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Outdoor Distributed Computing*

Outdoor Distributed Computing*. Cristian Borcea Department of Computer Science New Jersey Institute of Technology. * Work done at Rutgers prior to September 2004. Computers Embedded Everywhere. Outdoor Distributed Applications. Cars collaborating for a safer and more fluid traffic.

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Outdoor Distributed Computing*

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  1. Outdoor Distributed Computing* Cristian Borcea Department of Computer Science New Jersey Institute of Technology * Work done at Rutgers prior to September 2004

  2. Computers Embedded Everywhere

  3. Outdoor Distributed Applications Cars collaborating for a safer and more fluid traffic Distributed object tracking over a large geographical area • Howto write distributed programs over outdoor networks of embedded systems?

  4. Traditional (Indoor) Distributed Computing • Commonly, computation is distributed for performance or fault tolerance • Relatively easy to program • Nodes are computationally equivalent • Networking is robust and has acceptable delays • Configuration is stable

  5. Outdoor Distributed Computing • Computation is physically distributed • Nodes distributed across the physical space • Location-aware computing • Hard to program • Nodes are functionally heterogeneous and potentially mobile • Ad hoc wireless networks of embedded systems • Volatile configurations

  6. Outdoor Programming Example Left Hill Right Hill Motion Sensor Mobile robot with camera • Intrusion detection across the hills using motion sensors and autonomous mobile robots with cameras • Number and location of systems are unknown • Configuration is not stable over time • Intrusions can appear randomly • Robots can move

  7. Traditional Distributed Computing Does Not Work Well Outdoors • End-to-end data transfers may hardly complete • Fixed address naming and routing (e.g., IP) are too rigid • Difficult to deploy new applications in existing networks • Outdoor distributed computing requires novel programming models and system architectures

  8. Outline • Motivation • Spatial Programming Model • Smart Messages System Architecture • Implementation Details • Experimental Results • Conclusions

  9. Traditional (Indoor) Programming • Programs access data through variables • Variables mapped to physical memory locations • Page Table and OS guarantee reference consistency • Access time has an (acceptable) upper bound Program Variable access Virtual Address Space Page Table + OS Physical Memory

  10. From Indoor to Outdoor Computing Virtual Address Space Space Region Variables Spatial References Spatial references mapped to systems embedded in the physical space Variables mapped to physical memory Reference consistency ? ? Bounded access time

  11. Spatial Programming (SP) at a Glance • Provides a virtual name space over outdoor networks of embedded systems • Embedded systems named by their locations and properties • Runtime system takes care of name resolution, reference consistency, and networking aspects • Implementation on top of Smart Messages: SP applications execute, sequentially, on each system referenced in their code

  12. Space Regions Hill = new Space({lat, long}, radius); {lat,long} radius • Virtual representation of a physical space • Similar to a virtual address space in a conventional computer system • Defined statically or dynamically

  13. Spatial References {Hill:robot[0]} Hill r7 {Hill:robot[1]} r2 {Hill:motion[0]} m5 • Defined as {space:property} pairs • Virtual names for embedded systems • Similar to variables in conventional programming • Indexes used to distinguish among similar systems in the same space region

  14. From Indoor to Outdoor Computing Virtual Address Space Space Region Variables Spatial References Spatial references mapped to systems embedded in the physical space Variables mapped to physical memory Reference consistency ? ? Bounded access time

  15. Reference Consistency • At first access, a spatial reference is mapped to an embedded system located in the specified space • Mappings maintained in per-application Mapping Table (MT) – similar to a page table • Subsequent accesses to the same spatial reference will reach the same system (using MT) as long as it is located in the same space region {space, property, index} {unique_address, location}

  16. Reference Consistency Example r2 r7 r5 Right Hill Left Hill {Left_Hill:robot[0]}.move = ON; {Left_Hill:robot[0]}.move = OFF;

  17. Space Casting r7 Right Hill Left Hill {Left_Hill:robot[0]} {Right_Hill:(Left_Hill:robot[0])}

  18. From Indoor to Outdoor Computing Virtual Address Space Space Region Variables Spatial References Spatial references mapped to systems embedded in the physical space Variables mapped to physical memory Reference consistency Mapping Table ? Bounded access time

