On Service Level Agreements for IP Networks

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1 On Service Level Agreements for IP Networks Jim Martin Gartner Consulting Arne Nilsson ECE Department 5 Falls of Neuse Road North Carolina State University Raleigh, NC 769 Raleigh, NC jim.martin@gartner.com nilsson@unity.ncsu.edu Abstract-- Many corporate WAN architects are considering migrating from costly leased line circuits to private IP services. In order to do so IP service providers must provide Service Level Agreements (SLAs) that offer robust assurances that cover service availability and performance. The industry direction is to model IP SLAs after those offered by frame relay networks. The reality however is that the SLAs surrounding today s private IP services only loosely mirror frame relay SLAs. In this paper, we examine the intent and limitations of current IP SLAs. We explore the feasibility of two significant enhancements. First we study the feasibility of reducing the time scales associated with the performance assessment to something that is meaningful to the corporate end user. Second we explore the potential benefits of extending current performance assessment methods with application level performance metrics. We present preliminary results suggesting that it is possible to offer performance assurances based on carefully chosen latency objectives as long as the average utilization of the access link is less than 5% over time scales identical to those used by the performance metrics. We introduce a web oriented performance metric (the Web Response Time metric) and show promising benefits over traditional ping based metrics. However, for either ping or web based performance metrics, due to the inherent nature of a best effort IP service, IP performance assurances are only practical when applied over long time scales. I. INTRODUCTION A Service Level Agreement (SLA) is a contract between a network provider and a customer that defines all aspects of the service that is to be provided. An SLA generally covers availability, performance and customer service. While the telephone operating companies are able to provide very tight performance assurances for circuit switched digital data services, packet switched networks are much less conducive to predictable behavior. Certain packet switched technologies, such as ATM and frame relay that were designed to provide QoS, can offer relatively robust performance assurances based on standard performance metrics. IP networks, on the other hand, are fundamentally different as they operate on a best effort basis to service packets. Universal connectivity as well as pricing issues have made the internet paradigm successful as evidenced by the explosive growth of the Internet. Large Internet Service Providers now offer an enhanced IP dedicated access service known as a private IP service. Many corporations are considering replacing their leased lines with private IP connections. A private IP service implies that the corporation s IP packets remain within the provider s IP backbone (i.e., packets do not travel over the public Internet). However before corporations are willing to bet their business on private IP, ISPs must support the service with robust SLAs. Many view frame relay SLAs as the benchmark. While frame relay SLAs provide service level assurances, an IP service with no additional control mechanisms can only attempt to offer statistical performance bounds through a combination of network measurement and dimensioning. The next evolution of IP performance will be achieved once providers support differential services [1]. DiffServ provides scalable QoS that can provide a variety of end-toend services across multiple, separately administrated domains. Although not yet widely deployed, DiffServ will be the basis for future tiered service levels as well as the technical foundation required to support inelastic applications such as VoIP that require tight QoS guarantees. DiffServ flows are policed and marked by an edge router according to a contracted service level agreement. A network offering DiffServ can offer absolute or statistical performance bounds (e.g., 95% of all packets measured over intervals of at least 5 minutes will cross the domain in less than 5 milliseconds) []. Such performance assurances are tied to the service definition. Until IP differential services are available however, IP SLAs will be problematic for service providers. With the help of MPLS, a service provider can confidently offer latency and loss assurances within their backbone network [3]. However the enterprise requires end-to-end SLAs that extend between the two routers that define the end points of the IP service. The challenge for the service provider is to extend performance assurances over the access link. Typically outside the control of the provider, the access link is relatively low bandwidth and represents a likely congestion point. Not surprisingly, service providers have traditionally excluded the access link from SLA offerings. That is changing as evidenced by WorldCom s end-to-end SLA for their managed VPN service []. In brief, Worldcom s performance assurances extend between the customer premises equipment (i.e., the VPN endpoints) and are highlighted as follows: 1) The performance metrics are based on large time scales (one month is typical); ) The average utilization of the access link must be less than 5%; 3) The SLA assessment includes latency but not packet loss.

