# Optimizing Cost and Performance in Online Service Provider Networks

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5 default to d k via path(dc i, link j ). The problem of computing cost can then be described as: Cost turning pt. min pseudocost = X j (price j X k X (f kij vol k )), i wrtt subject to: Figure 3: The cost-performance tradeoff in TE strategy space. 4.2 Computing optimal TE strategies We now present our optimization framework that uses the cost and performance information to derive the desirable TE strategy for an OSP. We first assume the traffic to a destination prefix can be arbitrarily divided among multiple alternative paths and obtain a class of optimal TE strategies. In this class of strategies, one cannot improve performance without sacrificing cost or vice versa. Second, we describe how we select a strategy in this class that best matches the cost-performance trade-off that the OSP desires. Third, since in practice the traffic to a prefix cannot be arbitrarily split among multiple alternative paths, we devise an efficient heuristic to find an integral solution that approximates the desired fractional one Searching for optimal strategy curve Given a TE strategy, we can plot its cost and performance (weighted average RTT or wrtt) on a 2-D plane. This is illustrated in Figure 3 where each dot represents a strategy. The number of strategies is combinatorial, N a p for N N p prefixes and N a alternative paths per prefix. A key observation is that not all strategies are worth exploring. In fact, we only need to consider a small subset of optimal strategies that form the lower-left boundary of all the dots on the plane. A strategy is optimal if no other strategy has both lower wrtt and lower cost. Effectively, the curve connecting all the optimal strategies forms an optimal strategy curve on the plane. To compute this curve, we sweep from a lower bound on possible wrtt values to an upper bound on possible wrtt values at small increments, e.g., 1 ms, and compute the minimum cost for each wrttvalue in this range. These bounds are set loosely, e.g., the lower bound can be zero and the upper bound can be ten times the wrtt of the default strategy. Given a wrt T R in this range, we compute the minimum cost using linear programing (LP). Following the notations in Figure 2, let f kij be the fraction of traffic to d k that traverses path(dc i, link j ) and rtt kij be the RTT X X (f kij vol k ) µ cap j (1) k i X X X (f kij vol k rtt kij ) X vol k R (2) k i j k X X f kij = 1 (3) i j k, i, j f kij 1 (4) Condition 1 represents the capacity constraint for each external link and µ is a constant (by default.95) that reserves some spare capacity to accommodate potential traffic variations for online TE. Condition 2 represents the wrt T constraint. Condition 3 ensures all the traffic to a destination is carried. The objective is to find feasible values for variables f kij that minimize the total pseudo cost. Solving such an LP for all possible values of R and connecting the TE strategy points thus obtained yield the optimal strategy curve Selecting a desirable optimal strategy Each strategy on the optimal strategy curve represents a particular tradeoff between performance and cost. Based on its desired tradeoff, an OSP will typically be interested in one or more of these strategies. Some of these strategies are easy to identify, such as minimum cost for a given performance or minimum wrtt for a given cost budget. Sometimes, an OSP may desire a more complex tradeoff between cost and performance. For such an OSP, we take a parameter K as an input. This parameter represents the additional unit cost the OSP is willing to bear for a unit decrease in wrtt. The desirable strategy for a given K corresponds to the point in the optimal strategy curve where the slope of the curve becomes higher than K when going from right to left. More intuitively, this point is also the turning point or the sweet spot when the optimal strategy curve is plotted after scaling the wrtt by K. We can automatically identify this point along the curve as the one with the minimum value of pseudocost + K wrtt. 5

