Presentation on theme: "ACOE 422 Network Design and Planning Issues and Performance Evaluation."— Presentation transcript:
ACOE 422 Network Design and Planning Issues and Performance Evaluation
Outline Network Design Radio Network Planning Performance Evaluation Case Study 1: WLAN Coverage Planning Case Study 2: WLAN Performance Evaluation
Introduction Wireless networks rely on an inexpensive but prone to errors medium (air) with limited bandwidth We require wireless networks to be Functional Affordable Scalable Flexible Manageable Secure Resilient and Reliable Meet the growing user demands (e.g. of bandwidth) Low cost of ownership consistent with these objectives Network Design and Planning is very essential!
Network Design Specify network architecture Define radio access network design and engineering Define core network design and engineering Provide detailed protocol design Traffic Modeling Decide on voice and data applications Mobility Modeling Mobility assessment and design is important Complete area plans Provide performance and bottleneck analysis Specify security and redundancy plans
Network Design: considerations Services & Traffic: How much and where? Impact on network quality, efficiency and cost What is best design strategy (given an imprecise demand forecast)? Coverage vs. capacity, cell breathing (UMTS) Ability to use existing sites (e.g. GSM) Meet budget and cash flow constraints
Radio Network Planning RNP includes: Dimensioning Detailed Coverage & Capacity Planning Network Optimization Dimensioning estimates: an approximate number of base station sites base stations and their configurations other network elements based on the operators requirements and the radio propagation in the area Dimensioning must fulfil certain requirements for: Coverage Capacity Quality of Service (QoS)
Radio Network Planning Coverage and Capacity Planning Determine the coverage regions, area type information and propagation conditions Determine the available spectrum and traffic density information Note: In W-CDMA networks (e.g. UMTS), capacity and coverage are closely related both must be considered simultaneously in the planning process Network Optimization Provide optimal coverage probability, blocking probability and end user throughput
Radio Network Planning Outputs during RNP: Rough number of base stations and sites Base station configuration Site selection Cell specific parameters for RRM & adjusting of RRM parameters to optimal values Analysis in the issues of capacity, coverage and QoS
Performance Evaluation Takes place prior to the deployment of a system Assesses a systems capabilities Evaluates any new mechanisms the system will use Note: System = a collection of related entities that interact together over a time to accomplish a goal E.g. to deliver telecommunication services that satisfy specific QoS requirements
Performance Evaluation Two ways to achieve performance evaluation of any system Experiment with the actual system Experiment with a model of the system Experiment with the actual system Set up the system and run it Collect measurements that will aid in the assessment of the system Exact results but costly Often the system is not available
Performance Evaluation Experiment with a model of the system The model can be physical or abstract abstract = representation of the system containing structural, logical or mathematical relationships The physical model is evaluated similarly to an actual system The abstract model may be evaluated in two ways: Analysis (mathematical analysis) Simulation Mathematical analysis Costly Requires specialized knowledge Often several approximations need to be made (for complex systems) hard to generalize results Simulation becomes more and more popular
Performance Evaluation Simulation Simulation models may be categorized according to the type of input data they accept Deterministic Stochastic Simulation models may be categorized according to the factors that cause system state to change Continuous (time-based) Discrete event-based (still requires a time-keeping mechanism to advance from one event to another)
Performance Evaluation Evaluation of a system Experiment with the actual system Costly Often the system is not available Experiment with a model of the system Physical model Abstract model Analytical evaluation (mathematical analysis) Costly Approximations due to complexity Simulation Categorized according to the type of input data it accepts: Deterministic Stochastic Categorized according to the factors that cause system state to change Continuous Discrete Event Based
Conclusions Review your existing applications and infrastructure Incorporate, as needed, wireless access points, routers, gateways, security devices and middleware Determine connectivity requirements for your network and mobile devices Integrate seamlessly with your current and future IT infrastructure Evaluate performance, scalability, and availability metrics Leverage simulation and modeling tools to help ensure consistent quality of service Assess server capacity and network coverage Ascertain security and management requirements Provide maximum security for the whole network infrastructure
Case Study 1: WLAN Coverage Planning Paper: WLAN Coverage Planning: Optimization Models and Algorithms, E. Amaldi, A. Capone, M. Cesana, F. Malucelli, F. Palazzo
Case Study 1: WLAN Coverage Planning WLAN medium access mechanism: listen before talk approach if a user terminal is covered by more than 1 AP and is transmitting/receiving to/from one of them, the other APs cannot transmit/receive to/from other users. causes limited system capacity when coverage areas overlap Appropriate positioning of APs is crucial
Case Study 1: WLAN Coverage Planning Simple way to plan coverage consider a set of possible positions of user terminals in the service area consider a set of AP candidate sites select a subset of sites in which to install APs so as to guarantee a high enough signal level to all user terminals Problem Minimizing the number of APs that cover the complete set of user terminals is an NP-hard task (a.k.a. cardinality set covering problem)
Case Study 1: WLAN Coverage Planning Heuristics are adopted to provide a sub-optimal solutions Not all such solutions provide acceptable levels of capacity and QoS Proposed solution: 2 phases: greedy approach & local search The greedy phase starts from an empty solution and iteratively adds to the current solution the candidate site which maximizes a certain benefit function (calculated for each candidate site) The local search phase takes as an input the solution provided by the greedy phase. Then the site neighborhood is explored for a better solution, using an objective function. The final solution is the one with the highest objective function.
