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Dynamic execution context management in heterogeneous computing environments, towards persistent computing environments

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Sergey Boldyrev

1) The document discusses the concept of persistent computing environments that allow seamless user experiences across devices, services, and partner ecosystems. 2) Key enablers of persistent computing include utilizing web-based applications on mobile devices, moving to a persistent model that balances computation and data across clouds, infrastructures, and devices. 3) The opportunities of persistent computing include building upon common data models and semantics, distributing computation granularly based on resource demand and component states.

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©2011 Nokia Dynamic execution context management in heterogeneous computing environments, towards persistent computing environments Serguei Boldyrev PhD Location & Commerce BU Common Technologies & Architecture NVRAMOS 2011, November 9th
©2011 Nokia 2 The nature of the consumer’s relationships with companies is changing From a monologue… to a conversation… into continuous relationships From a unified… to a segmented… Into a dynamic personalized offering together with ecosystem 36 Microsoft Content and Consumer Lifetime Value are MAIN DRIVERS
©2011 Nokia 3 Enhancing user experience past device only present device + service(s) + partners future Seamless User Experience across device + services + partner ecosystem Nokia N9+Maps Nokia Lumia + Maps + Music
©2011 Nokia 4 Towards inclusive and sustainable ecosystem Operators People and Places (including Maps) Consumer understanding Open Interfaces Operator Services Attractive offering to mobile users Microsoft Services Nokia Services • Nokia targets 300M active Nokia Services users by the end of 2011 • 800M Cloud-compatible devices exist in 2012 • Persistent computing, energy and cost effective HW is on the vendor’s roadmaps, 2013 and onwards • ARM big.LITTLE, GPUs, SSDs NAND, PCM, MRAM • Cloud Computing subscribers would total nearly one billion by 2014 A sustainable ecosystem where Nokia services will be only one part of the offering
©2011 Nokia 5 Outline • From Web of Information • To Web of Computing • Explore & Share • Persistent Computing • Opportunities & Challenges • Data • Computation • Managed Performance • Privacy • Conclusions
©2011 Nokia 6 From a Web of Information People and Places (including Maps) Service Platforms Open Interfaces Share favorite songs with friends Share content and micro-blog between friends Surface the most relevant content Locate friends around you on a map • Cloud computing today serve email, apps, downloads and storage • Current "cloud apps" relying more on server-side processing • SaaS, PaaS, NaaS and other service models to emerge • Thin client, network as a computer, etc • Seamless information management between Client and Cloud
©2011 Nokia 7 Towards a Web of Computing Between Devices & Infrastructure Scale up/down to/from Cloud Between devices Within Operators & Infrastructures Explore & Share Seamless computing
©2011 Nokia 8 Why is this happening? (we agree with “Others”) Because digital transformation of products and services are demanded by consumers, with increasing complexity driven by new customer expectations Multiple & continuous Interaction Consumption based Always on Personalized and custom Transparent and trusted Everything as a service Consumer expectation
©2011 Nokia 9 Part I: Explore & Share Deliver services and content Device to Device Ovi at Retail concept (see in YouTube) RF memory tags Share content between devices Explore and Share new content in tags
©2011 Nokia 10 RF Memory tag technology Motivation: Evolution of non-volatile memories => from RFID tags to RF memory tags Applications: wireless memory in batteryless objects, ambient intelligence => Universal Local Storage Batteryless tags Capacity ~1 Gbit Mobile phone centric: phone as the UI -> Low cost -> Low power -> Simple -> Minimize added HW -> 100 Mbit/s
©2011 Nokia 11 Positioning of wireless technologies Transfer speed Operation range 1 mm 1 cm 10 cm 1 meter 10 m 100 m 10kb/s 100kb/s 1Mb/s 10Mb/s 100Mb/s EPC gen 2 operational area NFC Universal Local Storage UWB RFID HP Memory SpotPhysical contact required WLAN, WI-FI, BTH, 3G Sony’s TransferJET Active technologies PASSIVE BATTERY- LESS TAGS 60 GHz
©2011 Nokia 12 PHONE UI/APP MIPI BUS WPT MINAmI CONTROLLER POWER SUBSYS MEMORY SUBSYSTEM BATTE RY PHONE RADIO SUBSYS MINAmI SUBSYSTEM POWER MANAGEMENT I-UWB HSI MEMORY CONTROL LAYER MIPI mPHY PCM NETWORK UWBLEE MAC UWBLEE PHY I-UWB WPT CONTROL BLOCK Explore & Share, architecture