10 1 (a) alternative paths shorter than default path (b) alternative paths cheaper than default path 1 1 (c) both shorter and cheaper Fraction of prefixes K single-loc..2 14K other 4K rand Number of paths Fraction of prefixes K single-loc..2 14K other 4K rand Number of paths Fraction of prefixes K single-loc..2 14K other 4K rand Number of paths Figure 8: Number of alternative paths that are better than the default path in the set of 6K single-location prefixes, the set of 14K other high-volume prefixes, and the set of 4K randomly selected low-volume prefixes. difference between rtt 5 and rtt 2 of all paths. It turns out that rtt 2 are indeed very close to rtt 5. The difference is under 1 ms and 5 ms in 78% and 92% of the cases respectively. 7 Results In this section, we demonstrate and explain the benefits of online TE optimization in MSN. We also study how the TE optimization results are affected by a few key parameters in Entact, including the number of DCs, number of alternative routes, and TE optimization window. Our results are based on one-week of data collected in September 29, which allows us to capture the time-ofday and day-of-week patterns. Since the traffic and performance characteristics in MSN are usually quite stable over several weeks, we expect our results to be applicable to longer duration as well. Currently, the operators of MSN only allow us to inject /32 prefixes into the network in order to restrict the impact of Entact on customer traffic. As a result, we have limited capability in implementing a non-default TE strategy since we cannot arbitrarily change the DC selection or route selection for any prefix. Instead, we can only simulate a non-default TE strategy based on the routing, performance and traffic data collected under the default TE strategy in MSN. When presenting the following TE optimization results, we assume that the routing, performance and traffic to each prefix do not change under different TE strategies. This is a common assumption made by most of the existing work on TE [9, 12, 15]. We hope to study the effectiveness of Entact without such restrictions in the future. 7.1 Benefits of TE optimization Figure 9 compares the wrtt and cost of four TE strategies, including the default, Entact 1 (K = 1), Lowest- Cost (minimizing cost with K = ), and BestPerf (minimizing wrtt with K = inf). We use 2-minute TE Cost (per unit traffic) default LowestCost BestPerf Entact 1 Entact 1, frac. offline Entact 1, int. offline wrtt (msec) Figure 9: Comparison of various TE strategies. window and 4 alternative routes from each DC for TE optimization. The x-axis is the wrtt in milliseconds and the y-axis is the relative cost. We cannot reveal the actual dollar cost for confidentiality reason. There is a big gap between the default strategy and Entact 1, which indicates the former is far from optimal. In fact, Entact 1 can reduce the default cost by 4% without inflating wrtt. This could lead to enormous amount of savings for MSN since it spends tens of millions of dollars a year on transit traffic cost. We also notice there is significant tradeoff between cost and performance among the optimal strategies. In one extreme, the LowestCost strategy can eliminate almost all the transit cost by diverting traffic to free peering links. But this comes at the expense of inflating the default wrtt by 38 ms. Such a large RTT increase will notably degrade user-perceived performance when amplified by the many round trips involved in downloading content-rich Web pages. In the other extreme, the BestPerf strategy can reduce the default wrtt by 3 ms while increasing the default cost by 66%. This is not an appropriate strategy either given the relatively large cost increase and small performance gain. Entact 1 appears to be at a sweet-spot between the two extremes. By exposing the performance and cost of various opti- 1

11 path type prefix wrtt (ms) pseudo cost same 88.2% pricier, longer.1% pricier, shorter 4.6% cheaper, longer 5.5% cheaper, shorter 1.7% wrtt (msec) wrtt Utility Cost Table 2: Comparison of paths under the default and Entact 1 strategies in terms of performance and cost. 1 default closest 1 closest 2 closest 3 all(11) path type prefix non-default DC, default route 2.1% non-default DC, non-default route 2.5% default DC, non-default route 7.2% Table 3: Comparison of paths under the default and Entact 1 strategies in terms of DC selection and route selection. mal strategies, the operators can make a more informed decision regarding which is a desirable operating point. To better understand the source of the improvement offered by Entact 1, we compare Entact 1 with the default strategy during a typical 2-minute TE window. Table 2 breaks down the prefixes based on their relative pseudo cost and performance under these two strategies. Overall, the majority (88.2%) of the prefixes are assigned to the default path in Entact 1. Among the remaining prefixes, very few (.1%) use a non-default path that is both longer