Scalable Network Architecture & Measurements

Scalable Network Architecture & Measurements
for Multicast and Adaptive QoS Xin Wang (Thesis Defense) Adviser: Henning Schulzrinne Internet Real -Time Laboratory Columbia University Xin Wang, Columbia University

Scope of this Talk Main work Other work
Resource Negotiation, Pricing, and QoS for Adaptive Multimedia Applications Other work Measurements and Analysis of LDAP Performance IP Multicast Fault Recovery in PIM over OSPF You don’t have the context to describe your outline. Delete this one, just begin the next slide by saying: I’ll begin by providing some background on my work…. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Resource Negotiation, Pricing and QoS
for Adaptive Multimedia Applications Xin Wang, Columbia University

Outline Introduction A Resource Negotiation And Pricing protocol: RNAP
Pricing models User adaptation models Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Conclusion and future work Resource Negotiation Framework We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Is Simple Over-Provisioning Enough?
Current Internet: Growth of new IP services and applications with different bandwidth and quality of service requirements Revenue from the traditional connectivity services is declining New services present opportunities and challenges Even though average bandwidth utilization is low, congestion can happen; access links get congested frequently Wireless bandwidth is even more scarce Bandwidth prices are not dropping rapidly No intrinsic upper limit on bandwidth use A large number of new applications are appearing in the Internet. This includes real-time audio, video, and mission-critical financial data. It is difficult to predict the bandwidth. At the same time, revenue from the traditional connectivity services (raw bandwidth) is declining The ISP has new business opportunities, but also new challenges: Even though average bandwidth utilization is low, congestion can happen; access links get congested frequently; wireless bandwidth is even more scarce; Bandwidth prices are not dropping rapidly In addition, it is difficult to predict the various user requirements, especially due to the quick deployments of new applications. Also, recent history tells us that availability of more bandwidth will create its own demand through increasing utilization of bandwidth intensive applications. Another option: manage the existing bandwidth more efficiently Option - manage the existing bandwidth better, with a service model which uses bandwidth efficiently. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

A More Efficient Service Model
Quality of Service (QoS) Condition the network to provide predictability to an application even during high user demand Provide multiple levels of services Problems: signaling to facilitate service negotiation; charging scheme to support differentiated services Application adaptation Source rate adaptation based on network conditions - congestion control and efficient bandwidth utilization Problems: How adaptive applications work with QoS-assured services? How to motivate an application to adapt? Two models currently can lead to more efficent bandwidth usage: QoS and Application adaptation QoS: Provide value-added services with QoS expectations even during high user demand. Provide multiple levels of QoS to meet diverse user requirements. In general, adding multiple levels of QoS requires more network management, and greater user-network negotiation. Clearly, providing different services also means that we need a differentiated pricing structure. Adaptation schemes in literature assume user adapts source rate based on knowledge of network statistics (polling, etc.) - able to control congestion at high loads Literature work in multimedia adaptation assumes best effort service, users are well-behaved, would adapt even without any incentive 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Design Goals Develop an efficient service model which combines QoS assurance with user rate adaptation Increase service value to the users through greater choices over price and quality, improved connectivity, and expected QoS Reduce network provision complexity, improve network efficiency and increase revenue to the providers; allow network operator to create different trade-offs between blocking admissions and raising congestion prices To support multiple services with QoS - service providers need to provide a service selection and negotiation mechanism (well-known example - RSVP) For efficient resource usage - network should be able to commit resources for a short-term, dynamically re-configure resources based on demand and network conditions, particularly if users are adaptive Network should price services based on QoS, or resources consumed to provide a certain QoS, and allocate resources based on user willingness-to-pay. Pricing should also serve as a signal and/or incentive for users to adapt All of the above translate to two main requirements: ….. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Related Work Signaling for Resource Reservation
RSVP, YESSIR, SIBBS (Simple Inter-domain Bandwidth Broker Signaling protocol) Problems No support for service selection and negotiation No support for short-term resource commitment and dynamic resource negotiation Restricted to either sender-driven or receiver-driven No support for pricing and billing We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Related Work (cont’d) Adaptive Internet Multimedia Applications