©2011 Nokia 13 ULSArchitecture SPI /HSI/MPHY(MIPI) APE CMT SSI SDR PCM DDR Combo ADmux LPDDR1 MAC+ PHY(RF) PCM EMC OS= Operating system Etag = Embedded tag, tag in the phone PCM = Phase Change Memory EMC= External Memory Ctrl SDR= Synchr. DRAM ADmux = Addr, Data mux LPDDR1 = Low power SDRAM Dual Data Rate Interface ULSv. x.y. = > e.g. ULSICv5, UMETAGICversion, that provides RFFrontend MACfunctionality to create over the air connectivity with I-UWB and UHF/NFCpowering. ULSbuffer (over the air part of the memory), memory what is coming from air (bidirectional) = External tag Addr space for OS OS(access to these memory) ULS Server OS(demanded from OS) eTag (in PCM), visible for external device Reader memory (PCM memory) ULS Server ULS Server ULSbuffer (over the air part of the memory), memory what is coming from air (bidirectional) = External tag Addr space for OS OS(access to these memory) ULS Server OS(demanded from OS) eTag (in PCM), visible for external device Reader memory (PCM memory) ULS Server ULS Server ULSServer, also called as MINAmI Server Reader/Writer memory ULSv. x.y MACHW MACSW PHY MAC+ PHY(RF) ULSv. x.y MACHW MACSWPHY “Over the air buffer” (Rx buffer & Tx buffer) “Reader buffer” “3rd buffer for OS” • Received Data first to ULSbuffer • Then ULSServer mark that part to eTag memory (if needed) TAG Reader/Writer Distributed execution memory block Local reflection of the Distributed execution memory block Explore & Share, functional blocks in
©2011 Nokia 14 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor RA M CPU Explore & Share, Computation migration, sort of  ClientA ClientB Client API Computation run-time Environment Agent 1 Agent 2 Client API Computation run-time Environment Distribution Distribution
©2011 Nokia 15 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #1 Approaching XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #2 Suspending XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #3 Dispersing RAM RAM RAM No link Link is going up Link is going up Execution context aggregated/running Execution context suspended Execution context dispersed
©2011 Nokia 16 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #4 Migrating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #5 Aggregating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #6 Resuming RAM RAM RAM Link is up Link is up Link is up Execution context aggregated/running Execution context suspended Execution context dispersed Virtual run-time environment Virtual run-time environment
©2011 Nokia 17 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #7 Suspending XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #8 Dispersing XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #9 Migrating RAM RAM RAM Link is going down Link is going down Link is going down Execution context aggregated/running Execution context suspended Execution context dispersed Virtual run-time environment Virtual run-time environment
©2011 Nokia 18 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #10 Aggregating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #11 Resuming XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #12 Leaving RAM RAM RAM No Link No Link No Link Execution context aggregated/running Execution context suspended Execution context dispersed
©2011 Nokia 19 Ideal Scenario for the Future Solution • Mobile device is a true contributing participant , not just a depending client • Mobile device connects to multiple data sources (possibly independent devices or clouds), forming one logical Computation/Data and Service space • From any connected device, the user deals with the same data regardless of its physical location • Solution provides both data and distributed computing services • Solution access is managed by fine-grained security policies applied according to the user’s context • From the design perspective, the Solution manages and hides complexity; does not increase it • Solution data and services decrease development and maintenance costs • Solution data and service pricing model creates new money flows between players
©2011 Nokia 20 New Digital Experience "My digital experience follows me regardless the computing environments around me“ • take advantage of finely granular and accountable processing services, • integrate heterogeneous information sources, and • ultimately free users of mundane challenges and details of technology usage Greater reuse of information and computational tasks is just few steps away
©2011 Nokia 21 Part II: New Digital Experience World of fully deployed and available Computing – Persistent Platforms The emergence of Platforms that can seamlessly span the “information spaces” of multiple • hardware, • software, • information and • infrastructure providers
©2011 Nokia 22 Technological enablers for Persistent Computing Utilizing the best parts of the Web development paradigm • …Web-based applications, not Web sites… • and leveraging the special contextual capabilities of mobile devices (ex. WURFL databases on sensors) Moving from a Device, Cloud and Web-centric models to a Persistent model (Persistent computing) • Reuse (partition) information and persistent computation in the Cloud, Infrastructure and Devices • Elimination of special-purpose software to download, install applications, Service oriented infrastructure constructed as a functional flows requested on demand • Enabling balancing of computation and relevant data between heterogeneous Cloud Back-Ends, Infrastructures (ex. Spanner, HDFS, Web Intents etc) and devices Fostering faster, easier, richer application innovation and deployment through SW and HW Persistent computation recycling • AMP processors, non-volatile execution memory • Diverse Cloud Back-Ends can all leverage the infrastructure capabilities • Atomic computations are now deployed once to the Back-Ends and composed down to Infrastructure and clients, where a set of functional flows or description of those form the actual service • Allowing Network Infrastructure to leverage data and computation workload, by taking care of services or provider of services capabilities, distributing computation between Back-Ends and Infrastructure • Allowing more efficient contextual composition of services than purely device or Web-centric models Platform Data App Device-centric model Web-centric model ABC Cloud Data Platform App “Future” model ABC Cloud Computation & Data Platform App XYZ Cloud
