1. Nomadic Service Points Edward Bortnikov Israel Cidon Idit Keidar

2. Distributed Service Grids Paradigm shift in networked service infrastructures –Geographically distributed servers instead of centralized server farms Performance benefits –Localized service provisioning –Adaptation to changes (e.g., user mobility) Technologies in the field –Content delivery networks (CDN), wireless access (mesh) networks (WMN), online gaming grids

3. Nomadic Service Points Abstraction of session attachment to service –Application is unaware of physical server assignment Implemented by service infrastructure –A single physical server is selected for the session –A session can be migrated to sustain QoS Example: session mobility following host mobility Optimization challenges: –When to transition from an old server? –Which server to select next?

4. Wireless Mesh Networks Tradeoff: suboptimal delay versus service interruption Tradeoff: suboptimal delay versus service interruption

5. Distributed Groupware Service Tradeoff: suboptimal delay versus state transfer Tradeoff: suboptimal delay versus state transfer

6. Model Hold cost –Paid each time slot to the currently assigned server –A server can update its hold cost on slot boundary Setup cost (C) –Paid every time the session is (re-)assigned Total cost = setup + hold Goal: finding the assignment schedule –… that minimizes the total cost over time

7. Formal Approach Online optimization goal: competitive ratio –Worst-case ratio versus the offline algorithm Notation: –k: number of servers –I: problem input (sequence of hold cost vectors) –σ: schedule produced by the online algorithm –σ*: schedule produced by the optimal (offline) algorithm Algorithm ALG is r(ALG) competitive iff for every finite I,

8. Summary of Results Theoretical –A lower bound of k on deterministic online algorithms –A 2k-competitive online algorithm DTrack-RR –Optimal offline solution in linear time and space Practical –Opportunistic versions of DTrack –Empirically verified through simulation Average performance within 20%-50% from the optimum Perfect scalability with network growth –Motion prediction helps a lot Performance gap below 10% with a small look-ahead window

9. The DTrack Algorithm DTrack = deficit tracker Initially, assign to server s with minimal hold cost Every time slot do: –Update the deficit between s and the other servers How much less would I have paid if I transitioned to s’ at some time after transitioning to s? –Single-slot lookahead When deficit between s and some s’ is about to become “big enough” (above C) When deficit between s and some s’ is about to become “big enough” (above αC) –Transition to a new server

10. Transition Policies Round-Robin DTrack-RR –Cyclic space of server ids –The a-priori deficit of the choice must not exceed C ( –The a-priori deficit of the choice must not exceed αC (α ≥ 0) Forward opportunistic DTrack-F –Minimal current hold cost Backward opportunistic DTrack-B –Round-robin between servers with “big enough” deficit values (exceeding C, -∞ < ≤ –Round-robin between servers with “big enough” deficit values (exceeding βC, -∞ < β ≤ α) – = –β = α is most aggressive Picks the server with largest deficit

11. Execution of DTrack 1 3213 t=0 hold setup = 5 α = 1 current leader other deficit

12. Execution of DTrack Execution of DTrack 42 05442 t=1 hold deficit setup = 5 α = 1 current leader other

13. Execution of DTrack Execution of DTrack 3 12202 t=2 hold deficit setup = 5 α = 1 current leader other

14. Execution of DTrack 64 14830 t=3 Transition! hold deficit setup = 5 α = 1 current leader other

15. Execution of DTrack Execution of DTrack 314 14830 hold deficit DTrack-RR setup = 5 α = 1 current leader other

16. Execution of DTrack 1 14830 hold deficit DTrack-B setup = 5 α = 1 β = 0 current leader other

17. Execution of DTrack 14830 hold deficit DTrack-F setup = 5 α = 1 current leader other

18. Efficiency Improvement CTrack = cost tracker Track accumulated cost instead of deficit –Transition if hold cost exceeds αC –Apply the same transition policies Advantage: reduced complexity –O(1) at each step instead of O(n) Disadvantage: weaker competitive ratio –(2+a)k, where hold(s,t) ≤ αC for each s

19. Negative Results No deterministic algorithm can be better than k- competitive –Hence, DTrack-RR is at most twice the lower bound! Neither DTrack-F nor aggressive DTrack-B are competitive –An Ω(C) lower bound What do we care for in real life? –The average performance ratio –Luckily, we have an optimal algorithm as a baseline

20. WMN: Average Total Cost Hold cost = Distance RWP movement Area = 1km x 1km 100 routers Scaling up by 25 Speed = 10 m/sec Setup cost = 50

21. WMN: Average Performance Ratio Hold cost = Distance RWP movement Area = 1km x 1km 100 routers Scaling up by 25 Speed = 10 m/sec Setup cost = 50

22. Motion Aware Algorithms TargetAware –Requires info on the node’s target + speed –Applies OPT as a subroutine DirectionAware –Requires info on the node’s direction + speed –Applies TargetAware as a subroutine

23. WMN: Motion Aware Algorithms Hold cost = Distance RWP movement Area = 1km x 1km 100 routers Scaling up by 25 Speed = 10 m/sec Setup cost = 50

24. Wide-Area Chatroom Static users Poisson arrivals 3 users 100 servers Scaling up by 25x25

25. Backup Slides

26. DTrack-RR is 2k-Competitive Overtake = leave or skip the server Consider σ (by DTrack-RR) and σ* (by OPT) –Round = period in which σ* does not move –Phase = part of round where σ overtakes every server

27. DTrack-RR is 2k-Competitive (cont’d) Competitive analysis within a round –Lookahead accounting is crucial! Hold cost bookkeeping –σ overtakes σ*’s choice every phase The hold cost incurred by σ* is at least C The hold cost incurred by σ* is at least αC The hold cost incurred by σ exceeds it by at most (k-1)C The hold cost incurred by σ exceeds it by at most (k-1)αC Setup cost bookkeeping –σ* pays at least C (initial assignment) –σ pays at most kC every phase Summing up and bounding the ratio – –α=1 minimizes the upper bound (2k)