1 Cooperative Computing for Distributed Embedded Systems* Cristian Borcea, Deepa Iyer, Porlin Kang, Akhilesh Saxena, and Liviu Iftode Division of Computer and Information Sciences Rutgers University * Work supported in part by the NSF under the ITR grant ANI

2 Programming Challenge How to program ? A single computer Parallel computers Networks of computers Next challenge: Networks of Embedded Systems (NES) How to program NES to perform distributed tasks ?

3 Networks of Embedded Systems (NES) Large scale, ad hoc networks Heterogeneous node functionality: sensors, intelligent cameras, smart appliances Limited resources: CPU, memory, bandwidth, energy Wireless Communication: , Bluetooth Volatile, possibly mobile Wildlife Monitoring Intelligent Highways Collaborative Robots Sensor Networks

4 Sensor Networks Programmability issues: How to add a new application ? How to execute user-defined applications ? Our goal: a general programming model for NES Data dissemination Data collection

5 Traditional Distributed Computing Does Not Work for NES Traditional distributed computing Stable configuration of functionally homogeneous nodes Fixed-address naming and routing (e.g., IP) Failures are exceptions Networks of Embedded Systems Ad hoc configurations of nodes with different properties Content-based naming and routing Volatile nodes are common

6 Outline Motivation Cooperative Computing Smart Messages (SM) Tag Space Node Architecture & SM Lifecycle API & Examples Prototype & Simulation Results Conclusions

7 Execution Migration Example Data migration Bob’s dinner: Appetizer Entree Dessert Execution migration

8 Cooperative Computing Distributed computing over NES based on execution migration Smart Messages (SM) User-defined applications which migrate through the network Execute at each node in the path Application executes at nodes of interest Migration occurs between nodes of interest Tag Space Name-based node memory Persistent across SM executions Used by SM for addressing, storage, synchronization, I/O access

9 Smart Messages (SM) Code Bricks: application & routing code Data Bricks: mobile data carried by SM during migration Execution State: used for execution migration Signature: access control to Tag Space Resource Table: tags to be accessed, memory, CPU cycles, and generated traffic estimates Code Bricks Data Bricks Execution State Signature Resource Table

10 Tag Space Tag structure Application tags Created by SMs I/O tags Provide interface to OS and I/O system Name Signature Lifetime Data Appetizer SM_Sign 10 hours 5$ Neighbors Node1,Node2

11 Node Architecture Admission Manager Virtual Machine Tag Space OS & I/O SM ArrivalSM Migration

12 Admission Control At arrival each SM presents its resource requirements Admission manager decides whether or not to accept an SM Each accepted SM becomes a task ready for scheduling Optimization: code bricks are cached upon SM acceptance

13 Execution Takes place over a virtual machine (VM) Non-preemptive, but time bounded Interrupted by SM admission May block on tags to be updated Terminate or continue on another node (SM migration)

14 Migration Appetizer $5Entree $10 migrate_SM is a user-level library call Implements content-based routing using A one-hop migration primitive: sys_migrate Captures execution state, transfers SM one hop, resumes execution Routing tags eat(Appetizer); migrate_SM(Entree); eat(Entree); Network eat(Entree); eat(Appetizer); migrate_SM(Entree);

15 Self Routing 1 Entree $ route_to_Entree 3 migrate_SM(Entree, timeout) sys_migrate(2)sys_migrate(3)sys_migrate(4) route_to_Entree 2route_to_Entree 4 Application controls routing in two ways: Can choose among multiple migrate_SM implementations Can change its routing algorithm during execution

16 Cooperative SMs if (!route_to_Entree) create_SM(DiscoverySM, Entree); block_SM(route_to_Entree); sys_migrate(next_hop); Route_to_Entree ? Network DiscoverySM if (!route_to_Entree) create_SM(DiscoverySM, Entree); block_SM(route_to_Entree); sys_migrate(next_hop);  Applications can be dynamic collections of SMs cooperating to perform a distributed task route_to_Entree next_hop

17 Smart Messages API Operations on Tag Space createTag(name, lifetime, data), deleteTag(name) readTag(name), writeTag(name, value) SM Creation create_SM(code_bricks, data_bricks) SM Spawning spawn_SM() SM Synchronization block_SM(tag_name, timeout) SM Migration migrate_SM(tag_names, timeout), sys_migrate(next_hop)

18 Code Example migrate_SM(appetizer, timeout); eat(appetizer); migrate_SM(entree, timeout); eat(entree); migrate_SM(dessert, timeout); eat(dessert); migrate_SM(home, timeout); while(!readTag(tag)) if (readTag(route_to_tag)) sys_migrate(readTag(route_to_tag)); else create_SM(DiscoverySM, tag); block_SM(route_to_tag, timeout); Application: migrate_SM: //forward do{ sys_migrate(All_Neighbors); createTag(prev_to_tag, lifetime, prev_hop()); }while(!(readTag(tag) || readTag(route_to_tag))); //backward do{ sys_migrate(readTag(prev_to_tag)); createTag(route_to_tag, lifetime, prev_hop()); }while(readTag(prev_to_tag)); writeTag(route_to_tag, prev_hop()); DiscoverySM:

19 Evaluation goals Expressiveness Ability to implement various user-defined distributed applications Performance Cost of execution migration vs. data migration Practicality SM prototype

20 Prototype Implementation Compaq’s iPAQs running Linux & Bluetooth for wireless communication Modified version of Sun’s Java KVM

21 Micro-benchmark Results Tag Space Operation Time (microsec) createTag 55.8 deleteTag30.8 readTag25.0 writeTag28.0 block_SM24.6  Processor: 206 MHz Intel StrongARM  Bandwidth: 11 Mbps (802.11), 1Mbps (Bluetooth)  Single SM execution with one-hop discovery  SM: 829 bytes (731 code, 20 data, 78 stack)  55.2 ms with  ms with Bluetooth

22 Simulation Event-driven simulator extended with support for SM execution Accounts for both communication and computation time Accurate measurements for execution time by counting at the VM level the number of cycles per VM instruction Each node is simulated by a Java thread Thread scheduling controlled by simulator Implemented two previously proposed applications for sensor networks using SMs: Data collection: Directed Diffusion [Estrin ’99] Data dissemination: SPIN [Heinzelman ’99]

23 Smart Messages vs. Message Passing Directed DiffusionSPIN

24 Related Work Mobile Agents Active Networks Mobile ad hoc networking Pervasive Computing Sensor Networks

25 Conclusions Propose Cooperative Computing and Smart Messages for programming Networks of Embedded Systems (NES) Implement and evaluate two SM distributed applications Smart Messages offer a promising programming model for NES SM software distribution to be released this summer

26 Future Work Security Distributed trust model for SM Energy Use the simulator to design and analyze energy efficient routing algorithms Spatial Programming with SM We propose it as a novel paradigm for programming NES Network resources represented as {space:tag} spatial references Translation from SP programs to SM programs

27 Thank you !

28 SM Admission Protocol resource table and code brick IDs IDs for code bricks not cached at destination data bricks and missing code bricks insert task in Ready Queue SourceDestination