2 We study this SLA in detail. Of particular interest is the viability of the long standing rule of thumb that says a network is under provisioned once the average utilization exceeds 5%. While the large time scales associated with the SLA protect the service provider, the end user is not offered a significant level of assurance. We explore the feasibility of reducing the time scales associated with the SLA to something more meaningful than 3 days. We also investigate the benefits and implications of application level performance objectives in addition (or in place of) traditional ping based loss and latency metrics. This paper is organized as follows. In section, we identify and discuss SLAs that are currently offered by ISPs. In section 3 we analyze a hypothetical SLA in an effort to understand the limitations and to identify improvements. In section we summarize and identify future work items. II. IP PERFORMANCE SLAS It is convenient to discuss IP SLAs by comparing to those offered by frame relay. The Frame Relay Forum has defined three performance metrics that can be used as the basis for an SLA [5]: Network Availability: This is the ratio of network up time divided by the total amount of time associated with the metric. Network Latency: This is the maximum one way frame latency over the frame relay network (end-to-end between frame relay devices at the customer site). Data Delivery Ratio (DDR): This is the percentage of data (i.e., bytes) that is successfully sent over the frame relay network (applicable only to traffic that does not exceed the committed information rate). A related metric, the Frame Delivery Ratio (FDR) is the percentage of frames sent successfully over the network. As an example, Sprint s domestic frame relay service offers the following SLA [6]: Availability: Sprint offers 1% availability when they provide a SONET access connection and 99.9% availability over Sprint-provided non-enhanced (i.e., not SONET) metropolitan-area access. If the customer provides the access link, Sprint limits the SLA to their network. In general, availability is averaged over a one month time period. Permanent Virtual Circuit (PVC) delay: Sprint s latency commitment depends on the access speeds and the class of service associated with the data. For T1 access, the maximum one-way end-to-end delay will be within 7 milliseconds within the continental United States. This is based on an instantaneous measurement over a special (unloaded) permanent virtual circuit (PVC). Loss: Sprint offers a 99.9% Frame Delivery Ratio commitment for traffic within CIR. Frame relay devices located at the customer site are periodically queried for their send and receive byte counts. The performance management entity compares the sending side s send count with the receiving side s receive count to determine the FDR. The popularity of frame relay demonstrates that it is possible to provide a data service that optimizes network efficiency while providing basic service commitments. Although frame relay is the dominant enterprise WAN technology, private IP is cheaper and less complex (i.e., a unified network technology). While IP SLAs in the form of availability, latency and loss are offered, they are generally confined to the backbone. As the evolution of IP continues, service providers are being pressured to extend IP performance assurances to the customer site. WorldCom/UUNET s VPN solution was one of the first provider s to offer a true end-to-end SLA commitment []. In brief, the commitment assures that the VPN service will be available 99.8% of the time averaged over 3-9 sites with latencies less than 1ms between customer premise equipment (CPE) routers within North America. The measurement is based on periodic pings between CPE s. Measurements are averaged over one month at all eligible sites. An eligible site is defined as a site whose sustained usage level is less than or equal to 5% of the total dedicated capacity. If sustained use exceeds 5% during two consecutive months, customers must order an upgrade within a 3 day period or the site becomes ineligible for the SLA. If an SLA violation occurs in two consecutive months, UUNET provides credit back to the customer 1. The key difference between Sprint s frame relay SLA and Worldcom s IP SLA is clearly that the level of assurance associated with IP is very weak. While both networks (a frame relay network and a private IP network) will be subject to the same highly variable traffic arrival processes (where spikes ride on ripples that ride on still longer term swells [7]), the basic service offered by frame relay and IP is different. Frame relay is a service that offers a committed information rate while IP is a best effort service with no notion of a maximum burst size (aside from the link capabilities). Frame relay has several basic elements that are necessary to provide performance assurances: traffic shaping and policing that contribute to accurate provisioning to meet service requirements. A private IP (or VPN) service provides a best effort service with a quality of service that can only be enforced through network provisioning. This explains the lengthy time scales associated with IP SLAs. Time scales on the order of weeks are necessary to identify and correct network provisioning problems. A second, more subtle difference, between IP and frame relay SLAs is that frame relay has an architected method for monitoring performance assurances. The frame relay latency metric is monitored over an unloaded PVC and is therefore 1 WorldCom s VPN SLA applies over certain access lines. In this paper we assume the access line is a leased line. We expect that over the next several years providers will be able to offer similar performance assurances over broadband access lines such as DSL.