©2011 Nokia 23 Persistent Computations NVRA M Regional DC Master DC Regional DC • A next generation distributed systems are dynamically spanned around virtual and physical infrastructures • Device becomes a known and contributing node of the cloud infrastructure, service provisioning element Examples: Services based on crowdsourced information on traffic, pollution, weather, body temperature, context intelligence, environments adapting to user context, efficient computing NVRA M Cloud Service Core Edge
©2011 Nokia 24 Persistent computing, Requirements The new emerging paradigm of Distributed Systems requires an explicit orchestration of computation across • Mobile Devices (D2D connectivity) • the Edge, (thin elements of the Cloud “skin”) • Cloud and • the Core Orchestration depends on • components’ states (persistency!) and • resource demand, through the proper exploitation of granular and therefore accountable mechanisms
©2011 Nokia 25 “Aha!” moment Design framework should be re-defined! Persistence is the key attribute of ALL future Data & Computation Management systems
©2011 Nokia 26 Opportunities and Challenges Main aspects of • Data and Computation semantics, • Performance and Scalability of computation and networking, • Role of Security and Privacy
©2011 Nokia 27 Data - Common Data Model (CDM) enables Shared data across application domains Policy Engine Shared Information Data & Computation Integration Semantics Service1 Logic Service1 API ClientA ClientB Service2 Logic Service2 API Data API Client Applications should not “own” data, instead, they should reflect the users’ desire to do something, to accomplish certain goals • to call someone, you need Contacts, • to know what to do next, you need Calendar, etc. Data is still the same… • for example, it would be helpful for Contacts to know who is the organizer of your next meeting • EXAMPLE: Apple Siri, Google Chrome
©2011 Nokia 28 Computation and Data perspective Prerequisites Core Data assets are encapsulated into results of Computation tasks Objectives (but not limited to listed below) • Design and implementation of scenarios where computation can be described and represented in a system-wide understandable way • enabling computation to be “migrated” (transmitted) from one computational environment to another • such transmission would involve reconstruction of the computational task at the receiving end to match the defined semantics of task and data involved • Construction of a system where larger computational tasks can be decomposed into smaller tasks or units of computation • independent of what the eventual execution environment(s) of these tasks (that is, independent of hardware platforms or operating systems)
©2011 Nokia 29 Computation, the components ClientA ClientB Backend Environment Client API Computation run-time Environment Agent 1 Agent 2 Agent 3 Client API Computation run-time Environment Recycling & Marshaling Computations Store Agent 4 Agent 5 Convenience API PHP API Java API Distribution Back end API Distribution Control/Ontologies Computation Distribution Computation closure – • current state of execution and context • automatically generated during the development phase • composed out of anonymous function object Function object – consists of: • class template result_of, • function template mem_fn, • function object binders, and • polymorphic function wrapper function
©2011 Nokia 30 Computations, zoom in Closures Environment Agent 3 Scalable Computing Monadic cross platform support Agent 2 Developer Experience Closures based Granular Reflective Processes support Agent 1 Services Infrastructure Computation Closures Closure set 3 User Device Backend Environment Agent 4 Closures Recycling & Marshaling Computation Closures Store Agent 5 Agent 6 Convenience API Distribution Back end API Closure set 1 Closure set 2 Closures Store Execution context checkpoint & distribution Process scheduling Memory management System calls Computation Closures set are backed by a Recycling or Marshaling Service, with a load-balancing facility that serializes Closures to be executed on remote computing environments Treating Closures as Data entities, letting us compose Closures, chain them, and project computations into them PHP API Java API Store
©2011 Nokia 31 Computations, User space Common Data Platform Object <compute_1, compute_2, compute_3, …> compute_3=filter(object) context{object<compute_3>} object=project(compute_3) 1. User Computations 6. Reconstruct computation Computation set 3 Computation set 1 Computation set 2 Applications, User Context Applications and Back End logic 3. Computation distribution put(compute_3) 5. Project computation to the Functional chain with User device context get(compute_3) 4. Computation extraction Users’ Computations [ ] ( ) { } Persistent Granular and Reflective Process Representation User Device 2. Selection of the context (functional chain) Agent 3 Agent 1 lift’n’compose Developer specifies ECMAScript bounded to the ComputationsBack End provides the basic Computation sets Agent 2