  19. Bounding the Access Time • How to bound the time to access a spatial reference? • Systems may move, go out of space, or disappear • Solution: associate an explicit timeout with each spatial reference access try{ {Hill:robot[0], timeout}.move = ON; }catch(TimeoutException e){ // the programmer decides the next action }

  20. Spatial Programming Example - Find the sensor that detected the “strongest” motion on Left Hill - Turn on a camera in the proximity of this sensor Left Hill Right Hill for(i=0; i<1000; i++) try{ if ({Left_Hill:motion[i], timeout}.detect > Max_motion) Max_motion = {Left_Hill:motion[i], timeout}.detect; Max_id = i; }catch(TimeoutException e) break; intrusionSpace = rangeOf({Left_Hill:motion[Max_id].location}, Range); {intrusionSpace:robot[0]}.camera = ON; {intrusionSpace:robot[0]}.focus = {Left_Hill:motion[Max_id].location};

  21. Outline • Motivation • Spatial Programming Model • Smart Messages System Architecture • Implementation Details • Experimental Results • Conclusions

  22. “Dumb” Messages vs. “Smart” Messages Lunch: Appetizer Entree Dessert Data migration Execution migration

  23. Smart Messages at a Glance • User-defined distributed applications • Composed of code bricks, data bricks, and execution control state • Execute on nodes of interest named by properties • Migrate between nodes of interest • Self-route at every node in the path during migrations

  24. Cooperative Node Architecture Network Network SM Ready Queue SM SM Admission Manager Virtual Machine Authorization Code Cache SM Platform Tag Space Operating System & I/O

  25. SM Execution at a Node • Takes place over a virtual machine • Non-preemptive, but time bounded • Ends with a migration, or terminates • During execution, SMs can • Spawn new SMs • Create smaller SMs out of their code and data bricks • Access the tag space • Block on a tag to be updated (update-based synchronization)

  26. Tag Space • Collection of application tags and I/O tags • Essentially, tags are (name, value) pairs • Application tags: persistent memory across SM executions • I/O tags: access to operating system and I/O subsystem • Tags used for • Content-based naming migrate(tag) • Inter-SM communication write(tag, data), read(tag) • Synchronization block(tag, timeout) • I/O access read(temperature)

  27. Protection Domains for Tag Space • Owner: SM that creates the tag • Family: all SMs having a common ancestor with the SM that owns the tag • Origin: all SMs created on the same node as the family originator of the tag owner • Code: all SMs carrying a specific code brick • Others: all the other SMs Owner Family Origin Code Others

  28. Access Control Example (Code-based protection domain) SM3 C4 C3 Access permission granted for SM2 Access permission denied for SM3 SM1 SM1 SM1 Node2 Node1 Cr C1 Code Bricks Node5 SM2 SM2 Node4 Node3 Cr C2 SM2 Tag Owner = SM1 [Hash(Cr), RW] Cr Same routing used by SM1 and SM2

  29. SM Admission • Ensures progress for all SMs in the network • Prevents SMs from migrating to nodes that cannot provide enough resources • SMs specify lower bounds for resource requirements (e.g., memory, bandwidth) • SMs accepted if the node can satisfy these requirements • More resources can be granted according to admission policy • If not granted, SMs are allowed to migrate

  30. Application Example need 2 taxis Taxi Taxi n=0 n=0 n=0 n=1 n=1 n=2 SM n=0 while (n<NumTaxis) migrate(Taxi); if (readTag(Available)) writeTag(Available, false); writeTag(Location, myLocation); n++; data brick application code brick routing code brick

  31. SM Migration migrate(Taxi) Taxi Taxi sys_migrate(3) sys_migrate(2) sys_migrate(4) 1 2 3 4 • migrate() • migrates application to next node of interest • names nodes by tags • implements routing algorithm • sys_migrate() • one hop migration • used by migrate to implement routing

  32. Routing Example Network 1 2 i Taxi RouteToTaxi = 2 RouteToTaxi = ? RouteToTaxi = j migrate(Taxi){ while(readTag(Taxi) == null) if (readTag(RouteToTaxi)) sys_migrate(readTag(RouteToTaxi)); else create_SM(DiscoverySM, Taxi); createTag(RouteToTaxi, lifetime, null); block_SM(RouteToTaxi, timeout); }