3 isolated from user data carried over other PVCs. Frame relay monitors packet loss by having both ends of the frame relay circuit maintain frame counts (sent and received) for traffic that abides to the CIR. Traffic that exceeds the CIR is considered outside the scope of the SLA. The measurement approach is designed to assess only traffic that meets the traffic specification. Because IP does not provide a measurable service the significant challenge for the service provider is to provide end-to-end assurances over a relatively uncontrolled portion of their network (i.e., the access link). Worldcom s approach is based on the rule of thumb that says that IP performance deteriorates once the average utilization of a link exceeds 5%. They are willing to offer a latency commitment that extends to the VPN endpoint as long as the customer s access link is correctly provisioned. We explore the robustness of this approach in the next section. III. AN END-TO-END IP SLA FRAMEWORK In this section we explore the intent and limitations associated with end-to-end performance guarantees modeled after Worldcom s VPN SLA. We show that such guarantees offer little assurances to the end user primarily because of the large time scales involved and because packet loss is not considered. We introduce a web oriented performance metric that can either augment or replace traditional latency and loss metrics. We consider the network scenario depicted in Figure 1. Assume that an enterprise is using a carrier s private IP service to connect remote branch offices to a corporate network. The SP-x routers are located within the provider s network. The C-1 router connects the main corporate network to the provider s WAN. The R-x routers are located at the branches. The link between the SP-x and the R-x routers is the access link. Each branch can have up to n users (employees). Assume that all traffic, including traffic destined for the Internet, flows through the corporate network. Based on the WorldCom SLA, we propose the following hypothetical end-to-end SLA : Availability: The network will be available 99.8% of the time averaged over one month including all of the customer routers (C-1 and R-1 through R-n). Assume that the network is considered unavailable once 5 minutes has expired without a successful ping. The frequency of the ping probe is seconds with a second timeout. Latency: A ping process continuously monitors the latency between each branch and the end point of the IP service (i.e., between the R-n nodes and the C-1 node). The hypothetical SLA is modeled as close as possible to that offered by Worldcom. We have added details such as ping frequency and the definition of availability as these were not specified in the published SLA information. The frequency of the ping probes is every seconds. The average ping round trip time must be less than 1 milliseconds averaged over 1 month. SLA constraint: The SLA is valid as long as the link utilization associated with each access link is less than 5% (averaged over 1 month). Public Internet Corporate intranet C-1 SP-1 private IP service provider SP- R-1 R- R-3.. R-n n branch offices 1Mbps switched Figure 1. Network and measurement infrastructure U-1 U- U-3.. U-n n users within a branch office For the analysis presented in this section, we make the following assumptions. First we assume that the access link connecting the branch offices is a T1 private line with a maximum queue depth of packets and represents the bottleneck over the path between the main corporate network and the branch. Second, we assume that the dominant application at the branch office is a web browser so that the direction of the bottleneck is downstream (i.e., in the SP- to R-1 direction). It is not difficult to see immediate deficiencies with our hypothetical SLA: The availability metric is network oriented rather than user oriented. In order for the metric to be violated, the network must be physically down for roughly 1 hours over a given month. It is possible for performance to deteriorate such that even though the network is not unavailable, it is unusable. A user oriented availability metric assumes the network is unavailable once the network becomes unusable. During peak hours (i.e., the busy hours between 9:AM and 5:PM), it is possible that the access link connecting the branch is heavily congested. However because the latency and utilization metrics are averaged over large time scales the SLA might not be violated. Packet loss is not a component of the performance assessment. It is well known that latency as well as packet loss determines TCP performance [8]. Clearly the SLA is intended to protect the interests of the service provider by providing minimal performance guarantees to end-users. The provider relies on large time scales as well as a link utilization constraint to minimize their risk. We use simulation to help us evaluate the effectiveness of this from both the provider s and the enduser s perspectives. We also explore the potential benefits associated with using application level performance metrics in addition to traditional loss and latency metrics. It has

