Locating Mobile Agents in Distributed Computing

Environment

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.

SPC - Proof of Correctness

• Temporal evolution of SPC

• Finite State Machine

• Reachability

• Completion

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.

Inter region migration

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.

Three cases of interaction & interregion migration

• Interaction before interregion migrationS1 → S2 → S5 → S11

• Interaction after interregion migrationS1 → S4 → S8 → S11

• Interaction concurrently with an interregion migration

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.

Intraregion migration

Interregion – FSM – Fig 10

• Interaction before intraregion migrationS1 → S2 → S7

• Interaction after intraregion migrationS1 → S4 → S7

• Interaction concurrently with an intraregion migration

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?

Location queries

Agent migration

SAR

RAR: m in region

SARSAR (m)

SAR

RAR

SAR(m)

SAR ()SAR(m)

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

Termination condition derivation

Figure 12: Time intervals

(14) is hard to compute. Two simplified conditions emerge.

On iteration this yields:

•Agent α motion λ1, λ2, λ3, …. •Agent β at λ wants interaction with α

•At t location query sent to SAR λi .•At t – δ; α begins 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.

Figure 13 Notation

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)

Availability Comparisons

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)

Interaction fault rates

Discussion

• What about path length?

• Is fault rate uniform, e.g. WAN vs LAN?

• Another approach to assess availability?

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.

Scalability comparisons

Fig. 15 Site usage (scalability)

Summary