Sender-driven, receiver driven, or transcoder-based Problems Fairness is one issue. TCP friendly adaptation may lead to unpleasantly abrupt changes in quality; buffering smoothes the abrupt change at the cost of high delay No mechanism for rate adaptation in QoS-enhanced environment No motivation for user adaptation We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Related Work (cont’d) Pricing and Billing in the Network
Total user benefit maximization based on welfare theory problems: rely on a centralized optimization process for total user utility maximization; assume knowledge (user’s truthful revelation ) of utility functions Pricing for congestion control problems: rely on well-defined source statistical model; not consider congestion control during sessions Pricing in multi-service environment problems: Qdlyzko’99 rely purely on pricing and user behaviors, without any service assurance; Kumaran’99 is restricted to special admission control algorithm We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Related Work (cont’d) Auction Mechanism
problems: signaling bursts, set-up delay, uncertainty of connection availability, user response to fluctuations in price Signaling Support for Pricing and Charging Very limited work in this area Karsten’98: estimated the cost for user request, no price quotation; restricted to IntServ We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Thesis Contributions Propose a Resource Negotiation And Pricing protocol: RNAP Enables user and network (or two network domains) to dynamically negotiate multiple services Supports price and charge collation, auction bids and results distribution Allows for service predictability, multi-party negotiation Designed to be scalable and reliable Can be embedded in other protocols, or implemented independently Enables differential charging for supporting differentiated services, reflecting the service cost and long-term user demand Support short-term resource commitment for better response to user demand and network conditions, and more efficient resource usage; congestion pricing to motivate user adaptation Develop reference user adaptation model Our work: We demonstrate a complete resource negotiation framework on test-bed network We show significant advantages of this framework relative to static resource allocation and fixed pricing using simulations. The resource negotiation framework provides much lower service blocking rate under resource contention, provides service assurances even under large or bursty offered loads. It provides higher perceived user benefit and higher network revenue 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Thesis Contributions (cont’d)
Demonstrate negotiation framework on test-bed and simulation Show significant advantages relative to static resource allocation and fixed pricing using simulations Our work: We demonstrate a complete resource negotiation framework on test-bed network We show significant advantages of this framework relative to static resource allocation and fixed pricing using simulations. The resource negotiation framework provides much lower service blocking rate under resource contention, provides service assurances even under large or bursty offered loads. It provides higher perceived user benefit and higher network revenue 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Pricing models User adaptation model Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Conclusion and future work Resource Negotiation Framework We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Protocol Architectures: Centralized (RNAP-C)
Host Resource Negotiator RNAP Messages Network Resource Negotiator NRN NRN NRN HRN HRN Access Domain - A We consider two alternative architectures for implementing RNAP in the network, a centralized architecture (RNAP-C) and a distributed architecture (RNAP-D) In RNAP-C, user negotiates through a HRN, each network domain has a NRN. In general, each NRN is in charge of admission control, monitoring network statistics, price quotation and charging for its domain. When a user wants to to apply for resources from the network, it first sends a request to the NRN of its access domain. This request is then propagated to next-domain NRN, and so on. Edge Router Access Domain - B Internal Router Intra-domain messages Transit Domain 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Protocol Architectures: Distributed (RNAP-D)
Local Resource Negotiator RNAP Messages HRN LRN LRN LRN LRN LRN LRN LRN LRN LRN HRN LRN LRN Access Domain - A LRN LRN Edge Router Access Domain - B In RNAP-D, Local Resource Negotiators (LRN) are implemented at each router. At network edge, LRNs dynamically configure traffic conditioners, based on on-going user requests. Internal Router Transit Domain 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

RNAP Messages Periodic negotiation
Query: Inquires about available services, prices Query Quotation Quotation: Specifies service availability, accumulates service statistics, prices Reserve Commit Reserve: Requests services and resources, Modifies earlier requests Periodic negotiation Quotation Commit: Confirms the service request at a specific price or denies it. Reserve We presents the messaging sequence between the users and network. This sequence also works between two network domains. If users do not need to find the service information, it could directly send the reserve message, instead of sending Query and wait for Quotation message first. If a user request negotiation functionality from network, the network will periodically provides the users with updated network status, allowing users to modify their requests. Commit Close: Tears down negotiation session Close Release: Releases the resources Release 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Message Aggregation (RNAP-D)