4 been shown that IP networks exhibit correlation between loss and delay which leads to bursty loss processes [9, 1]. It is well known that second order network dynamics impacts real-time flows [11] as well as TCP sessions [1]. Given that the majority of application traffic flowing over the Internet is HTTP data [13], we propose the use of a web performance metric to assess end-to-end network performance. We define the Web Response Time (WRT) metric as follows. A client periodically issues an HTTP request for a web object from a web server. The client and server are positioned at the end points of the IP service (i.e., at routers R-1 and C-1 respectively in Figure 1). A WRT sample is the amount of time from when the client issues the request to when the entire web object has been successfully received by the client. The client accumulates response time samples over time and periodically assesses network performance based on the web response time data. An application level performance metric offers several advantages over traditional ping based metrics. First, the WRT metric seamlessly incorporates the impact of loss and latency dynamics on the application into the performance assessment. Second, it is much more natural to translate WRT metric results into an assessment of how the network is impacting end users compared to an assessment based on loss or latency metrics. For example, it is not possible to know the impact a network has on web browsing sessions when the average ping RTT statistic reaches 1 milliseconds. Although the network assessment will be more accurate when a ping loss rate statistic is included, one dimensional loss and latency metrics are unreliable indicators of the actual quality perceived by the end user. On the other hand web response time metrics can effectively indicate the impact of the network as observed by an end user. Studies have found that users become frustrated if web pages are not displayed within 11 seconds [1]. Given that a typical web page embeds roughly 1 web objects (although this varies widely), a rough rule of thumb is that once the average web response time statistic (i.e., a moving average based on the most recent samples) approaches 1 second, it is reasonable to conclude that network performance is negatively impacting the end user experience. We define a supportive performance objective based on a statistical percentile as: 95% of the web response time samples must be less than seconds over a two hour time period. 3 The advantage of the latter metric is that it naturally provides a user oriented availability objective. If the network does not meet the performance criteria 95% of 3 We are developing a decision algorithm that can assess a network s ability to transport web sessions based on the WRT metric data. The challenge is to reliably detect when a network is impacting an end user s quality of experience but in a manner that if sufficiently flexible to allow a provider to build a profitable best effort IP network. For the purposes of this paper we assume that a private IP service is performing well only if the 95 th percentile metric is less than seconds and the response time mean is less than 1 second. the time, the network is unusable and is considered unavailable. Figure illustrates a simulation model that is based on the corporate network shown in Figure 1 using the ns simulation package [15]. The model consists of backbone routers and edge routers. The carrier s IP network extends from SP1 through SP. The simulated backbone network links are high speed (135Mbps to account for ATM overhead) with propagation delays in the to 1 millisecond range. The corporate network is connected to the carrier s IP network with a 5 Mbps leased line (i.e., between the C1 and SP1 routers). The access link connecting the service provider with the branch office (SP to R1) is a T1 link with a 1 ms propagation delay. The C1 and R1 routers are located at the customer premises. As each branch, there can be up to n hosts (H1 through Hn) all of which attach to the R1 router through a 1Mbps full duplex switched LAN. WS1 -\ WS - \ 5Mb 135Mb 135Mbs 5Mbps 1.5Mbps WS3--- C1---SP1-----SP SP SP R1---- H1 WS - / \ \---- H / \ \--- Hn WS1 / WS11 WS1 WS13 \ \ Rn Figure : The Network simulation model The model generates background traffic by creating multiple branch offices (R through Rn) all of which aggregate at the SP router. We assume that the majority of users at a branch are web users and interact with web servers located within the corporate network or over the Internet. The WS1 through WS13 nodes represent web servers. These connect to the C1 router or any of the SPn routers with 1 Mbps links. The WSx nodes connected to C1 have propagation delays in the 1 to 5 millisecond range while the nodes connected to the SPn routers (e.g., WS11 through WS13) have propagation delays in excess of milliseconds to simulate web servers located outside the corporate network. During a simulation, we collect ping response times between the R1 and WS1 nodes. The ping samples are taken every seconds. In addition we collect web response times between R1 and WS1 (this is the basis for our application level metric). We have modeled and attached a WRT client at node R1. The client periodically (every seconds) probes the network by initiating a TCP connection with the WS1 web server. The server responds by sending Kbytes of data to the client. Our simulation model allows us to simulate any number of users at a branch, each generating realistic traffic levels over the WAN. A user is assumed to be an HTTP user that Kbytes was chosen as it represents a realistic size for a web object [BARF98].

