Locating Mobile Agents in Distributed Computing Environment

1. Locating Mobile Agents in Distributed Computing Environment

2. Reference [1] A. Di Stefano, and C. Santoro, “Locating Mobile Agents in a Wide Distributed Environment,” IEEE T-PDS, vol 13, no. 8, Aug 2002, p. 844 – 864.

3. SPC - Proof of Correctness • Temporal evolution of SPC • Finite State Machine • Reachability • Completion

4. Query formulation and response • q(r,m,t): Query q that returns the location of agent named m and registered at r (SAR or RAR) at time t. • Result could be • found, successful completion of location phase • wait, while agent is migrating.

5. Inter region migration

6. Location Finding Protocol: FSM – Figure 9 No cycles. States are reachable.All paths from initial state end at the final state (S1→ S11). So agent can always be found.

7. Three cases of interaction & interregion migration • Interaction before interregion migration S1 → S2 → S5 → S11 • Interaction after interregion migration S1 → S4 → S8 → S11 • Interaction concurrently with an interregion migration

8. Intraregion Migration • FSM shows that before reaching λl the protocol finds λl.region; so the correctness proof requires confirmation that location of the correct RAR leads to the correct SAR and agent location.

9. Intraregion migration

10. Interregion – FSM – Fig 10 • Interaction before intraregion migration S1 → S2 → S7 • Interaction after intraregion migration S1 → S4 → S7 • Interaction concurrently with an intraregion migration

11. RAR RAR: m in region SAR SAR SAR SAR (m) SAR () Protocol Termination Condition • Single interaction: single migration (considered) • Several interactions overlap single migrations (handled by SAR lock) • Single interaction overlap with several migrations, • e.g. finding phase overlaps 2 migration • RAR points to SAR, but agent migrates before SAR is queried • Chasing an agent – conditions to catch up? SAR(m) SAR(m) Location queries Agent migration

12. Query propagation • If agent has migrated, instead of SAR/RAR sending query reply propogate query to the next location. Figure 11: Query propogation Figure 12: Time intervals

13. Agent α motion λ1, λ2, λ3, …. • Agent β at λwants interaction with α • At t location query sent to SAR λi . • At t – δ; αbegins to migrate to λi+1; query sent to RAR λi.reg before t – δ. • To catchup to the agent the query to SAR λi+1 has to be before α starts a new migration. On iteration this yields: Termination condition derivation Figure 12: Time intervals (14) is hard to compute. Two simplified conditions emerge.

14. Figure 13 Notation

15. Availability Comparison • Availability = MTTF/(MTTF+MTTR) • MTTF = 1/Λ, where Λ = average fault rate • Compute fault rate for migration and interaction phases • Migration fault rate = Agent transfer fault rate + location updating fault rate • Database logging (DL) approach (Figure 17) • Path Proxies (PP) approach (Figure 18) • SPC approach (Figure 19) • Fault rate of SAR for intraregion migration • Fault rate of a generic site (SAR) • Fault rate of SAR and RAR for interregion migration • Fault rate of a generic site (SAR) + • Fault rate of a specialized site (e.g. ANS) • Fault rate of a generic site on a LAN (RAR) • Ratio of interregion to intraregion migration • If source site fails then migration also fails. So ignore the source site failure. (Figure 20)

16. Availability Comparisons

17. Location interaction fault rate • Database Logging – access the location database (Eq 21) • Path Proxies – complete path from the birth location has to be accessed (Eq 22) • Search by Path Chase – accesses involved updating during the migration processes; best case – all updates; worst case – only mandatory updates (Eq 23). • General solution depends on the number of regions kr and number of locations ks in the search path. (Minimum vlues are 2 and 0 respectively)

18. Interaction fault rates

20. Discussion • What about path length? • Is fault rate uniform, e.g. WAN vs LAN? • Another approach to assess availability?

21. Scalability • With respect to number of agents n(t), and number of agent migrations s(i,t). • Impact the network usage UN • Function of the number of agents • If there is no congestion (i.e. messages per link are acceptable), then focus on US. • SPC db is distributed. We would require a distributed db for DL. • Impacts site usage US. • Function of the number of entries in all the databases (including proxy objects) • Improve performance (Fig 15) by • Limiting of agents (db entries) per SAR. • FIFO is used to limit agents per SAR.

22. Scalability comparisons

23. Fig. 15 Site usage (scalability)

24. Summary