Turn off router alert Turn on router alert If each flow needs a RNAP session, it can’t scale. We consider a sink-tree based aggregation mechanism: aggregate messages when senders share the same destination network Messages merged by source or intermediate domains At network edge, the router alert function is turned off. As a result, the original per-flow messages will be tunneled directly to the destination network, without interception by intermediate RNAP agents; aggregate message reserves and collects price at intermediate nodes/domains Messages de-aggregated at destination border routers (RNAP-D), or NRNs (RNAP-C) Overhead Reduction Processing overhead, storage of states Edge Routers Sink-tree-based aggregation 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Message Aggregation (RNAP-D)
Turn on router alert Turn off router alert Sink-tree-based aggregation 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Block Negotiation (Network-Network)
Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics Bandwidth In the above aggregation scheme, all the flows need to be synchronized, which is not practical. Each flow arrival will modify the aggregation message, resulting high overhead. In practice, we proposed to use the block negotiation mechanism: aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics time 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Pricing models User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Conclusion and future work Resource Negotiation Framework We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Two Volume-based Pricing Strategies
Fixed-Price (FP): fixed unit volume price During congestion: higher blocking rate OR higher dropping rate and delay Congestion-dependent-Price (CP): FP + congestion-sensitive price component During congestion: users have options to maintain service by paying more OR reducing sending rate OR switching to lower service class Overall reduced rate of service blocking, packet dropping and delay During resource contention, fixed price based frame work can only block the future arrivals or drop and delay the packets that have arrived. If adaptive pricing is allowed, there are some other options for users: quality sensitive applications can maintain their resource levels by paying more, quality insensitive applications will reduce their sending rate or change to a lower service class. As a result, the blocking rate and average packet loss and delay of the whole network can get improved. We will show this later in our simulation. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Proposed Pricing Strategies
Holding price and charge: based on cost of blocking other users by holding bandwidth even without sending data phj =  j (pu j - pu j-1) , chij (n) = ph j r ij (n) j Usage price and charge: maximize the provider’s profit, constrained by resource availability max [Σl x j (pu1 , pu2 , …, puJ ) puj - f(C)], s.t. r (x (pu2 , pu2 , …, puJ ))  R cuij (n) = pu j v ij (n) Congestion price and charge: drive demand to supply level (tatonnement process or auction) We proposed a pricing model, which comprise holding price, usage price and congestion price: The holding price is set to be proportional to the difference of the usage price of two neighboring service classes The usage price is set to maximize the network revenue, under the constraint of network resources. The congestion price is set to drive user demand to the target network resource utilization We will consider the usage price and congestion price setup in more details. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Usage Price for Differentiated Service
Usage price based on cost of class bandwidth: lower target load (higher QoS) -> higher per-unit bandwidth price Parameters: pbasic basic rate for fully used bandwidth  j : expected load ratio of class j xij : effective bandwidth consumption of application i Aj : constant elasticity demand parameter Price for class j: puj = pbasic /  j Demand of class j: xj ( puj ) = Aj / puj Effective bandwidth consumption: xe j ( puj ) = Aj / ( puj  j ) Network maximizes profit: max [Σl (Aj / pu j ) pu j - f (C)], puj = pbasic /  j , s. t. Σl Aj / ( pu j  j )  C Hence: pbasic = Σl Aj / C , puj = Σl Aj /(C j) For an environment with multiple service classes, a class with lower target utilization level will provide higher QoS expectation, but it will also involve higher bandwidth cost Assume bandwidth are fully used, and the basic price is pbasic; , assume the expected load of s service class is  j the price of a service class can be set to be reverse proportional to the target load of a service class If we assume the fixed elasticity user demand, that is the user demand will be reverse proportional to the usage price of the bandwidth, we can obtain the effective bandwidth usage as: xe j ( puj ) = Aj / ( puj  j ) If we set the price so that the network revenue is optimized, we can obtain the basic bandwidth price as … the price of a class is inverse proportional to the target load of the class, as we have seen earlier. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Congestion Price: First Mechanism - Tatonnement
