1. ISP and Egress Path Selection for Multihomed Networks
Amogh Dhamdhere
Constantine Dovrolis
Networking and Telecommunications Group
College of Computing
Georgia Tech

2. Multihoming Multihoming: Connection of a stub network to multiple ISPs
70% of stub networks are multihomed
Redundancy
primary/backup relationships
Load Balancing
Distribute outgoing traffic among ISPs
Cost Effectiveness
Lower cost ISP for bulk traffic, higher cost ISP for performance-sensitive traffic
Performance
Intelligent Route Control
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

3. Major Questions How to select the set of upstream ISPs ?
Low monetary cost
Good performance (low delay, loss rate)
Path diversity to major traffic destinations – improves robustness to network failures
How to allocate egress traffic to the set of selected ISPs ?
Objective: Avoid congestion on the upstream paths
Also maintain low cost
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

4. Related Work Wang et al. (Infocom 2004)
ISP selection to minimize cost to subscriber
Did not consider performance constraints and path diversity
Applicable only to percentile based charging pricing function
IRC systems (RouteScience, Internap, Radware..)
Path switching for better performance
Work on short timescales – Can lead to oscillations
Goldenberg et al. (Sigcomm 2004)
IRC algorithm for optimizing latency and cost over short timescales
Applicable to the percentile based charging model
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

5. Problem Definition Two phase problem Phase I – ISP Selection:
Select K upstream ISPs
K depends on monetary and performance constraints
“Static” operation
Change only when major changes in the traffic destinations or ISP pricing
Phase II – Egress Path Selection
Allocate egress traffic to selected ISPs
Avoid long term congestion and minimize cost
“Semi-static” operation, performed every few hours or days
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

6. Assumptions We provision only the egress traffic of S
The set of M major destinations is known
Average rates to the major destinations are known
Number of ISPs to choose (K) and the set of possible ISPs (I) is known
An ISP charges based on the volume of traffic routed through it (volume based charging)
Assume increasing and concave pricing functions
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

7. Objectives of ISP selection
ISP selection should consider both monetary cost and performance
Minimum monetary cost
Estimate the cost that “would be” incurred if a set of ISPs was selected
Minimum AS-level path lengths
Longer paths can lead to larger delays and increase vulnerability to inter-domain routing failures
AS-level paths can be measured offline using Looking Glass Servers
Maximum Path diversity
AS-level paths to destinations should be as “different” as possible
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

8. ISP Selection K ISPs to be selected out of |I|
Associate a cost with each performance metric
Monetary cost, path length cost and path diversity cost
Total cost of a selection of ISPs C:
ct(C): = αmcm(C) + αpcp(C) + αdcd(C)
Optimization problem: Find the set C* with the minimum total cost
Brute Force approach is feasible
E.g. For |I|=15 and K=4, there are 1365 combinations
Solution approach: Evaluate the cost of each selection and choose the set with the minimum cost
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

9. Monetary and Path Length Cost
Lower level optimization problem: Given a set of ISPs C, what is the minimum monetary and path length cost of routing egress flows ?
Find the mapping G* of items to bins that minimizes the cost of the assignment (Bin Packing)
Flows = items
ISPs = bins
To find: Least cost assignment of flows to ISPs
NP hard !
Use First Fit Decreasing (FFD) heuristic
Generated mapping G* very close to optimal
Monetary and path length costs of C are then calculated using the mapping G*
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

10. Path Diversity Cost A selection C gives K paths to each destination d
K-shared link to d: A link which is shared by all K paths to d
If a K-shared link fails, destination d is unreachable
Minimize the number of K-shared links
Should give best performance for single-link failures
Define metric k(d,C): number of K-shared links to d in selection C
Choose the selection with the minimum k(d,C) averaged over all destinations
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

11. Evaluation - Bin Packing
FFD heuristic used to find the minimum monetary and path length costs
Simulations
Need exhaustive search to identify optimal cost
Restrict network to 3 ISPs and 15 destinations
FFD heuristic finds a solution with high probability, when average load is below 60-70%
In high load conditions, the probability of finding a solution decreases
Cost ratio is close to 1, even at high load conditions
FFD heuristic is close to the optimal in terms of cost
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