©2011 Nokia 32 Computations, Reflective Run-time environment Distributed information management framework enables reflective process/context migration – Marshalling/UnMarshalling – Alignment and enhancement with distribution Distributed Computation and Data model allows dynamically balanced load between concurrent execution environments taking into account the user’s current context Common Data PlatformProcess scheduling Memory mngm. System calls Process scheduling Memory mngm. System calls Device boundaryDevice Remote/Device Back-end User’s context User’s context Agent Agent Agent Agent Agent Agent Agent Agent Store Store
©2011 Nokia 33 Persistent Computations URIs Computation environment Computation environment Legacy routines Legacy routines 0xFFFF 0x0 0x0 0xFFFF … FFFF Legacy routines Persistent Computations based platform Legacy routines URIs Front-End Side Back-End Side Distribution/sync Computations SUPERSET Computations subset
©2011 Nokia 34 Construction of the computational ontology Closure Object EnvironmentClosure State C64BitInteger Variable Signature Instance Properties Class Properties Non- Functional properties name: str value: str index: int use: by-value / by-ref 1 1..* 1..* 2..* 11 111 1 1 1 1 1 1 1 1 1..* Constraint: Each variable can be a property of only one of Signature, Environment or Closure State These help in creating optimal functional flows • Core parts contain generic definitions for closure behavior • Environment specific extensions as specialization of core parts • Matching between the environment specific extensions • Variation in closure ontologies • Limited number of specializations • Also non-functional properties • energy • performance • … • Moving closures states from e.g. Back-End Java/PHP/C++ to pure Qt/QML/Java environment and vice versa
©2011 Nokia 35 Developer Experience – integration with SDK Closures allow to compose function expression that can be wrapped and exposed to Developer as Data Closures can be passed to API, stored, shared or spread the computation – The best estimated result based on the first returning closure can be seen as a part of functional flow – When closures “return”, results can be refined (parallelism) – Computation recycling is possible – Builds up a cache of results/partial results (“fast computation”)
©2011 Nokia 36 Example Computational branch x Computation 1 Computation 2 Computation 3 Connector 00 start Each branch represents Computation associated with Data, where Computational model is encapsulated into Data model Before Starts: Parameters to know beforehand (before start invoked): 1) Capabilities 2) Functional Flow specification (of how many parts we have in the map), see legend in this slide (right). 3) Cost functions (compound function): what are cost in order to bind next Computation 4) Rules (entity that drives this connector Computation and if not then it directs elsewhere) - capabilities Cost function Functional flow Rules e.g. Connector 00 e.g. Connector x0 Connector x0 Connector x1 Connector x2 => First list the 1-4 parameters then proceed with load balancing phases Computation 0 Connector x3 Computation x1 Within devices Connector x1 Connector x2 Computation x1 Computation x2 Computation x2 Computation x3 Computation x3 Connector x3 Computation 4 stop
©2011 Nokia 37 Performance/Scalability, Requirements • Granular latency control for diverse Data and Computational load in heterogeneous environments • Resolution of short term capacity decisions, including • the determination of optimal configuration, • the number of (live) Persistent nodes or • the migration of computation or data • Resolution of long term capacity decisions • decisions concerning the development and • extension of data- and service architecture, choice of technology, etc • Control of SLAs at different levels, • e.g. consumer services, platform components, etc • Quality assessment of distributed software architecture
©2011 Nokia 38 User, Trust, Privacy the placement and scope Content and Consumer Lifetime Value are MAIN DRIVERS (note that in this diagram, by “ontology” the semantics is meant)
©2011 Nokia 39 Conclusion - Driving agenda • Creation of approaches and effective tools for the next developer and consumer applications, tackling • Fuse & Lift of Big Data (Bring algorithm to the data, Knowledge creation) • Social graph oriented patterns (social media, chat, calendar, etc) • Tele immersive environment (AR, 3D maps, etc) • Persistent Computing platform for the Continues Operations o the fine-grained (and thus easily accountable) software frameworks developments o anything as a persistent service, e.g. sometimes giving hardware for “free” • Mobile devices supported by Backend infrastructure technologies to enable qualitative leap in ICT industry offerings, from the perspectives of o scalable technology and o multisided business models with high elasticity Aiming and supporting future strategic growth areas and maintenance requirements
40©2011 Nokia Q&A [ ] ( ) { } sergey.boldyrev@nokia.com
©2011 Nokia 41 References • ULS project • SlipStream project • Boldyrev, S., Kolesnikov, D., Lassila, O., & Oliver, I.. (2012). Data and Computation Interoperability in Internet Services. CLOSER.