Tatonnement process (CPA-TAT): Congestion charge proportional to excess demand relative to target utilization pc j (n) = min [{pcj (n-1) +  j (Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ] ccij (n) = pc j v ij (n) We consider two models for setting up the congestion price In the tatonnement model, the congestion price is adjusted that the demand is driven to the supply. Generally, there is an upper limit of the congestion price. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Congestion Price: Second Mechanism - M-bid Second-price Auction
Auction models in literature: Assume unique bandwidth/price preference, one bid Service uncertainty: does not know about high demand until rejected Other issues: setup delay, signaling burst, user response to auction results M-bid auction Model Reduce uncertainty & provide complete user preferences: user bids (bandwidth, price) for a number of bandwidths, bids obtained by sampling utility function. Congestion price: charges highest rejected bid price Bandwidth allocations: higher valued bandwidth get allocated first; more users served Congestion control: auctions for period of time Reduce set-up delay: inter-auction admission We proposed an M-bid auction model, which is particularly useful for adaptive applications, with multiple preferences for different bandwidth. An application normally has higher value to the basic bandwidth, and hence would bid higher price for basic bandwidth. During network contention, the higher price bids get selected, and hence the most valued bandwidth get supported first, and hence more users could be allocated with their valuable bandwidth This model also support short-term resource auction, and allows inter-auction admission to reduce setup delay: as long as one bid from a user is higher than the recent bidding price, the user’s request can get admitted 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Example of M-bid Auction
Total capacity 70, congestion price is 2 Bid Bandwidth Bid Price Bidder Bid Selection 5 10 1 10 2 4 15 1 4 20 3 3.5 25 2 3 Cutoff 2 30 3 Congestion Price 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Rate Adaptation of Multimedia System
Gain optimal perceptual value of the system based on the network conditions and user profile Utility function: users’ preference or willingness to pay Cost U1 U2 Utility/cost/budget U3 Budget Bandwidth 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Example Utility Function
Utility is a function of bandwidth at fixed QoS An example utility function: U (x) = U0 +  log (x / xm) U0 : perceived (opportunity) value at minimum bandwidth  : sensitivity of the utility to bandwidth Function of both bandwidth and QoS U (x) = U0 +  log (x / xm) - kd d - kl l , for x  xm kd : sensitivity to delay kl : sensitivity to loss 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Rate-Adaptation Models
Model1: User adaptation under CPA-TAT (tatonnement-based pricing) Gaining optimal transmission rate by optimizing perceived surplus of the multimedia system subject to budget and application requirements U = Σi Ui (xi (Tspec, Rspec)] max [Σl Ui (xi ) - Ci (xi) ], s. t. Σl Ci (xi)  b , xmini  xi  xmaxi Determine optimal Tspec and Rspec With the example utility functions, resource request of application i: Without budget constraint: x i = i / pi With budget constraint: x i = bi / pi, with b i = b ( i / Σl  k) Model2: User adaptation under CPA-AUC (second-price auction) Adapt rate based on allocated bandwidth/QoS When the total resource requirements of all applications are within user’s budget, the resource allocated is set to be the user’s willingness to pay bandwidth When the total resource requirements of all applications are beyond user’s budget, the budget for a system is first distributed to each application based on users’ willingness to pay for that application, and the allocated budget is used to find the optimal resource allocation for an application In the auction model, the network will allocate the resources based on the users’ bidding. The application will adapt based the allocated price 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Testbed Architecture Demonstrate functionality and performance improvement: blocking rate, loss, delay, price stability, perceived media quality Host HRN negotiates for a system Host processes (HRN, VIC, RAT) communicate through Mbus Network Router: FreeBSD ALTQ 2.2, CBQ extended for DiffServ NRN: (1) Process RNAP messages; (2) Admission control, monitor statistics, compute price; (3) At edge, dynamically configure the conditioners and form charge Inter-entity signaling: RNAP RAT VIC Mbus HRN RNAP NRN The main objective behind our testbed work was to demonstrate the functionality of our resource negotiation framework, including RNAP, pricing, and user adaptation. We were also able to demonstrate performance improvements relative to a non-adaptive, fixed-price environment in terms of blocking rate, average loss and delay, price stability, and perceived media quality . 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Pricing models User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Conclusion and future work Resource Negotiation Framework We will first present the background of this work We then describe the proposed resource negotiation framework, which consists of a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model. Finally, we introduce our test-bed setup and also present some simulation results. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Simulation Design Performance comparison: fixed price policy (FP) vs. congestion price based adaptive service (CPA) loss, delay, blocking rate, user benefit, network revenue, stability Three groups of experiments: effect of traffic load, admission control, and load balance between classes Weighted Round Robin (WRR) scheduler Three classes: EF, AF, BE EF: load threshold 40%, delay bound 2 ms, loss bound 10-6 AF: load threshold 60%, delay bound 5 ms, loss bound 10-4 BE: load threshold 90%,delay bound 100 ms,loss bound 10-2 Sources: mix of on-off traffic and Pareto on-off traffic 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Simulation Architecture