12. Evaluation – Path Diversity
AS-level paths and traffic rates are input to simulator
9 ISPs, 250 destinations
Given K, find the selection C* with the minimum path diversity cost
For each selection C, u(C) = total traffic lost due to the failure of each link in topology
Calculate Δu(C) = u(C) – u(C*) for each selection C
Replace graphs with new graphs increasing the line thickness
Possibly color ?
Single link failures: C* is the optimal selection
2,3 link failures: C* is close to the optimal selection
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

13. Cannot know a priori whether a mapping will cause congestion
Egress Path Selection
After Phase-I, S has K upstream ISPs
Problem: How to map outgoing traffic to the ISPs
M flows: KM mappings of flows to ISPs
Some mappings may cause congestion to flows !
Flows can be congested at access links or further upstream
Objective: Find the loss-free mapping with the minimum cost
Challenges:
Upstream topology and capacities are unknown
Cannot know a priori whether a mapping will cause congestion
Iterative routing approaches required
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

14. Egress Path Selection
Step 1: Use the FFD heuristic to map flows to ISPs
Assume initially that the access links are bottlenecks
Access capacities are known
FFD heuristic for bin packing gives a cost close to the best possible cost
Some flows may be congested
Bottlenecks in upstream networks
Use as the starting point for the stochastic search
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

15. Egress Path Selection
Step 2: Use iterative stochastic search to find a loss-free solution
Stochastic search by simulated annealing
Iterative combinatorial optimization algorithm
Route traffic, measure congestion, decide next action
Action: Flows re-routed from one ISP to another
can accept moves which increase congestion
Accepting “bad” moves can help to escape local minima
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

16. Evaluation of Stochastic Search
Stochastic search involves iterative routing
Traffic has to be re-routed
Some traffic may be dropped due to congested links
Evaluation metrics
Probability of finding a solution (high)
Number of iterations to find a solution (low)
Amount of traffic re-routed (low)
Amount of dropped traffic (low)
Compare against other heuristics
Only bin packing (access-link)
Greedy iterative algorithm (greedy-single)
Moves which increase congestion are not accepted
Variants of Simulated Annealing
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

17. Evaluation of Stochastic Search
SA-slow and greedy show similar probability of finding a solution
Other algorithms have a significantly lower probability of finding a solution
On the average, SA-slow needs fewer iterations to find a feasible solution
Accepting “worse” solutions can actually help find a loss-free solution faster
SA drops less traffic on the average than greedy-single
SA re-routes less traffic on the average than greedy-single
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

18. Summary of Contributions
Proposed practical algorithms for ISP selection and egress traffic allocation among selected ISPs
ISP selection algorithm takes into account both monetary and performance constraints
Formulated as a bin-packing problem
Applicable for general pricing functions
Can be extended to incorporate more performance metrics
Egress path selection without knowledge of upstream topology
Proposed simulated annealing for stochastic search
Performs better than other simple iterative algorithms
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

19. Thank You !
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

20. Evaluation of Bin Packing
Simulations
Need exhaustive search to identify optimal cost
Restrict network to 3 ISPs and 15 destinations
FFD-like heuristic finds a solution with high probability, when average load is below 60-70%
In high load conditions, the probability of finding a solution decreases
Cost ratio is very close to 1, even at high load conditions
Heuristic algorithm is close to the optimal in terms of cost
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

21. Stochastic Search – Probability of finding a solution
bneck_loc=0.5 (bottlenecks in the middle of network) and bneck_shar=0 (no shared bottlenecks)
SA-slow and greedy-single show similar probability of finding a solution
Access-link, greedy-mult and SA-fast show significantly lower probability of finding a solution
Henceforth, compare only greedy-single and SA-slow
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

22. Stochastic Search – Number of Iterations
How many iterations before solution is found ?
Number of iterations required increases with offered load (average flow rate)
SA-slow performs better than greedy-single on the average
Accepting solutions with increasing congestion can actually help find a non-congested solution quicker
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

23. Stochastic Search – Results
What is the total rate of traffic that is re-routed ?
What is the total rate of traffic that is dropped due to congestion ?
SA-slow drops less traffic on the average than greedy-single
SA-slow re-routes less traffic on the average than greedy-single
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006

24. Assumptions Provision the egress traffic of S
The set of M major destinations is known
Average rates to the major destinations are known
Number of ISPs to choose (K) and the set of possible ISPs (I) is known
An ISP charges based on the volume of traffic routed through it (volume based charging)
Assume increasing and concave pricing functions
9/20/2018
Amogh Dhamdhere
IEEE Infocom 2006