CA2344738A1 - Revenue-optimal admission controller with hard quality of service guarantees for data networks - Google Patents

Description

Motivation Multimedia traffic, comprising voice, video, images and text, is becoming increasingly common on internets and is expected to account for a large portion of future Internet use. Entertainment (movies, television and games) and videoconferences (meetings, classes) are two of the important applications.
Multimedia traffic, unlike e-mail or file transfers, requires strict guarantees of the Quality of Service (QoS) provided by the Internet, mostly relating to the data rate (transmission speed), error rate and latency (delay) provided to the customer.
Video, for example, must be transmitted at the correct rate and with few errors, or users will see jerky or distorted images. Interactive voice conversations must suffer network delays of less than a few hundred milliseconds or participants will become disoriented, unsure of who's speaking. A network which guarantees the promised level of QoS
to each customer is called a QoS-enabled network.
Given that any network has finite resources -- the capacities of its transmission links (measured in bits/sec) and routers or switches (often measured in packets/sec) --the allocation of enough link and switch capacity to each user to guarantee QoS means that not all applicants can be admitted to a QoS-enabled network. (To do so would

2 invite overbooking of resources and hence the violation of QoS guarantees.) If there is not enough free capacity to serve a new applicant, it must be rejected. The entity which scrutinizes applicants and admits or rejects them is called a network admission controller. An admission controller which keeps track of committed network resources, and only admits a new applicant if sufficient uncommitted or free resources are available to meet its QoS needs, is called an admission controller with Quality of Service Guarantees. If these guarantees are absolute they are called hard; if they can be broken occasionally without penalty they are soft. Finally, an admission controller which is able to select among all of the customers on offer, so as to admit the subset which yields the highest possible revenue, is revenue-optimal.
Description of the Invention We claim to have invented and built the first revenue-optimal admission controller with QoS guarantees for internets. (In previously disclosed work, we described a revenue-optimal admission controller for a multimedia server computer.) Customers' requests for admission are expressed as Service Level Agreements; we describe these first.
1. SLA request The Service Level Agreement or SLA specifies

3 1. the data rate requested 2. the maximum acceptable latency 3. the offered price, and

4. start and end times

5. whether the service is recurnng (e.g. every Tuesday) for each of one or more levels of QoS.
The admission controller must then admit the customer at one of the specified levels of QoS. (By convention, QoS Level 0 means zero data rate, infinite latency, and no charge, i.e. rejection of the SLA.) If a rejected customer chooses, she may revise her offered price upwards and bid again to be admitted; hence the admission process incorporates an auction.
As SLAs may arrive randomly in time, and as the controller selects a subset of those on offer to be admitted, we collect or batch SLAs for an interval of time called an epoch. At the end of each epoch ( a few seconds to a few minutes in practice) the controller selects SLAs for admission from the accumulated batch, and concurrently the next epoch begins.

2. Mathematical Formulation of the Controller's Functioning For each session, a QoS level must be selected. This level determines the session revenue and the session resource requirements, where a session is the flow of datagrams ( for example, a telephone call or the viewing of a movie) requested and permitted by an SLA.
In order to guarantee service at the level of QoS selected, we must bind the necessary resources to the session before it begins, and for the duration of its existence. In this manner we can avoid allocating the same resource to two competing sessions (analogous to allocating an airline seat to two competing passengers), and thus violating QoS guarantees.
The system revenue is the arithmetic sum of all session revenues. Figure 1 shows the relations between system and session revenues, and between resource mappings and constraints, established via the choice of session QoS.

' session i bronze silver gold Quality Q;
session utility function , ~ \ , resource mapping session utility u~(Q;)~ ( session resources r system utility ~ \ system resource constraints U= E u,(Q~ ~ ~ E r (Q~) 5 R
Figure 1: Relations among qualities, utility and resources.
Our goal, then, is to maximize the revenue U while respecting the system resource constraints R. This problem is called the Adaptive Multimedia Problem, or AMP.
We believe we are the first to realize that the problem of selecting SLAs for admission from an offered batch so as to maximize revenue, while observing all QoS
guarantees, can be expressed as a variant of the famous Knapsack Problem of combinatorial mathematics.
2.1 Knapsack Problem

6 In its simplest form, we have a pile of stones, each of which has a weight and a l~
r J
w Pick items to maximize weight (utility) U=Eu, sub ject to volume constraint (resource constraint): Eps39 Fig 2- Knapsack Problem Volume (please see Fig 2), and a knapsack which has a volume. The problem is to pick a subset of the stones which maximizes the weight of the knapsack while remaining within its volume constraint, i.e. not overfilling it.
2.2 Multidimensional Multiple Choice Knapsack Problem (MMKP) In this little-studied variant of the Knapsack Problem, we have piles of stones, and we must select exactly one stone per pile, so as to maximize weight while respecting the volume constraint. And, volumes are allowed to be vectors rather than single