5 requests a certain amount of data from a web server. We base our traffic model on the notion of a user equivalent that was presented in [16]. A user equivalent is essentially an ON/OFF source that alternates between making a request for a web file and lying idle. By generating web file sizes and idle times based on a Pareto distribution it is possible to create realistic user traffic loads. We also configured other branches (i.e., R through Rn) with simulated users to generate a small level of additional traffic through the network. However the experiments were designed to study performance at the bottleneck link between SP and R1 as the number of users at branch R1 increase. First we wish to convey the accuracy of our traffic models. Using tcpdump on a live network, we trace a (real) user s 5 minute Internet browsing session. The user was located at a residence connected to the Internet via a high speed cable connection (8Mbps downstream, 51Kbps upstream). The user was surfing the Internet either visiting news sites (Usatoday, Wall Street Journal) or searching for information by visiting and following search hits. The top curve of Figure 3 plots the cumulative amount of downstream data that arrives over time. A total of 1.6 Mbytes of data is downloaded during the 5 minutes session leading to an average bandwidth requirement of just under 5Kbps. The top curve shows the ON/OFF dynamics associated with web browsing. Because the browser and server support HTTP version 1.1, a burst (i.e., an ON cycle) actually represents multiple TCP connections, some of which request more than one web object. The lower curve shown in Figure 3 illustrates the load induced by one of our simulated branch office users. We modeled each simulated user using a single TCP/Reno connection with a pareto traffic generator attached. The TCP receivers were limited to a maximum window of 16Kbytes. The receiver was set to utilize a delayed acknowledgement using a random delay based on a uniform distribution with a mean of.1 seconds. The pareto generators were configured with a burst-time of. seconds, a burst-rate of 1Mbps, an idle time of 35 seconds and a shape parameter of 1.1. We performed 1 runs, each run lasting 1 seconds ( hours) in simulation time. Each run was identical except we increased the number of branch office users from 5 up to. The lower curve in Figure 3 plots the traffic arrival observed by one of the users located in the branch (from the run with 5 users). We monitored the bandwidth consumed by each simulated user and found that the average bandwidth consumed per user was 5.3Kbps. The minimum and maximum bandwidths consumed by the simulated users were 1.9Kbps and 9.3Kbps respectively. Although our traffic model does not capture HTTP protocol details (which might account for the visual difference between the simulated and measured bandwidths illustrated in Figure 3), we believe that the model produces traffic patterns that could be generated by a real web user. 15 Byte Arrivals 1 5 Byte Arrivals 1 x 1 5 Single Measured User Downstream Arrivals x 1 5 Single Simulated User Downstream Arrivals 15 5 Util (%) 6 Loss (%) Figure 3. Calibrating the simulation models Downstream Link Utilization Number of Active Users Downstream Loss Rate Number of Active Users Figure : Utilization and loss rate results The top curve of Figure plots the average utilization of the bottleneck (in the direction from the corporate network to the branch office) for each of the 1 runs in the experiment. Based on the utilization curve, we see that the network can support up to 13 simulated users before exceeding the 5% utilization constraint. The lower curve in Figure shows that the loss rate for 13 users is about 1.6% which is reasonable. In the following discussion we study network performance when the link is slightly less than 5% utilized (i.e., such that the SLA is still in effect). The top graph of Figure 5 plots the results of the ping probe process during the run with 15 active users (which had an average link utilization of 7%). The solid curve plots the RTT samples, the dashed curve plots the average of the most recent two hours worth of ping samples (i.e., a moving average). The uncongested RTT is roughly 5 milliseconds, the moving average is roughly 1 milliseconds. Assuming normal daily user workloads, the branch office might see this level of use only during certain hours of the day. If the link is minimally used during non-

6 business hours and fully used during business hours, the RTT averaged over one month would be roughly 5 milliseconds which is well within the SLA latency commitment Ping RTT Samples Web Response Time Samples Figure 5: Metric results (15 Users) However, many branch offices perform large data transfers during the evening that can significantly increase the monthly average utilization statistic. The worst case is when the access link utilization is just under the 5% threshold averaged over long time scales (3 days) as well as short times scales such as hours. From the provider s perspective, the question is if the latency commitment can be guaranteed when the access link is 7% utilized (as in the 15 user run). The top curve in Figure 5 shows that the monthly average RTT would be in the range of 1 milliseconds which is very close to the