Topology 1 (60 users) Topology 2 (360 users) 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Effect of Traffic Load CPA maintains the traffic load at the targeted level, meets the expected performance bounds Without admission control, we can see the delay and loss increase almost linearly when the load is beyond the target level, and the targeted service are seriously violated. If adaptive framework is used, all the targeted service are assured. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Effect of Admission Control
Admission control is important in maintaining the expected performance of a class. With admission control the loss and delay can be maintained at target level even under high load. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Effect of Admission Control (cont’d)
With admission control, the dynamics of the network price can be better controlled. Coupled with user adaptation, the blocking rate of CPA is up to 30 times smaller than that of FP. This is at the cost of higher user blocking rate. Combining admission control and user adaptation, the blocking rate can be greatly reduced (up to 40 times) Compared to without admission control, the price dynamics is reduced for the dynamic pricing scheme. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Effect of Admission Control (cont’d)
CPA allows for higher network revenue and user benefit. The network revenue is kept almost constant when admission control is used, since the load above targeted level is blocked. The CPA framework gain much higher revenue since it serves the high valued customers. Since we have using the example user utility function that is sensitive to the loss and delay, the average user benefit of the static service drop sharply due to the higher loss and delay at high load. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Load Balance Between Classes
Even when a small portion of users (15%) select other service classes, the performance of the over-loaded class is greatly improved. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Other Results Users with different demand elasticity share bandwidth proportional to their willingness to pay Even a small proportion of adaptive users (e.g 25%) results in a significant performance improvement for the entire user population (18% improvement) Performance of CPA further improves as the network scales and more connections share the resources Both M-bid auction and tatonnement process can be used to calculate the congestion price; auction gives higher perceived user benefit and network utilization at cost of implementation complexity and setup delay 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Conclusions Proposed a dynamic resource negotiation framework consisting of: A Resource Negotiation And Pricing protocol (RNAP) , a rate and QoS adaptation model, and a pricing model RNAP: supports dynamic service negotiation Pricing models: based on resources consumed by service class and long-term user demand; including congestion-sensitive component to motivate user demand adaptation Performance: Effectively restricts load to targeted level and meet service assurance Provide lower blocking rate, higher user satisfaction and network revenue Admission control and inter-service class adaptation give further improvements in blocking rate and price stability 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Future Work Interaction of short-term resource negotiation with longer-term network provision A light-weight resource management protocol Cost distribution in QoS-enhanced multicast network Pricing and service negotiation in the presence of alternative data paths or competing networks User valuation models for different QoS Resource provisioning in wireless environment 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Joint work with Dilip Kandlur, and Dinesh Verma (IBM Research)
Measurements and Analysis of LDAP Performance Joint work with Dilip Kandlur, and Dinesh Verma (IBM Research) Xin Wang, Columbia University

Motivation and Experiment
Lightweight Directory Access Protocol (LDAP): widely used, but little study of performance Related work: Mindcraft’98 treated LDAP server as black box, did not study the influence of system components Thesis contributions: Developed a benchmark tool to analyze the performance of LDAP Provided guidelines for the configuration of LDAP client and server Suggested schemes for LDAP performance improvement Is the first effort that addressed the performance issues and configuration issues for the widely used LDAP Usage context for thesis: management for network resources, pricing policies cachesize: size in entries of in-memory cache; variable size dbcachesize: size in bytes of the in-memory cache associated with each open index file; 10 MB 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Experimental Setup & General Results