©2011 Nokia Thank you

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Dynamic execution context management in heterogeneous computing environments, towards persistent computing environments

  • 1. ©2011 Nokia Dynamic execution context management in heterogeneous computing environments, towards persistent computing environments Serguei Boldyrev PhD Location & Commerce BU Common Technologies & Architecture NVRAMOS 2011, November 9th
  • 2. ©2011 Nokia 2 The nature of the consumer’s relationships with companies is changing From a monologue… to a conversation… into continuous relationships From a unified… to a segmented… Into a dynamic personalized offering together with ecosystem 36 Microsoft Content and Consumer Lifetime Value are MAIN DRIVERS
  • 3. ©2011 Nokia 3 Enhancing user experience past device only present device + service(s) + partners future Seamless User Experience across device + services + partner ecosystem Nokia N9+Maps Nokia Lumia + Maps + Music
  • 4. ©2011 Nokia 4 Towards inclusive and sustainable ecosystem Operators People and Places (including Maps) Consumer understanding Open Interfaces Operator Services Attractive offering to mobile users Microsoft Services Nokia Services • Nokia targets 300M active Nokia Services users by the end of 2011 • 800M Cloud-compatible devices exist in 2012 • Persistent computing, energy and cost effective HW is on the vendor’s roadmaps, 2013 and onwards • ARM big.LITTLE, GPUs, SSDs NAND, PCM, MRAM • Cloud Computing subscribers would total nearly one billion by 2014 A sustainable ecosystem where Nokia services will be only one part of the offering
  • 5. ©2011 Nokia 5 Outline • From Web of Information • To Web of Computing • Explore & Share • Persistent Computing • Opportunities & Challenges • Data • Computation • Managed Performance • Privacy • Conclusions
  • 6. ©2011 Nokia 6 From a Web of Information People and Places (including Maps) Service Platforms Open Interfaces Share favorite songs with friends Share content and micro-blog between friends Surface the most relevant content Locate friends around you on a map • Cloud computing today serve email, apps, downloads and storage • Current "cloud apps" relying more on server-side processing • SaaS, PaaS, NaaS and other service models to emerge • Thin client, network as a computer, etc • Seamless information management between Client and Cloud
  • 7. ©2011 Nokia 7 Towards a Web of Computing Between Devices & Infrastructure Scale up/down to/from Cloud Between devices Within Operators & Infrastructures Explore & Share Seamless computing
  • 8. ©2011 Nokia 8 Why is this happening? (we agree with “Others”) Because digital transformation of products and services are demanded by consumers, with increasing complexity driven by new customer expectations Multiple & continuous Interaction Consumption based Always on Personalized and custom Transparent and trusted Everything as a service Consumer expectation
  • 9. ©2011 Nokia 9 Part I: Explore & Share Deliver services and content Device to Device Ovi at Retail concept (see in YouTube) RF memory tags Share content between devices Explore and Share new content in tags
  • 10. ©2011 Nokia 10 RF Memory tag technology Motivation: Evolution of non-volatile memories => from RFID tags to RF memory tags Applications: wireless memory in batteryless objects, ambient intelligence => Universal Local Storage Batteryless tags Capacity ~1 Gbit Mobile phone centric: phone as the UI -> Low cost -> Low power -> Simple -> Minimize added HW -> 100 Mbit/s
  • 11. ©2011 Nokia 11 Positioning of wireless technologies Transfer speed Operation range 1 mm 1 cm 10 cm 1 meter 10 m 100 m 10kb/s 100kb/s 1Mb/s 10Mb/s 100Mb/s EPC gen 2 operational area NFC Universal Local Storage UWB RFID HP Memory SpotPhysical contact required WLAN, WI-FI, BTH, 3G Sony’s TransferJET Active technologies PASSIVE BATTERY- LESS TAGS 60 GHz
  • 12. ©2011 Nokia 12 PHONE UI/APP MIPI BUS WPT MINAmI CONTROLLER POWER SUBSYS MEMORY SUBSYSTEM BATTE RY PHONE RADIO SUBSYS MINAmI SUBSYSTEM POWER MANAGEMENT I-UWB HSI MEMORY CONTROL LAYER MIPI mPHY PCM NETWORK UWBLEE MAC UWBLEE PHY I-UWB WPT CONTROL BLOCK Explore & Share, architecture
  • 13. ©2011 Nokia 13 ULSArchitecture SPI /HSI/MPHY(MIPI) APE CMT SSI SDR PCM DDR Combo ADmux LPDDR1 MAC+ PHY(RF) PCM EMC OS= Operating system Etag = Embedded tag, tag in the phone PCM = Phase Change Memory EMC= External Memory Ctrl SDR= Synchr. DRAM ADmux = Addr, Data mux LPDDR1 = Low power SDRAM Dual Data Rate Interface ULSv. x.y. = > e.g. ULSICv5, UMETAGICversion, that provides RFFrontend MACfunctionality to create over the air connectivity with I-UWB and UHF/NFCpowering. ULSbuffer (over the air part of the memory), memory what is coming from air (bidirectional) = External tag Addr space for OS OS(access to these memory) ULS Server OS(demanded from OS) eTag (in PCM), visible for external device Reader memory (PCM memory) ULS Server ULS Server ULSbuffer (over the air part of the memory), memory what is coming from air (bidirectional) = External tag Addr space for OS OS(access to these memory) ULS Server OS(demanded from OS) eTag (in PCM), visible for external device Reader memory (PCM memory) ULS Server ULS Server ULSServer, also called as MINAmI Server Reader/Writer memory ULSv. x.y MACHW MACSW PHY MAC+ PHY(RF) ULSv. x.y MACHW MACSWPHY “Over the air buffer” (Rx buffer & Tx buffer) “Reader buffer” “3rd buffer for OS” • Received Data first to ULSbuffer • Then ULSServer mark that part to eTag memory (if needed) TAG Reader/Writer Distributed execution memory block Local reflection of the Distributed execution memory block Explore & Share, functional blocks in
