Presentation on theme: "Network Design 1 May 2008 Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703."— Presentation transcript:
Network Design 1 May 2008 Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703
CERDEC-021.22/8/201410/21/04 Briefing Outline Background on Networks Network Design Issues STCD Network Design Program
CERDEC-021.32/8/201410/21/04 Distribution Statement This briefing has been previously cleared for public release. The comments recommendations and conclusions are solely those of the author, and not of the Army or DOD.
Background on Networks
CERDEC-021.52/8/201410/21/04 Mobile Ad Hoc Networks (MANETs) vs Commercial Networks Mobile Ad Hoc Networks (MANETs) do not have a fixed infrastructure or backbone MANET connectivity is determined by node location, mobility, and RF propagation characteristics. Commercial Networks ( Digital Cellular ) have fixed infrastructure backbone of Base Stations and Cell Towers that are interconnected thru high bandwidth Fiber Optic cable. In Digital Cellular, only the last hop is wireless. Other Commercial wireless networks ( IEEE 802.xx ) are usually static in nature. The MANET model of networks, with extensions for multi-tier operation, is most applicable to Army Networks such as FCS and WIN-T.
CERDEC-021.62/8/201410/21/04 MANET Networks User Traffic and RF connectivity are in fact probabilistic and stochastic quantities User node mobility is also a stochastic quantity Node interconnection, as determined by RF propagation, leads to a time dependent, stochastic topology graph -Classic Graph Theory has limited application for MANETS due to the inability of represent link to link coupling and node mobility explicitly. Hence the mathematics of networks must include both a stochastic nature as well as a combinatoric nature In WIRELESS MANETS, both the Network and the Traffic loads are stochastic in nature -In WIRED Networks typically only the Traffic is stochastic Hence MANET Network Design is a HARD problem!!
CERDEC-021.72/8/201410/21/04 Network Analysis vs. Network Design (Synthesis) The Analysis Problem: -Given a network solution perform an performance analysis typically for »User Requirements for throughput, delay, reliability »Network survivability »Network connectivity The Design Problem: -Design is the Synthesis Problem -Given what you want ( as given requirements), how do you go about creating the network solution that meets (or exceeds the requirements? -This is a creative and inventive process
Network Design Issues
CERDEC-021.92/8/201410/21/04 Specific Network Design Issues Design metrics for MANETs -Connectivity ( in mobile ad hoc environment ) -ETE User Requirements -Survivability ( in mobile ad hoc environment ) -Optimality or goodness of design to be used in comparisons of different designs RF Connectivity representation ( including mobility ) Steady state vs transients in network operation Closed loop vs open loop for adaptive/dynamic algorithms Scalability ( to arbitrary network sizes ) Multi-tiered Operation -Widely differing RF characteristics link highly dispersed users Design solution should be insensitive to traffic loads, operational scenarios -Typically requirements are uncertain and/or subject to change
CERDEC-021.102/8/201410/21/04 Specific Network Design Issues (Concluded) Security Optimality in the face of uncertain requirements, scenarios, and RF environments RF Waveform Design for Jamming Environment Validation issues -Does the design meet/exceed requirements -Typically requires extensive testing Verification Issues -Does the design have good properties that are common to all designs COST, COST, COST
CERDEC-021.112/8/201410/21/04 Network Analysis / Design Techniques Analytic Modeling -Define/develop mathematical equations for network behavior – connectivity, capacity, thruput/goodput, delay, survivability, etc -Very difficult to get good closed form solutions -A 6.1 and academic focus Simulation -Develop Discrete Time Event Driven simulation for the Network -Execute simulation over range of traffic loads, topologies, mobility patterns, failure/destruction patterns -Requires a large number of runs to get solutions -OPNET, Qualnet, etc
CERDEC-021.122/8/201410/21/04 Network Analysis/Design Techniques ( Concluded ) Prototyping -Build a solution, make measurements on it. -Collect a large amount of data and then use data driven modeling techniques to define necessary relationships -Rutgers WIN-LAB ORBIT is good example Emulation -Build a scaled or abstracted version of the network solution -make measurements and develop data driven relationships
