applications in Edge Computing Environments, Wang, S., Zafer, M., Leung, K.K., Mobidata 2015, June. 2015, doi: 10.1109/ACCESS.2017.2665971.
  23. 23. Energy Monitoring and Consumption Optimization • Energy can be considered as the first metric for placement strategies – i.e. relocate jobs/data according to the energy sources • Preemptive jobs – i.e. we can think about batch approaches and schedule them on the right edge DC at the right moment • Multi-criteria resource management • Taking care of new energy sources (solar, wind, …) • QoS for applications, resource consumption, energy cost • Several issues – Instrument realistic infrastructures, – measure accurately consumption of resources, – design the right models, – isolate influential factors, – combine energy models with performance models, – propose models integrating inherent variability, – perform campaign measurements, – achieve invalidation studies Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 23
  24. 24. Renewable Energy and IoT • Problem: How to decide to compute at the edge or offload at the edge depending on QoS and energy-efficiency for a given IoT application? – Performance/energy tradeoff • Modeling application for its energy consumption and its response time – Benchmarking (wattmeters, photovoltaic panel production traces) and simulation – CPU and network • Offloading the data to process video streams at edge – Effectively reduces the response time – Avoids unnecessary data transmission between edge and core – Extends for instance the battery lifetime of end-user equipment – On-site renewable energy production and batteries in our scenario can save up to 50% total consumed energy consumed at the edge Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 24 Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT, Y. Li, A.-C. Orgerie, I. Rodero, M. Parashar, J.-M. Menaud, CCGrid 2017. Edge Core Edge1 data aggregation v-4 720p v-5 480p v-6 360p Core Edge Core Edge0 v-3 360p v-2 360p v-1 360p r0: p=(a,b), ac = n% A B C Data stream analysis from cameras embedded on vehicles
  25. 25. Resilience • Several Cloud failures in the past – Dropbox, Netflix, Amazon – Huge costs involved • Advantage of Edge computing platforms – No single point of failure • At the infrastructure level – Replication of VMs and data on various geographic locations – Proactive and reactive strategies taking into account network latency into account • At the middleware level – Rescheduling of failed tasks • At the application level – Periodical checkpointing (taking into account locality) Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 25 A Survey of Fog Computing: Concepts, Applications, and Issues? Yi, S., Li, C., Li, Q, Mobidata 2015, June. 2015.
  26. 26. Virtualization/Sandboxing Technologies • SDN/NFV requirements also requires edge DCs • VMs/Containers/Baremetals – How to deliver those abstractions at the edge – Booting a VM may last minutes if the VM image is a remote attached volume – Containers boot faster but they also require containers images • where should we put those images? • What's about Data? – Where should be the data put? – Can we envision data storage repository in every edge site? • Extreme edge (i.e. inside Rasbperry PI, home gateways, ....) – No sufficient resources to start VM/containers with local images – Some system mechanisms should be deployed locally whereas other ones should stay higher in the infrastructure Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 26
  27. 27. Other issues • FOG networking – Maintaining connectivity with heterogeneous (and dynamic) networks – Use/adaptation of Software Defined Networking (SDN) and Network Function Virtualization (NFV) features – Quality of Service • Interfacing and programming model – Right now assembly code level (bunch of low level models for each kind of platforms) – Need of a unified model ? • Accounting, billing and monitoring • Privacy • Simulation and experiments – How to validate algorithms, protocols, and software stacks Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 27
  28. 28. Security Issues Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 28
  29. 29. The Discovery Initiative • Leverage network backbones – Extend any Point of Presence (PoP) of network backbones with servers (from network hubs up to major DSLAMs that are operated by telecom companies, network institutions…). • Extend to the edge by including radio base stations • Discovery – how to operate such a massively distributed infrastructure Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 29 USA NREN http://www.renater.fr/raccourci?lang=fr http://beyondtheclouds.github.io/
  30. 30. Revise OpenStack to Support Fog/Edge Computing Infrastructures • Do not reinvent the wheel… it is too late • Mitigate development efforts – By favoring a bottom/up approach – Investigate whether/how OpenStack core services can become cooperative by default (using P2P and Self-* technics) Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 30 http://beyondtheclouds.github.io/
  31. 31. Several research issues for Discovery • Cost of the network(s) ? • Partial view of the system ? • Impact on others VMs ? • Management of VM images ? • How to take into account locality aspects? • Which software abstractions to make the development easier and more reliable (distributed event programming)? … • OpenStack distribution and deployment Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 31 Beyond The Cloud, How Should Next Generation Utility Computing Infrastructures Be Designed? Lèbre, A., J. Pastor, J., Bertier, M., Desprez, F., Rouzaud- Cornabas, J., Tedeschi, C., Anedda, P., Zanetti, G., Nou, R., Cortes, T., Riviere, E. and Ropars, T., INRIA Research Report 8348, Aug. 2013. http://beyondtheclouds.github.io/
  32. 32. • Pro • Locality (jurisdiction concerns, latency-aware apps, minimize network overhead) • Reliability/redundancy (no critical point/location/center) • The infrastructure is naturally distributed throughout multiple areas • Lead time to delivery • Leverage current PoPs and extend them according to UC demands • Energy footprint (on-going investigations with RENATER) • Bring back part of the revenue to NRENs/Telcos • Cons • Security concerns (in terms of who can access to the PoPs) • Operate a fully IaaS in a unified but distributed manner at WAN level • Not suited for all kinds of applications : Large tightly coupled HPC workloads 50 nodes/1000 cores, 200 nodes / 4000 cores (5 racks), so 1000 nodes in one PoP does not look realistic … • Peering agreement / economic model between network operators http://beyondtheclouds.github.io/ 32Labex UCN@Sophia – F. Desprez Feb. 18, 2016 The DISCOVERY Initiative Pros and Cons