and pricier than the default path (which is well expected). Only a small number of prefixes (1.7%) use a non-default path that is both cheaper and shorter. In contrast, 1.1% of the prefixes use a non-default path that is better in one metric but worse in the other. This means Entact 1 is actually making some intelligent performance-cost tradeoff for different prefixes instead of simply assigning each prefix to a better non-default path. For instance, 4.6% of the prefixes use a shorter but pricier non-default path. While this slightly increases the pseudo cost by 42.1, it helps to reduce the wrtt of these prefixes by 14.5 ms. More importantly, it frees up the capacity on some cheap peering links which can be used to carry traffic for certain prefixes that incur high pseudo cost under the default strategy. 5.5% of the prefixes use a cheaper but longer non-default path. This helps to drastically cut the pseudo cost by 56.5 at the expense of a moderate increase of wrtt (12.2 ms) for these prefixes. Note that Entact 1 may not find a free path for every prefix due to the performance and capacity constraints. The complexity of the TE strategy within each TE window and the dynamics of TE optimization across time underscore the importance of employing an automated TE scheme like Entact in a large OSP. Figure 1: Effect of DC selection on TE optimization. (Utility and cost are scaled according to wrtt of the default strategy.) Table 3 breaks down the prefixes that use a non-default path under Entact 1 during the 2-minute TE window by whether a non-default DC or a non-default route from a DC is used. Both non-default DCs and non-default routes are used under Entact 1 4.6% of the prefixes use a non-default DC and 9.7% of them use a non-default route from a DC. Non-default routes appear to be more important than non-default DCs in TE optimization. We will further study the effect of DC selection and route selection in 7.2 and 7.3. Figure 9 shows that the difference between the integral and fractional solutions of Entact 1 is negligibly small. In TE optimization, the traffic to a prefix will be split across multiple alternative paths only when some alternative paths do not have enough capacity to accommodate all the traffic to that prefix. This seldom happens because the traffic volume to a prefix is relatively small compared to the capacity of a peering link in MSN. We also compare the online Entact 1 with the offline one. In the latter case, we directly use the routing, performance, and traffic volume information of a 2-minute TE window to optimize TE in the same window. This represents the ideal case where there is no prediction error. Figure 9 shows the online Entact 1 incurs only a little extra wrtt and cost compared to the offline one (The two strategy points almost completely overlap). This is because the RTT and traffic to most of the prefixes are quite stable during such a short period (e.g., 2 minutes). We will study to what extent the TE window affects the optimization results in Effects of DC selection We now study the effects of DC selection on TE optimization. A larger number of DCs will provide more alternative paths for TE optimization, which in turn should lead to better improvement over the default strategy. 11

12 Nonetheless, this will also incur greater overhead in RTT measurement and TE optimization. We want to understand how many DCs are required to attain most of the TE optimization benefits. For each prefix, we sort the 11 DCs based on the RTT of the default route from each DC. We only use the RTT measurements taken in the first TE window of the evaluation period to sort the DCs. The ordering of the DCs should be quite stable and can be updated at a coarse-granularity, e.g., once a week. We develop a slightly modified Entact n k which only considers the alternative paths from the closest n DCs to each prefix for TE optimization. Figure 1 compares the wrtt, cost, and utility ( 4.2.2) of Entact n 1 as n varies from 1 to 11. We use 4 alternative routes from each DC to each prefix. Given a TE window, as n changes, the optimal strategy curve and the optimal strategy selected by Entact n 1 will change accordingly. This complicates the comparison between two different Entact n 1 s since one of them may have higher cost but smaller wrtt. For this reason, we focus on comparing the utility for different values of n. As shown in the figure, Entact 1 1 (only with route selection but no DC selection) and Entact 2 1 can cut the utility by 12% and 18% respectively compared to the default strategy. The