  • 14. ©2011 Nokia 14 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor RA M CPU Explore & Share, Computation migration, sort of  ClientA ClientB Client API Computation run-time Environment Agent 1 Agent 2 Client API Computation run-time Environment Distribution Distribution
  • 15. ©2011 Nokia 15 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #1 Approaching XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #2 Suspending XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #3 Dispersing RAM RAM RAM No link Link is going up Link is going up Execution context aggregated/running Execution context suspended Execution context dispersed
  • 16. ©2011 Nokia 16 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #4 Migrating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #5 Aggregating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #6 Resuming RAM RAM RAM Link is up Link is up Link is up Execution context aggregated/running Execution context suspended Execution context dispersed Virtual run-time environment Virtual run-time environment
  • 17. ©2011 Nokia 17 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #7 Suspending XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #8 Dispersing XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #9 Migrating RAM RAM RAM Link is going down Link is going down Link is going down Execution context aggregated/running Execution context suspended Execution context dispersed Virtual run-time environment Virtual run-time environment
  • 18. ©2011 Nokia 18 XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #10 Aggregating XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #11 Resuming XiP NVRA M CPU R F R F Mobile device, handheld form factor Stationary device, notebook form factor CPU Step #12 Leaving RAM RAM RAM No Link No Link No Link Execution context aggregated/running Execution context suspended Execution context dispersed
  • 19. ©2011 Nokia 19 Ideal Scenario for the Future Solution • Mobile device is a true contributing participant , not just a depending client • Mobile device connects to multiple data sources (possibly independent devices or clouds), forming one logical Computation/Data and Service space • From any connected device, the user deals with the same data regardless of its physical location • Solution provides both data and distributed computing services • Solution access is managed by fine-grained security policies applied according to the user’s context • From the design perspective, the Solution manages and hides complexity; does not increase it • Solution data and services decrease development and maintenance costs • Solution data and service pricing model creates new money flows between players
  • 20. ©2011 Nokia 20 New Digital Experience "My digital experience follows me regardless the computing environments around me“ • take advantage of finely granular and accountable processing services, • integrate heterogeneous information sources, and • ultimately free users of mundane challenges and details of technology usage Greater reuse of information and computational tasks is just few steps away
  • 21. ©2011 Nokia 21 Part II: New Digital Experience World of fully deployed and available Computing – Persistent Platforms The emergence of Platforms that can seamlessly span the “information spaces” of multiple • hardware, • software, • information and • infrastructure providers
  • 22. ©2011 Nokia 22 Technological enablers for Persistent Computing Utilizing the best parts of the Web development paradigm • …Web-based applications, not Web sites… • and leveraging the special contextual capabilities of mobile devices (ex. WURFL databases on sensors) Moving from a Device, Cloud and Web-centric models to a Persistent model (Persistent computing) • Reuse (partition) information and persistent computation in the Cloud, Infrastructure and Devices • Elimination of special-purpose software to download, install applications, Service oriented infrastructure constructed as a functional flows requested on demand • Enabling balancing of computation and relevant data between heterogeneous Cloud Back-Ends, Infrastructures (ex. Spanner, HDFS, Web Intents etc) and devices Fostering faster, easier, richer application innovation and deployment through SW and HW Persistent computation recycling • AMP processors, non-volatile execution memory • Diverse Cloud Back-Ends can all leverage the infrastructure capabilities • Atomic computations are now deployed once to the Back-Ends and composed down to Infrastructure and clients, where a set of functional flows or description of those form the actual service • Allowing Network Infrastructure to leverage data and computation workload, by taking care of services or provider of services capabilities, distributing computation between Back-Ends and Infrastructure • Allowing more efficient contextual composition of services than purely device or Web-centric models Platform Data App Device-centric model Web-centric model ABC Cloud Data Platform App “Future” model ABC Cloud Computation & Data Platform App XYZ Cloud