Case Study 1: WLAN Coverage Planning Conclusions Coverage planning for WLANs is a hard task An optimal solution is NP-hard A sub-optimal approach is usually taken Proposed approach uses heuristics and is composed of two phases: the greedy phase and the local search phase Results show that this approach achieves better overall capacity than the classical approach, which is based on the minimum cardinality set covering problem.
Performance evaluation IEEE (XIV) Unicast data transfer DIFS data ACK other stations receiver sender t data DIFS waiting time contention SIFS –station has to wait for DIFS before sending data –receivers acknowledge after waiting for a duration of a Short Inter-Frame Space (SIFS), if the packet was received correctly
Masters thesis edu/dgoodman/fai nberg.pdf
Case Study 2: Performance Evaluation of Wireless LANs Paper: Enhancements and Performance Evaluation of Wireless Local Area Networks, Jiaqing Song and Ljiljana Trajkovic. Performance Evaluation is done using the OPNET simulation tool.
Case Study 2: Performance Evaluation of Wireless LANs Known problems with WLANs WLAN media is error prone (very high BER) Hidden Terminal problem decreases performance Carrier Sensing (for collision detection) is difficult a station is incapable of listening to its own transmissions Investigate 3 approaches for improving WLAN performance tuning the physical layer related parameters tuning the IEEE parameters using an enhanced link layer (MAC) protocol
Case Study 2: Performance Evaluation of Wireless LANs OPNET WLAN models WLAN station IEEE WLAN station includes ON/OFF traffic source includes sink includes WLAN interface includes receiver/ transmitter pair
Case Study 2: Performance Evaluation of Wireless LANs OPNET WLAN models WLAN workstation workstation with client/server applications running over TCP/IP and UPD/IP supports IEEE connections at 1Mbps, 2Mbps, 5.5Mbps or 11Mbps (speed is determined by data rate of connecting link) WLAN server server with applications running over TCP/IP and UDP/IP supports IEEE connections at 1Mbps, 2Mbps, 5.5Mbps or 11Mbps (speed is determined by data rate of connecting link)
Case Study 2: Performance Evaluation of Wireless LANs OPNET WLAN models WLAN access point wireless router Ethernet interface connects the wireless network to wired networks
Case Study 2: Performance Evaluation of Wireless LANs Approach 1: tuning the physical layer related parameters Modified OPNET wlan_mac process to introduce 4 parameters Slot time SIFS time Minimum contention window Maximum contention window To enable choose customized option for Physical Characteristics
Case Study 2: Performance Evaluation of Wireless LANs Approach 1: tuning the physical layer related parameters Scenario with 2 WLAN stations WLAN stations have no TCP or higher layers, therefore reflect the performance of MAC layer protocols more accurately First set of simulations demonstrates the effect of Slot time and Short Inter-frame Space (SIFS) on WLAN performance Second set of simulations demonstrates the effect of Minimum Contention window on the average media access delay
Case Study 2: Performance Evaluation of Wireless LANs Approach 1: tuning the physical layer related parameters Simulation set 1 media access delay in first node is collected media access delay = queue delay + contention delay Results: smaller slot time and SIFS decrease the average media access delay improved performance
Case Study 2: Performance Evaluation of Wireless LANs Approach 1: tuning the physical layer related parameters Simulation set 2 media access delay is again collected Results: setting Min contention window to a smaller value (in the case when there are few WLAN stations in the network) decreases media access delay improved performance
Case Study 2: Performance Evaluation of Wireless LANs Approach 2: tuning the IEEE parameters A BER generator was developed and integrated in the wlan_station model Nine simulation scenarios with various combinations of values for BER and Fragmentation threshold to demonstrate the effects of the fragmentation threshold Throughput is collected Throughput represents the rate of data successfully received by other stations
Case Study 2: Performance Evaluation of Wireless LANs Approach 2: tuning the IEEE parameters Results show that for low BER various fragmentation threshold have no significant effect on the WLAN performance.
Case Study 2: Performance Evaluation of Wireless LANs Approach 2: tuning the IEEE parameters Results show that for relatively high BER, a small fragmentation threshold can significantly improve WLAN performance.
Case Study 2: Performance Evaluation of Wireless LANs Approach 2: tuning the IEEE parameters Results show that for relatively low BER, a very small fragmentation threshold can significantly deteriorate WLAN performance, because of the heavy packet overhead.
Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC) protocol Adaptive back-off mechanism was examined This mechanism can be implemented on top of the existing access scheduling protocol and does not introduce additional overhead. The main idea of the mechanism is to estimate the shared channel by calculating the slot utilization ratio. High utilization possible collision back-off
Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC) protocol adaptive back-off mechanism was implemented and integrated into the wlan_mac process model.
Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC) protocol Three simulation scenarios with various numbers of identical WLAN stations Data is sent at an average rate of 820kbps Destination stations are randomly chosen by the source station Results collected for analysis include: Throughput (rate of data successfully received by other stations) Load (rate of data sent to other stations)
Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC) protocol Results: with the adaptive back-off mechanism load can be greatly reduced while throughput can still achieve the same or higher value. the mechanism can effectively reduce the number of collisions and data loss
Case Study 2: Performance Evaluation of Wireless LANs Approach 3: using an enhanced link layer (MAC) protocol Results: throughput/load behavior of WLAN with more nodes is consistent
Case Study 2: Performance Evaluation of Wireless LANs Conclusions 3 methods for improving WLAN performance were implemented in OPNET Tuning the physical layer characteristics can greatly improve network performance Properly chosen values for fragmentation threshold improves WLAN performance when BER is high The adaptive back-off algorithm in the MAC layer can effectively reduce the number of collisions This case study used simulation as the performance evaluation method and came to its conclusions after a series of simulation sets for different scenarios