7 numbers, so the volume constraint is multidimensional. Please see Figure 3, where the volume is 2-dimensional with components p and m. This means that the sum of p-values of the stones chosen must not exceed the p-value of the knapsack, and the sum of m-values of stones chosen must not exceed the m-value of the knapsack.
Revenues are u-values, as before.
Figure 3: Multidimensional Multiple Choice Knapsack Problem (MMKP) 2.3 Mapping The key to solving our admission control problem is to convert it into a known, well-understood problem - the MMKP. We do this by letting

8 ~ a stone represent a SLA at a particular level of QoS
~ a pile of stones represent a SLA ( all levels of QoS) ~ the knapsack represent the data network ~ each volume constraint of a stone represent a requirement for one of the network's resources, i.e. data rate or latency of one link of the network.
~ the weight of a stone be the price offered for this SLA at this level of QoS
Thus the act of selecting a set of stones to maximize weight becomes the act of selecting a set of SLAs at particular QoS levels which maximizes revenue. To refrain from overfilling the knapsack is to refrain from oversubscribing any of the network's resources - the data rates or latencies which its links can sustain - and thus QoS is assured.
2.4 Routing However, we must know which links of the network a given SLA will use, in order to know the links whose data rate or bandwidth resource will be (partially or completely ) consumed by the SLA. In order to determine this, we must select a path or route through the network from source to destination. As figure 4 shows, a route is a sequence or chain

9 of links connected by switches, along which datagrams flow from source to destination.
We must find a route which has adequate spare (free, uncommitted) capacity or bandwidth on each of its links. We do this by deleting all links with insufficient spare capacity from the network and applying a well-known, standard network routing algorithm to the remainder.

Figure 4 - A Route through a network from S to D.
2.5 Solving the MMKP
We have now completely converted our problem to a mathematical one, an MMKP.
To solve the MMKP we invented two new algorithms, described in the Appendices.
The first, BBLP, (Branch & Bound with Linear Programming) yields an exact solution. However, as the problem grows (bigger networks or more SLAs) the time and cost for a solution grows very rapidly (as the problem is NP-hard) so BBLP
is impractical for large networks. The second solution algorithm, NHEU, is inexact.
However, it is much faster and usually yields solutions which are within 10 or of the exact, i.e. truly optimal or best solution.
2.6 Building the Admission Controller The admission controller has been constructed as software running on a standard Pentium-based computer running Windows 9~. We implemented the NHEU solution algorithm for the MMKP, a routing algorithm (OSPF), and procedures to accept a set of SLAs and a description of the subject network's topology and link capacities.
2.7 Using the Admission Controller In our demonstrations of the controller, we make up SLAs manually and put them in an input file. The controller reads the network topology and capacity files and builds an internal description of the subject network. It then reads the SLAB.
Procedure NHEU is invoked to solve the resulting MMKP, and the SLAs admitted are displayed on the computer's screen, together with the revenue earned and the states of all network links.
To use the controller to regulate admissions to a real network, we pass SLAs to the controller, which batches them and selects the admitted ones by solving the MMKP. It then asks the local switch of the network, to which it has a direct connection, to build MPLS (Multi Path Label Switching) paths corresponding to the routes chosen by the controller for the admitted SLAB. It then passes the resulting MPLS path id to the customer, who labels every datagram with this label. The usual MPLS procedures of the switch then ensure that all datagrams of this SLA are routed along this MPLS path.

2.8 Performance The controller must run fast enough to allow real-time admissions; that is, the decisions to admit SLAs, and if so at which level of QoS, must be taken as the SLAs arrive in real time. Admission of a batch can be done concurrently with the collecting of SLAs for the next batch, so the controller need only complete an admission in less than the epoch time inter~.-al: We have suggested that a few seconds to a few minutes are realistic values for the epoch interval.
Using a controller built in Java running on a Pentium 3 microprocessor, we are able to admit 100 SLAs to a 30-node network in less than 2 seconds. As a re-implementation of the controller in C would yield about a factor of 10 speedup, a controller to do this task in 200 msec is feasible. Hence the controller using current technology is fast enough for real time admission to enterprise networks (usually defined as networks of less than 100 switches or nodes).
of the patent application 1. A method for admission control of service reque's'ts-i~ QoS-guaranteed data network to provide dynamically optimal system revenue, by using a technique

Claims (4)

3. Claims of the patent application

1. A method for admission control of service requests into a QoS-guaranteed data network to provide dynamically optimal system revenue, by using a technique where the admission control problem is first mapped to a variant of the combinatorial knapsack problem.

2. The method of claim 1, where a service request is expressed using a service level agreement describing several levels of QoS, where each level specifies a. the requested data rate, b. the maximum acceptable, c. the offered price, and d. start and end times.

3. The method of claim 1, where MPLS or RSVP is used for setting up a route in order to meet the QoS guarantee of an admitted service request.

4. The method of claim 1, where a heuristic is used to find a real-time and near-optimal solution of the knapsack problem.