Hardware: server- dual Ultra-2 processors, 200 MHz CPUs, 256 Mb memory; Clients- Ultra1, 170 MHz CPU, 128 MB memory; 10 Mb/s Ethernet LDAP server: OpenLDAP 1.2, Berkeley DB Search filter: interface address, and corresponding policy object Default parameters: 10,000 entries, entry size 488 bytes General results: response latency 8 ms up to 105 requests/second Maximum throughput 140 requests/second 5 ms processing latency - 36% from backend, 64% from front end Connect time dominates at high load, and limits the throughput 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Scalability of the Performance
Scaling with Directory Size - determined by back-end processing In memory operation, 10,000 -> 50,000: processing time increases 60%, throughput reduces 21%. Out-of-memory, 50,000 ->100,000: processing time increases another 87%, and throughput reduces 23%. Scaling with Entry Size (488 ->4880 bytes): In-memory, mainly increase in front-end processing, i.e., time for ASN.1 encoding . Processing time increases 8 ms, 88% due to ASN.1 encoding, and throughput reduces 30%. Out-of-memory, throughput reduces 70%, mainly due to increased data transfer time. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Performance Improvement
Disabling Nagle algorithm: reduces latency about 50 ms Entry Caching: for 10,000 entry directory, caching all entries gives 40% improvement in processing time, 25% improvement in throughput CPU: For in-memory operation, dual processors improve performance by 40% Connection Re-use: 60% performance gain when connection left open 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

IP Multicast Fault Recovery
in PIM over OSPF Joint work with Chien-ming Yu (Microsoft) Paul Stirpe and Wei Wu (Reuters) Xin Wang, Columbia University

Motivations Many IP multicast applications require high availability, especially mission-critical real-time data A lot of work on reliable multicast, but little work on multicast fault tolerance Study failure recovery in a complete architecture: IGMP + OSPF (unicast) + PIM (multicast) Focus: the interplay of underlying protocols; the interactions of failure recovery, between routers, links, WAN and LAN Method: quantitative analysis; simulation over OPNET; study failure recovery and implementation issues on test-bed using Cisco routers Many IP multicast applications require high availability, for example, near real-time dissemination of financial information; Little work has been done in this field. OSPF: rapid fault recovery properties, widespread use, support of parameteric tuning; while RIP has long, non-tunable fair-over periods. PIM (DM and SM): dominant multicast routing protocols. DVMRP or CBT resemble dense and sparse mode respectively. Since fault recovery for rendezvous point (RP) for PIM SM has been studied extensively, our focus is on the sequence of events and interactions under different failures, between router, link, WAN and LAN; Consider single link and router faults. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Results General observations
Channel recovery time: dominated by unicast table re-construction time. Protocol control loads: PIM-DM control load increases proportionally with the redundancy factor and decreases inversely with the percentage of receivers Below certain time interval threshold, OSPF load is dominated by Hello messages and increases proportionally as OSPF Hello interval decreases Neither PIM nor OSPF has high control traffic during failure recovery. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Results (cont’d) PIM Enhancement for Fault Recovery
Fast recovery from Dedicated Router (DR) failure: reduce Hello-Holdtime to detect neighbor failure faster; Backup DR; IGMP group information caching in all LAN routers (reloading group membership information leads to minutes of delay) Fast recovery from last-hop router failure: DR records the last-hop router address, actively recover, instead of waiting for an IGMP report to reactivate its oif to the LAN (up to minutes of delay) Use interrupts instead of polling to reduce delay 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Some References X. Wang, H. Schulzrinne, “Auction or Tatonnement - Finding Congestion Prices for Adaptive Applications”, submitted. X. Wang, H. Schulzrinne, “Pricing Network Resources for Adaptive Applications in a Differentiated Services Network,” In Proceeding of INFOCOM'2001, April 22-26, Anchorage, Alaska. X. Wang, H. Schulzrinne, “An Integrated Resource Negotiation, Pricing, and QoS Adaptation Framework for Multimedia Applications,” IEEE JSAC, vol. 18, Special Issue on Internet QoS. X. Wang, H. Schulzrinne, “Comparison of Adaptive Internet Multimedia Applications,” IEICE Transactions on Communications, Vol. E82-B, No. 6, pp , June 1999. X. Wang, H. Schulzrinne, D. Kandlur, D. Verma, “Measurement and Analysis of LDAP Performance,” International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS'2000). X. Wang, H. Schulzrinne, C. Yu, P. Stirpe, W. Wu, “IP Multicast Fault Recovery in PIM over OSPF,” In 8th International Conference on Network Protocols (ICNP'00), Also appears at ACM SIGMETRICS’2000 as short paper. 2/24/2019 Xin Wang, Henning Schulzrinne, Columbia University Xin Wang, Columbia University

Questions and Answers Thanks ! Xin Wang, Columbia University

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