  • 23. ©2011 Nokia 23 Persistent Computations NVRA M Regional DC Master DC Regional DC • A next generation distributed systems are dynamically spanned around virtual and physical infrastructures • Device becomes a known and contributing node of the cloud infrastructure, service provisioning element Examples: Services based on crowdsourced information on traffic, pollution, weather, body temperature, context intelligence, environments adapting to user context, efficient computing NVRA M Cloud Service Core Edge
  • 24. ©2011 Nokia 24 Persistent computing, Requirements The new emerging paradigm of Distributed Systems requires an explicit orchestration of computation across • Mobile Devices (D2D connectivity) • the Edge, (thin elements of the Cloud “skin”) • Cloud and • the Core Orchestration depends on • components’ states (persistency!) and • resource demand, through the proper exploitation of granular and therefore accountable mechanisms
  • 25. ©2011 Nokia 25 “Aha!” moment Design framework should be re-defined! Persistence is the key attribute of ALL future Data & Computation Management systems
  • 26. ©2011 Nokia 26 Opportunities and Challenges Main aspects of • Data and Computation semantics, • Performance and Scalability of computation and networking, • Role of Security and Privacy
  • 27. ©2011 Nokia 27 Data - Common Data Model (CDM) enables Shared data across application domains Policy Engine Shared Information Data & Computation Integration Semantics Service1 Logic Service1 API ClientA ClientB Service2 Logic Service2 API Data API Client Applications should not “own” data, instead, they should reflect the users’ desire to do something, to accomplish certain goals • to call someone, you need Contacts, • to know what to do next, you need Calendar, etc. Data is still the same… • for example, it would be helpful for Contacts to know who is the organizer of your next meeting • EXAMPLE: Apple Siri, Google Chrome
  • 28. ©2011 Nokia 28 Computation and Data perspective Prerequisites Core Data assets are encapsulated into results of Computation tasks Objectives (but not limited to listed below) • Design and implementation of scenarios where computation can be described and represented in a system-wide understandable way • enabling computation to be “migrated” (transmitted) from one computational environment to another • such transmission would involve reconstruction of the computational task at the receiving end to match the defined semantics of task and data involved • Construction of a system where larger computational tasks can be decomposed into smaller tasks or units of computation • independent of what the eventual execution environment(s) of these tasks (that is, independent of hardware platforms or operating systems)
  • 29. ©2011 Nokia 29 Computation, the components ClientA ClientB Backend Environment Client API Computation run-time Environment Agent 1 Agent 2 Agent 3 Client API Computation run-time Environment Recycling & Marshaling Computations Store Agent 4 Agent 5 Convenience API PHP API Java API Distribution Back end API Distribution Control/Ontologies Computation Distribution Computation closure – • current state of execution and context • automatically generated during the development phase • composed out of anonymous function object Function object – consists of: • class template result_of, • function template mem_fn, • function object binders, and • polymorphic function wrapper function
  • 30. ©2011 Nokia 30 Computations, zoom in Closures Environment Agent 3 Scalable Computing Monadic cross platform support Agent 2 Developer Experience Closures based Granular Reflective Processes support Agent 1 Services Infrastructure Computation Closures Closure set 3 User Device Backend Environment Agent 4 Closures Recycling & Marshaling Computation Closures Store Agent 5 Agent 6 Convenience API Distribution Back end API Closure set 1 Closure set 2 Closures Store Execution context checkpoint & distribution Process scheduling Memory management System calls Computation Closures set are backed by a Recycling or Marshaling Service, with a load-balancing facility that serializes Closures to be executed on remote computing environments Treating Closures as Data entities, letting us compose Closures, chain them, and project computations into them PHP API Java API Store
  • 31. ©2011 Nokia 31 Computations, User space Common Data Platform Object <compute_1, compute_2, compute_3, …> compute_3=filter(object) context{object<compute_3>} object=project(compute_3) 1. User Computations 6. Reconstruct computation Computation set 3 Computation set 1 Computation set 2 Applications, User Context Applications and Back End logic 3. Computation distribution put(compute_3) 5. Project computation to the Functional chain with User device context get(compute_3) 4. Computation extraction Users’ Computations [ ] ( ) { } Persistent Granular and Reflective Process Representation User Device 2. Selection of the context (functional chain) Agent 3 Agent 1 lift’n’compose Developer specifies ECMAScript bounded to the ComputationsBack End provides the basic Computation sets Agent 2