SLA limit of 1 milliseconds. Given that the uncongested RTT over the path is 5 milliseconds and that the coast-to-coast propagation delay is around 6 milliseconds, it seems reasonable to assume that the provider would not be able to guarantee a latency commitment of 1 milliseconds under worst case conditions. The latency commitment has the following components: RTT threshold = RTT min + T downstreamdelay + T upstreamdelay The RTT min represents the uncongested RTT which includes all transit and propagation times in both directions. The T represents the total queueing delay in downstreamdelay the downstream direction accumulated at each hop. For our example, the downstream access link is the bottleneck and will contribute up to 16 milliseconds of delay (i.e., packet times over a 1.5 Mbps link). The delays incurred at other links in the downstream path as well as the delays in the upstream path are insignificant when the downstream access link is congested. The challenge for the service provider is to estimate the queue delays over the access link. The problem of predicting queue dynamics at a link subject to aggregate IP traffic is extremely difficult. The model must account for many parameters including the router s buffer size, the link speed, the average link utilization and the amount of traffic that is CBR (e.g., video rather than TCP traffic). We will show shortly that the percentage of high speed UDP flows (e.g., streaming) can have a tremendous impact on network dynamics at an access link. The average queue delay at a given link is a function defined as follows: T = f ( buffersize, linkcap, linkutil,% CBRTraffic) Qdelay Our simulation method for solving this problem indicates that the T is 75 milliseconds when the link downstreamdelay utilization is 7% and all traffic is TCP. Therefore in order to meet the latency commitment over any path within the continental United States in the worst case scenario described above the provider must increase the latency threshold to at least 135 milliseconds (assuming the queue delay at other hops is negligible). Or, because the hypothetical SLA does not include loss, the provider can set the router s maximum buffer size lower (assuming the ISP provides and installs the routers located at the customer sites). Or, a third alternative is for the provider to reduce the utilization threshold to % where the maximum number of users that can be supported drops to 1. The top graph of Figure 6 plots the average of all the ping samples (the solid curve) observed for each run. The dashed curve plots the average values from the moving average of the most recent ping samples (within the last hours). For 1 users, the T is roughly 5 milliseconds which would meet downstreamdelay the latency commitment over cross country paths. Util (%) Loss (%) Downstream Link Utilization(avg:7.15) Downstream Loss Rate (avg:1.15%) Figure 6. Utilization and loss rate for 15 user run From the end user s perspective, the question is how a 7% utilized link impacts performance. Just as important is to understand how well the performance metrics assess the impact of the network on the end user experience. Figure 7 plots the utilization and loss for the 15 user run. The loss rate varies dramatically, with an average over the entire hour period of about 1.1%. The ping performance metric

7 shows an average ping response time of.1 second (Figure 5). It is difficult to translate these statistics into an assessment of how the network is impacting the end user s perceived quality Avg Ping RTT Samples The lower plot of Figure 5 illustrates the output from the web response time metric. The dashed-dotted curve plots the 95 th percentile statistic that is computed in real-time by the simulated WRT client. Each value is the 95 th percentile of the most recent web response time samples (i.e., within the last hours). The dashed curve plots the mean statistic based on the most recent samples (within the last hours) for the run. The solid curve plots the actual web response time data associated with the run. As discuss earlier, the WRT statistics are a reliable indicator of the impact the network has on the end user web browsing experience. We use the following rules of thumb to determine if a network is negatively impacting the end user s browsing sessions: first, if the 95 th percentile of WRT samples exceeds seconds; second, if the most recent WRT average exceeds 1 second. The WRT results visualized in the lower curve of Figure 5 suggest that when the network is 7% utilized the performance of the network is sufficient to support 15 web users. The lower curve in Figure 6 plots the average WRT statistics as the number of users increase. The solid curve plots the average of a given run s 95 th percentile statistic (e.g., the point for 15 users would be the average of the values associated with the dashed curve plotted in the lower figure of Figure 5 which is about 1 second). The dashed curve plots the average of a given run s mean statistic. The dashed-dotted curve plots the average of all the sample data associated with a given run. The results suggest that the network can support over 18 users. The upper curve in Figure 6 indicates that only 1 users can be supported when constrained by a maximum RTT of 1 milliseconds. We conclude