utility reduction diminishes as n exceeds 2. This suggests that TE optimization benefits can be attributed to both route selection and DC selection. Moreover, selecting the closest two DCs for each prefix seems to attain almost all the TE optimization benefits. Further investigation reveals that most prefixes have at most two nearby DCs. Using more DCs generally will not help TE optimization because the RTT from those DCs is too large. Note that the utility of Entact 11 1 is slightly higher than that of Entact 2 1. This is because the utility of Entactn k is computed from the 95% traffic cost during the entire evaluation period. However, Entact n k only minimizes pseudo utility computed from pseudo cost in each TE window. Even though the pseudo utility obtained by Entact n k in a TE window always decreases as n grows, the utility over the entire evaluation period may actually move in the opposite direction. 7.3 Effects of alternative routes We evaluate how TE optimization is affected by the number of alternative routes (m) from each DC. A larger m will not only offer more flexibility in TE optimization but also incur greater overhead in terms of route injection, optimization, and RTT measurement. In this experiment, we measure the RTT of 8 alternative routes from each DC to each prefix every 2 minutes for 1 day. Figure 11 illustrates the wrtt, cost, and utility of Entact 1 under different m. For the same reason as in the previous section, we focus on comparing utility. As m grows wrtt (msec) wrtt Utility Cost default all(8) Figure 11: Effect of the number of alternative routes on TE optimization. wrtt (msec) wrtt Utility Cost default 2min 4min 1hr 2hrs 3hrs 4hrs Figure 12: Effect of the TE window on TE optimization. from 1 to 3, the utility gradually decreases up to 14% compared to the default strategy. The utility almost remains the same after m exceeds 3. This suggests that 2 to 3 alternative routes are sufficient for TE optimization in MSN. 7.4 Effects of TE window Finally, we study the impact of TE window on optimization results. Entact performs online TE in a TE window using predicted performance and traffic information ( 5). On the one hand, both performance and traffic volume can vary significantly within a large TE window. It will be extremely difficult to find a fixed TE strategy that performs well during the entire TE window. On the other hand, a small TE window will incur high overhead in route injection, RTT measurement, and TE optimization. It may even lead to frequent user-perceived performance variations. Figure 12 illustrates the wrtt, cost, and utility of Entact 1 under different TE window sizes from 2 minutes to 4 hours. As before, we focus on comparing the utility. We still use 4 alternative routes from each DC to each prefix. Entact 1 can attain about the same utility reduction compared to the default strategy when the TE 12

14 Fraction of 5-minute intervals static default Entact Total traffic shift of a 5-minute interval 8.4 Probing requirement To probe 3K prefixes in an 1-hour TE window, the bandwidth usage of each prober will be 3K (prefixes) x 2 (alternative routes) x 2 (RTT measurements) x 5 (TCP packets) x 8 (bytes) / 36 (seconds) =.1 Mbps. Such overhead is negligibly small. 8.5 Processing traffic data Figure 13: Traffic shift under the static, default, and Entact 1 TE strategies change across different intervals. As a result, Entact 1 incurs limited extra traffic shift compared to the default strategy. 8.3 Computation time Entact k computes an optimal strategy in two steps: i) solving an LP problem to find a fractional solution; ii) converting the fractional solution into an integer one. Let n be the number of prefixes, d be the number of DCs, and l be the number of peering links. The number of variables f ijk in the LP problem is n d l. Since d and l are usually much smaller than n and do not grow with n, we consider the size of the LP problem to be O(n). The worst case complexity of an LP problem is O(n 3.5 ) [1]. The heuristic for converting the fractional solution into an integer one ( 4.2.3) requires n iterations to assign n prefixes. In each iteration, it takes O(n log(n)) to sort the unassigned prefixes in the worst case. Therefore, the complexity of this step is O(n 2 log(n)). We evaluate the time to solve the LP problem since it is the computation bottleneck in TE optimization. We use Mosek [6] as the LP solver and measure the optimization time of one TE window on a Windows Server 28 machine with two 2.5 GHz Xeon processors and 16 GB memory. We run two experiments using