  33. 33. “Good experiments” A good experiment should fulfill the following properties – Reproducibility: must give the same result with the same input – Extensibility: must target possible comparisons with other works and extensions (more/other processors, larger data sets, different architectures) – Applicability: must define realistic parameters and must allow for an easy calibration – “Revisability”: when an implementation does not perform as expected, must help to identify the reasons Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 33
  34. 34. SILECS: Super Infrastructure for Large- scale Experimental Computer Science • Having a large scale infrastructure to experiment IoT/Edge cloud applications and software stacks – Scaling factor – Exascale platforms – Virtualized, Programmable – FOG and Mobile Edge Computing • Features – Manageability • Agility (SDN, NFV) • Self adaptability • Global orchestration – Complexity • Resources • Energy – Data Flow Management • Data deluge processing Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 34
  35. 35. SILECS: based upon two infrastructures • FIT – Proving Internet players access to a variety of fixed and mobile technologies and services, thus accelerating the design of advanced technologies for the Future Internet – 4 key technologies and a single control point: IoT-Lab (connected objects & sensors, mobility), CorteXlab (Cognitive Radio), wireless (anechoic chamber), Network Operations Center (including a PLE access), Advanced Cloud technology including OpenStack – 9 sites (Paris (2), Evry, Rocquencourt, Lille, Strasbourg, Lyon, Grenoble, Sophia Antipolis) • Grid’5000 – A scientific instrument for experimental research on large future infrastructures: Clouds, datacenters, HPC exascale, Big Data infrastructures, networks, etc. – 10 sites, service nodes, > 8000 cores, with a large variety of network connectivity and storage access, dedicated interconnection network granted and managed by RENATER gathered around a GIS (CNRS, CEA, Inria, CPU, RENATER, Institut Mines-Telecom, CDEFI) • Software stacks dedicated to experimentation • Monitoring tools, resource reservation, data collection and storage Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 35
  36. 36. Grid’5000 Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 36 • Testbed for research on distributed systems • Born from the observation that we need a better and larger testbed • HPC, Grids, P2P, and nowCloud computing and BigData systems • A complete access to the nodes’ hardware in an exclusive mode (from one node to the whole infrastructure) • Dedicated network (RENATER) • Reconfigurable: nodes with Kadeploy and network with KaVLAN • Current status • 10 sites, 29 clusters, 1060 nodes, 10474 cores • Diverse technologies/resources (Intel, AMD, Myrinet, Infiniband, two GPU clusters, energy probes) • Some Experiments examples • In Situ analytics • Big Data Management • HPC Programming approaches • Network modeling and simulation • Energy consumption evaluation • Batch scheduler optimization • Large virtual machines deployments
  37. 37. FIT Infrastructure Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 37 FIT-CorteXlab: Cognitive Radio Testbed 40 Software Defined Radio Nodes (SOCRATE) FIT-Wireless: WiFi mesh testbed (DIANA) FIT-IoT-LAB • 2700 wireless sensor nodes spread across six different sites in France • Nodes are either fixed or mobile and can be allocated in various topologies throughout all sites. Sophia Lyon
  38. 38. SILECS Design Objectives • Deploy a large set of digital resources from sensors to data centers – Open, remotely accessible, virtualized infrastructure – Provide rich, diverse and advanced tools: test, measurement, benchmarking, reproducibility, data repository, … – Typically a « mid-scale » infrastructure • Mobilize the scientific community in the domain of digital sciences – Articulate the French and European efforts in this domain – International attractivity and visibility (unique today at the international level) • Several challenges – Heterogeneity of the resulting infrastructures – Different communities and different software stacks – Keep reproducibility at its highest level – Keep the infrastructure up-to-date – Connect the infrastructure to other platforms in Europe and elsewhere Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 38
  39. 39. Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 39 The GRAIL
  40. 40. SILECS Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 40 • New infrastructure based on two existing instruments (FIT and Grid’5000) • Keep the aim of previous platforms (their core scientific issues addressed) – IoT, wireless networks, future Internet for FIT – HPC, Big Data, Clouds, Virtualization, … for Grid’5000 • Address new challenges – IoT and Clouds – New generation Cloud platforms and software stacks (Edge, FOG) – Data streaming applications – Locality aware resource management – … • Submitted to ESFRI in August
  41. 41. Conclusions • Epic battle between centralization and distribution – Batch processing, supercomputers, P2P, Grid, Cloud, Fog, and Edge • Tons of new applications (with new related issues) coming • Probably a mix of different approaches to get the best from every infrastructure – Regular DC, Edge, Extreme Edge – Performance, Quality of Service, energy consumption • Lots of research issues (both theoretical and software design issues) • Distributed computing/network convergence • We need new models to handle heterogeneity (CPU, networks, storage) and dynamicity • Scale issue • How to perform significant experiments for these problems ? • We live in an exciting time ! Entretiens Jacques Cartier - Oct. 2017F. Desprez - From IoT devices to Cloud Computing Infrastructures - 41
  42. 42. Thanks. Any questions ? Thanks to Adrien Lebre (ASCOLA/STACK, Inria, France), Anne-Cécile Orgerie (Myriads, Inria, France), Thierry Coupaye (Orange, France), Omer Rana (UK)

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