that an RTT objective of 1 milliseconds might be too restrictive when the dominant application is web browsing. The elastic nature of web browsing makes a browser very tolerant of large round trip time delays. While further study is required, we conjecture that it is easier for a service provider to guarantee an SLA based on the WRT metric than on latency and loss metrics. In a live network, the optimal ping based loss and latency thresholds dynamically change as network conditions evolve. A web based metric inherently avoids this difficulty while at the same time providing a more meaningful level of assurance as it is a direct indicator of the quality of service experienced by the end user Number of Active Users Avg Web Response Time Samples Number of Active Users Figure 7. Average performance for all 1 runs To further confirm the benefits of a WRT metric we performed another experiment. Using the 15 user simulation run, we added an additional flow, a simulated video stream from the corporate network to the branch. The bandwidth is set to 3 kbps and the flow is TCP unfriendly meaning it does not react to congestion [17]. We are interested in observing the performance metric s ability to accurately assess the UDP flow s impact on the quality of service experienced by users in the branch. The top curve in Figure 8 illustrates that the average utilization increases from 7% to 67%. Surprisingly the loss rate has decreased from 1.% to.55% (this is also evident when comparing the lower curves of Figure 8 and Figure 7). The top curve of Figure 9 illustrates that the ping metric decreases from.1 second to just over.5 seconds. The packet loss dynamics have become bursty contributing to a higher frequency of TCP timeouts. While the ping based metric incorrectly indicates that network conditions have improved, the WRT metric detects the increased variability in network performance. The WRT result plotted in Figure 9 indicates that even though the access link is 67% utilized, it is able to support 15 users. This suggests that the 5% utilization rule might be unnecessarily strict for highly adaptive applications such as web browsing. While this might be true in certain network scenarios (as in our experiment), the more accurate conclusion is that a performance assessment based on one dimensional metrics such as average utilization or ping latency can lead to an inaccurate assessment of the impact that the network is having on the end user. A multidimensional assessment based on higher level metrics provides an accurate, user oriented assessment of network performance. One of the objectives our research was to explore the feasibility of reducing the time scale associated with the hypothetical SLA from 3 days to two hours (including reducing the time scale associated with the utilization constraint). Our previous analysis (i.e., the worst case 15 equivalent user load) suggests that it is feasible to reduce the time scale to two hours as long as the average link utilization

8 is less than 5% for each two hour period. However reducing the time scales eliminates the statistical smoothing benefits gained when including periods of low network usage such as during non-business hours. The natural time scales associated with network provisioning are on the order of 3 days and consequently is the basis for current IP SLAs. It is not feasible to offer an SLA that requires the access link to be less than 5% utilized over small time periods (e.g. such as two hours as we used in our analysis) without additional control mechanisms. While performance assurances for today s best effort IP service must be based on large time scales, next generation IP service offerings based on differentiated services will be capable of supporting smaller time scale SLAs similar to those currently available for frame relay. 15 Util(%) 1 Loss (%) 5 Downstream Link Utilization (avg:67.73) Downstream Loss Rate (avg:.556%) Figure 8: Results for the 15 user run with an additional (CBR) video stream Ping RTT Samples Web Response Time Samples Figure 9. Simulation Metric results with CBR video source IV. CONCLUSIONS AND FUTURE WORK We have evaluated an end-to-end SLA with the following attributes: 1) The performance assessment is based on large time scales (one month is typical); ) The average utilization of the access link must be less than 5%; and 3) The SLA assessment includes latency but does not consider packet loss. Through simulation analysis, we have shown that the 5% utilization constraint is a viable method for offering a latency assurance although the provider must select the latency commitment with care. If bandwidth usage over the access link significantly deviates from normal time of day usage patterns, the smoothing benefits gained by averaging over large time scales disappear. In worst case usage scenarios (involving a private IP service with a T1 access link), we found the coast-to-coast RTT latency commitment must be at least 135 milliseconds which is larger than the common threshold of 1 milliseconds. While additional analysis is required to confirm this finding, it is clear that any performance assessment based on one dimensional metrics (e.g., link utilization or latency) can lead to an unreliable assessment of how network performance is impacting the