the top 2K high-volume prefixes and all the 3K prefixes respectively. The RTTs of the 2K prefixes are from real measurement while the RTTs of the 3K prefixes are generated based on the RTT distribution of the 2K prefixes. We consider 2 alternative routes from each of the 11 DCs to each prefix. The traffic volume, routing, and link price and capacity information are directly drawn from the MSN dataset. The running time of the two experiments are 9 and 171 seconds respectively, representing a small fraction of an 1-hour TE window. We use a Windows Server 28 machine with two 2.5 GHz Xeon processors and 16 GB memory to collect and process the Netflow data from all the routers in MSN. It takes about 8 seconds to process the traffic data of one 5-minute interval during peak time. Because Netflow data is processed on-the-fly as the data is streamed to Entact, such processing speed is fast enough for online TE optimization. 9 Related Work Our work is closely related to the recent work on exploring route diversity in multihoming, which broadly falls into two categories. The first category includes measurement studies that aim to quantify the potential performance benefits of exploring route diversity, including the comparative study of overlay routing vs. multihoming [7,8,11,24]. These studies typically ignore the cost of the multihoming connectivity. In [7], Akella et al. quantify the potential performance benefits of multihoming using traces collected from a large CDN network. Their results show that smart route selection has the potential to achieve an average performance improvement of 25% or more for a 2-multihomed customer in most cases, and most of the benefits of multihoming can be achieved using 4 providers. Our work differs from these studies in that it considers both performance and cost. The second category of work on multihoming includes algorithmic studies of route selection to optimize cost, or performance under certain cost constraint [12, 15]. For example, Goldenberg et al. [15] design a number of algorithms that assign individual flows to multiple providers to optimize the total cost or the total latency for all the flows under fixed cost constraint. Dhamdhere and Dovrolis [12] develop algorithms for selecting ISPs for multihoming to minimize cost and maximize availability, and for egress route selection that minimizes the total cost under the constraint of no congestion. Our work differs from these algorithmic studies in a few major ways. First, we propose a novel joint TE optimization technique that searches for the optimal sweet-spot in the performance-cost continuum. Second, we present the design and implementation details of a route-injection- 14

15 based technique that measures the performance of alternative paths in real-time. Finally, to our knowledge, we provide the first TE study on a large OSP network which exhibits significantly different characteristics from multihoming stub networks previously studied. Our work as well as previous work on route selection in multihoming differ from numerous work on intra- and inter-domain traffic engineering, e.g., [13, 18, 23]. The focus of these later studies is on balancing the utilization of ISP links instead of on optimizing end-to-end user performance. 1 Conclusion We studied the problem of optimizing cost and performance of carrying traffic for an OSP network. This problem is unique in that an OSP has the flexibility to source traffic from different data centers around the globe and has hundreds of connections to ISPs, many of which carry traffic to only parts of the Internet. We formulated the TE optimization problem in OSP networks, and presented the design of the Entact online TE scheme. Using our prototype implementation, we conducted a tracedriven evaluation of Entact for a large OSP with 11 data centers. We found that that Entact can help this OSP reduce the traffic cost by 4% without compromising performance. We also found these benefits can be realized with acceptably low overheads. 11 Acknowledgments Murtaza Motiwala and Marek Jedrzejewicz helped with collecting Netflow data. Iona Yuan and Mark Kasten helped with maintaining BGP sessions with the routers in MSN. We thank them all. We also thank Jennifer Rexford for shepherding this paper and the NSDI 21 reviewers for their feedback. References [1] Linear programming. wiki/linear programming. Retrieved March 21. [2] Nielsen Online. [3] Quagga Routing Suite. [4] Schooner User-Configurable Lab Environment. schooner.wail.wisc.edu/. 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