  33. SM Self-Routing • SMs carry the routing and execute it at each node • Routing information stored in tag space • SMs control their routing • Select routing algorithm (migrate primitive) • Multiple library implementations • Implement a new one • Change routing algorithm during execution in response to • Adverse network conditions • Application’s requirements

  34. Self-Routing Simulation Results • Geographic + On-Demand Routing performs better than simple on-demand routing • 25%-40% decrease in completion time • 62%-92% reduction in amount of bytes sent in the network - Less energy and bandwidth consumption starting node node of interest other node

  35. Outline • Motivation • Spatial Programming Model • Smart Messages System Architecture • Implementation Details • Experimental Results • Conclusions

  36. Spatial Programming Implementation Using Smart Messages • SP application translates into an SM • Spatial reference access translates into an SM migration to the mapped node • Embedded system properties: Tags • SM self-routes using geographical routing and content-based routing • Reference consistency • Unique addresses (stored in mapping table) are unique tags created at nodes • SM carries the mapping table

  37. SP to SM Translation: Example Left Hill Right Hill Spatial Reference Access Max_motion = {Left_Hill:motion[1], timeout}.detect; Mapping Table {Left_Hill,motion,1} {yU78GH5,location} ret = migrate_geo(location, timeout); if (ret == LocationUnreachable) ret = migrate_tag(yU78GH5, timeout); if ((ret == OK) && (location == Left_Hill)) return readTag(detect); else throw TimeoutException Smart Message Code Brick

  38. SM Prototype Implementation • Modified version of Sun’s Java K Virtual Machine • Small memory footprint (160KB) • SM and tag space primitives implemented inside virtual machine as native methods (efficiency) • I/O tags: GPS location, neighbor discovery, image capture, light sensor, system status Ad hoc networks of HP iPAQ PDAs running Linux

  39. Lightweight Migration • Traditional process migration difficult • Strong coupling between execution entity and host • Needs to take care of operating system state (e.g., open sockets, file descriptors) • Tag space decouples the SM execution state from the operating system state • SM migration transfers only • Data bricks explicitly specified by programmer • Minimal execution control state required to resume the SM at the next instruction (e.g., instruction pointer, operand stack pointer)

  40. Outline • Motivation • Spatial Programming Model • Smart Messages System Architecture • Implementation Details • Experimental Results • Conclusions

  41. Micro-benchmark Results Cost of data serialization Cost of single hop migration

  42. Experimental Results for Simple Routing Algorithms 8 HP iPAQs running Linux and using IEEE 802.11 for wireless communication user node node of interest intermediate node Routing algorithm Code not cached (ms) Code cached (ms) Geographic 415.6 126.6 On-demand 314.7 506.6 Completion Time

  43. SP Application: Intrusion Detection 10 HP iPAQs with 802.11 cards and GPS devices user node monitored space light sensor camera node regular node

  44. Conclusions • Spatial Programming makes outdoor distributed computing simple • Volatility, mobility, configuration dynamics, ad hoc networking are hidden from programmer • Implementation on top of Smart Messages • Easy to deploy new applications in the network • Quick adaptation to highly dynamic network configurations • Acknowledgments: Liviu Iftode, Ulrich Kremer, Chalermek Intanagonwiwat, Porlin Kang

  45. Other projects based on Smart Messages • Portable Smart Messages: work over unmodified JVMs using bytecode instrumentation (done by Nishkam) • Tested on iPAQs, Ericsson P900, and Nokia Communicator • Context-aware Migratory Services in Ad Hoc Networks (Oriana will present it after this talk) • Spatial Views: A location-aware, declarative programming language (Uli Kremer will present it on Friday)

  46. Two of my current projects • Intelligent distributed transportation systems • Sponsored by NSF, collaboration with Rutgers University (Pravin will talk about it in the afternoon) • Design a vehicular network architecture and build a prototype for distributed computing over vehicular networks • SmartCampus • Sponsored by NSF, joint work with IS & ECE departments at NJIT • Investigate mobile computing applications, protocols, middleware over a campus-wide, location-aware, mobile community test-bed (100s of users with mobile devices)

  47. Thank you! http://www.cs.njit.edu/~borcea/

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