end user s quality of experience. We presented the Web Response Time (WRT) performance metric and showed that it seamlessly accounts for loss and latency dynamics and consequently can be the basis for an accurate, user oriented performance assessment. Without QoS mechanisms, meaningful end-to-end IP SLAs must be based on large time scales and accompanied by a utilization constraint. In this paper, we have presented results suggesting that web based performance objectives can augment today s existing ping based SLAs. Although further analysis is required, we believe that web based metrics can be the basis of future IP SLAs. To validate the feasibility of this, we have developed a tool that monitors network performance using the WRT metric. We have deployed the tool and are gathering data over a cable broadband access network. Our approach is to deploy a WRT client (a Java applet) at a subscriber s premises (located at the author s residence) and a server at a site that is well connected to the provider s backbone. The server is a Unix PC running the Apache web server along with a report server (developed by the authors) that receives performance data periodically from the client. In one particular experiment (illustrated in Figure 1), we ran a ping RTT monitor (using the same client machine that is running the WRT client) concurrently with a WRT monitor over the cable network for a time period of 1 days. Because the server is lightly utilized and is well connected to the provider s backbone, congestion detected by the client was most likely located in the access link. The frequency of the ping probe and the WRT probe was on average seconds (the actual frequency is based on an exponential distribution with a mean of seconds). The top curve in Figure 1 illustrates the ping results (each point represents the ping RTT averaged over the last hours). The WRT curves (the lower graph) plot the 95 th percentile as well as the moving average of web response time samples

9 as described in our simulation analysis. The WRT data shows that the path between the client and the server is well provisioned. The ping results correlate well with the WRT data because the loss rate over the path was almost negligible Time (days) Ping RTT Samples Test Time (days) Figure 1. Metric results over a broadband cable network The motivation for our research is clear: a business class IP service must offer substantial performance assurances. Performance assurances based on time scales that span one month are not sufficient for enterprise customers. Our analysis showed that it is possible to offer latency assurances based on short time scales as long as the utilization level (averaged over the same time scale) was less than 5%. However, limiting utilization over small time scales is simply not feasible without bandwidth controls. While time scales as short as two hours might be impractical, is there room for compromise? Perhaps, but the question suggests that the future evolution of a private IP service should be towards new service offerings based on differentiated services. With this vision, we are developing a next generation IP service offering that involves: A set of application level metrics along with a corresponding set of user oriented performance decision algorithms appropriate for specific IP services. A IP service modeled loosely on a frame relay service but based on DiffServ technology. The service offers an integrated measurement capability that can monitor application level and/or service specific performance objectives. [5] Frame Relay Forum, Service Level Definitions Implementation Agreement, August, [6] K. Adams, Sprint s Network Services, Gartner/Datapro report DPRO-9915, December, 1. [7] W. Leland, et. Al., On the Self-Similar Nature of Ethernet Traffic, IEEE Transactions on Networking, Feb 199. [8] J. Padhye, et. Al., Modeling TCP Throughput: A Simple Model and its Empirical Validation, ACM SIGCOMM98, [9] S. Moon, et. Al., Correlation of Packet Delay and Loss in the Internet, INFOCOM [1] J. Bolot, End-to-end Packet Delay and Loss Behavior in the Internet, ACM SIGCOMM93. [11] W. Jiang, H. Schulzrinne, Modeling of Packet Loss and Delay and Their Effect on Real-Time Multimedia Service Quality, NOSSDAV, June,. [1] V. Paxson, End-to-end Internet Packet Dynamics, SIGCOMM 97. [13] K. Claffy, G. Miller, K. Thompson, The Nature of the Beast: Recent Traffic Measurements from an Internet Backbone, [1] N. Bhatti, et. Al., Integrating User-Perceived Quality into Web Server Design, Ninth International WWW Conference, May. [15] The Network Simulator. Available at : [16] P. Barford, M. Crovella, Generating Representative Web Workloads for Network and Server Performance Evaluation, ACM SIGMETRICS 98, July, [17] S. Floyd, et. Al., Equation Based Congestion Control for Unicast Applications, SIGCOMM. REFERENCES [1] S. Blake, et. Al., An Architecture for Differentiated Services, IETF RFC, [] K. Nichols, B. Carpenter, Definition of Differentiated Services Behavior Aggregates and Rules for their Specification, IETF Draft, draft-ietf-diffserv-ba-def-.txt, Feb. [3] D. Awduche, MPLS and Traffic Engineering in IP Networks, IEEE Communications Magazine, December, [] WorldCom/Uunet s VPN Total Access Edition,

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