  • 32. ©2011 Nokia 32 Computations, Reflective Run-time environment Distributed information management framework enables reflective process/context migration – Marshalling/UnMarshalling – Alignment and enhancement with distribution Distributed Computation and Data model allows dynamically balanced load between concurrent execution environments taking into account the user’s current context Common Data PlatformProcess scheduling Memory mngm. System calls Process scheduling Memory mngm. System calls Device boundaryDevice Remote/Device Back-end User’s context User’s context Agent Agent Agent Agent Agent Agent Agent Agent Store Store
  • 33. ©2011 Nokia 33 Persistent Computations URIs Computation environment Computation environment Legacy routines Legacy routines 0xFFFF 0x0 0x0 0xFFFF … FFFF Legacy routines Persistent Computations based platform Legacy routines URIs Front-End Side Back-End Side Distribution/sync Computations SUPERSET Computations subset
  • 34. ©2011 Nokia 34 Construction of the computational ontology Closure Object EnvironmentClosure State C64BitInteger Variable Signature Instance Properties Class Properties Non- Functional properties name: str value: str index: int use: by-value / by-ref 1 1..* 1..* 2..* 11 111 1 1 1 1 1 1 1 1 1..* Constraint: Each variable can be a property of only one of Signature, Environment or Closure State These help in creating optimal functional flows • Core parts contain generic definitions for closure behavior • Environment specific extensions as specialization of core parts • Matching between the environment specific extensions • Variation in closure ontologies • Limited number of specializations • Also non-functional properties • energy • performance • … • Moving closures states from e.g. Back-End Java/PHP/C++ to pure Qt/QML/Java environment and vice versa
  • 35. ©2011 Nokia 35 Developer Experience – integration with SDK Closures allow to compose function expression that can be wrapped and exposed to Developer as Data Closures can be passed to API, stored, shared or spread the computation – The best estimated result based on the first returning closure can be seen as a part of functional flow – When closures “return”, results can be refined (parallelism) – Computation recycling is possible – Builds up a cache of results/partial results (“fast computation”)
  • 36. ©2011 Nokia 36 Example Computational branch x Computation 1 Computation 2 Computation 3 Connector 00 start Each branch represents Computation associated with Data, where Computational model is encapsulated into Data model Before Starts: Parameters to know beforehand (before start invoked): 1) Capabilities 2) Functional Flow specification (of how many parts we have in the map), see legend in this slide (right). 3) Cost functions (compound function): what are cost in order to bind next Computation 4) Rules (entity that drives this connector Computation and if not then it directs elsewhere) - capabilities Cost function Functional flow Rules e.g. Connector 00 e.g. Connector x0 Connector x0 Connector x1 Connector x2 => First list the 1-4 parameters then proceed with load balancing phases Computation 0 Connector x3 Computation x1 Within devices Connector x1 Connector x2 Computation x1 Computation x2 Computation x2 Computation x3 Computation x3 Connector x3 Computation 4 stop
  • 37. ©2011 Nokia 37 Performance/Scalability, Requirements • Granular latency control for diverse Data and Computational load in heterogeneous environments • Resolution of short term capacity decisions, including • the determination of optimal configuration, • the number of (live) Persistent nodes or • the migration of computation or data • Resolution of long term capacity decisions • decisions concerning the development and • extension of data- and service architecture, choice of technology, etc • Control of SLAs at different levels, • e.g. consumer services, platform components, etc • Quality assessment of distributed software architecture
  • 38. ©2011 Nokia 38 User, Trust, Privacy the placement and scope Content and Consumer Lifetime Value are MAIN DRIVERS (note that in this diagram, by “ontology” the semantics is meant)
  • 39. ©2011 Nokia 39 Conclusion - Driving agenda • Creation of approaches and effective tools for the next developer and consumer applications, tackling • Fuse & Lift of Big Data (Bring algorithm to the data, Knowledge creation) • Social graph oriented patterns (social media, chat, calendar, etc) • Tele immersive environment (AR, 3D maps, etc) • Persistent Computing platform for the Continues Operations o the fine-grained (and thus easily accountable) software frameworks developments o anything as a persistent service, e.g. sometimes giving hardware for “free” • Mobile devices supported by Backend infrastructure technologies to enable qualitative leap in ICT industry offerings, from the perspectives of o scalable technology and o multisided business models with high elasticity Aiming and supporting future strategic growth areas and maintenance requirements
  • 40. 40©2011 Nokia Q&A [ ] ( ) { } sergey.boldyrev@nokia.com
  • 41. ©2011 Nokia 41 References • ULS project • SlipStream project • Boldyrev, S., Kolesnikov, D., Lassila, O., & Oliver, I.. (2012). Data and Computation Interoperability in Internet Services. CLOSER.