CERDEC-021.132/8/201410/21/04 Mathematics Potentially Applicable to Network Design and Analysis Graph Theory -Topology representation and analysis – but not sufficient for MANET Closed Loop control theory -Assumes a network operating point that is to be maintained -Need to add delay/ loss model to control model Stochastic Processes -Random or statistical nature of traffic load, node mobility, and RF channel, Discrete Time Markov Processes, Fractals, Jackson Networks. BCMP Networks Discrete Event Transition Models -Finite State machines, Petri-Nets, etc -To represent control interaction with system state Optimization Theory as applied to Network Design -Primal/Dual, hill climbing, annealing, Genetic Algorithms, Robust Optimization, Stochastic Optimization VERY MESSY INDEED
CERDEC-021.142/8/201410/21/04 Overall MANET Network Design Elements The protocol/stack design -individual protocols -cross layer issues -protocol parameters/configuration The node design -Radio hardware design -Antenna Modifications -Waveform Modifications The Recommended Network Architecture -Relay function for multi-hop networks -Addition relay (like UAV or UGV) capability may be needed to achieve network goals of connectivity, capacity, and survivability -Domain sizing and organization -Interconnection points
CERDEC-021.152/8/201410/21/04 Cross Layer Design Issues (Goldsmith, et. al.) Multiple Antennas Coding/Modulation Power Control Adaptive Link Techniques for Power Control, Scheduling, and link selection Neighbor selection / maintenance Delay/energy constrained routing
CERDEC-021.162/8/201410/21/04 Network Layer Design Issues Network Initialization Time (Cold Start) Node Join Time To Existing Network Group Node Join Time Node Leave Time From Existing Network Group Node Leave Time Network Recovery Time ( after node / link failure ) Network Overhead ( packets/sec ) Processing Resource Requirements ( CPU cycles/memory) QOS/Data Handling
CERDEC-021.192/8/201410/21/04 Background on Engineering Network Design Tools Design Tools exist for wired backbone networks, such as Digital Cellular and POTS. Some design tools exist for IEEE802.xx type networks, but are typically limited only to connectivity determination and node placement. Some design Tools exist for Satellite Networks but are limited primarily to up/down link design.
CERDEC-021.202/8/201410/21/04 Why a Network Design Toolset? Due to network complexity, many relationships may be required to accurately describe network behavior These relationships will need to be coupled and use in a coordinated fashion to produce a complete network design Tools/Toolsets have be used successfully in many other areas: -Cell phone network design -Design of Boeing 777 -VLSI circuit design -Layout of internal spaces on submarines But not ad hoc network design ( yet!)
CERDEC-021.212/8/201410/21/04 Network Design vs Network Planning Network Planning -Used by Military user in Operational environment to plan deployment of existing network assets to meet operational needs in theater. -Assumes that Network hardware/software is fixed and designed. Network Design -Used by Engineering community -protocol/stack design, node design, and network architecture -provides rapid design/trade offs of design options -validated thru detailed OPNET ( or equivalent ) simulations, prototyping, or experimentation
CERDEC-021.222/8/201410/21/04 Approaches to Network Design Analytic Approach -Human must create, discover or invent mathematical relationships/heuristics -Many assumptions required for mathematical tractability -Typically, one function at a time is all that can be represented mathematically ; hence many coupled relationships may be required to completely describe ad hoc networks -In many areas, these analytic relationships do not exist Discrete Time Event Driven Simulation Approach -The human must do the design, and the simulation does the computation; The human does the analysis of simulation results -Very detailed, fine grained simulations are possible -Specific solutions to specific input sets (a big calculator) -Many runs needed to vary parameters and get statistically valid results -Little to no insight as to network behavior or what effects what Prototyping / experimental testing Approach -Must have a network built first -Instrumentation issues to get accurate measurements -Much data can be collected --> challenging data analysis SHOULD BE DONE TOGETHER IN CONSISTENT FASHION to achieve good network design
CERDEC-021.232/8/201410/21/04 Network Engineering Design Analytic Toolset (NEDAT) NEDAT Development Approach Build a little/test a little Start with small numbers of nodes (~15) Increase number of nodes (~100, ~500, ~1000) Add more refined behavior as Network Science results become available. NEDAT INPUTS NUMBER OF NET NODES LOCATION OF NET NODES RANGE OF XMIT POWER RF REPRESENTATIONS RANGE OF RF LINK RATES NET CONNECTIVITY NET END TO END CAPACITY NET SURVIVABILITY GENERIC PROTOCOLS MOBILITY PATTERNS NEDAT OUTPUTS 6.1 RESEARCH NETWORK DESIGN EQUATIONS/RELATIONSHIPS 6.1 RESEARCH NETWORK DESIGN EQUATIONS/RELATIONSHIPS ADDITIONAL NODES AT LOCATIONS NEEDED?? ACTUAL XMIT POWER ACTUAL DATA RATES PROTOCOL STACK REQUIRED PARAMETERS Network input goals/ranges. Network output - What can the network do?
CERDEC-021.242/8/201410/21/04 Detailed OPNET Simulation used to validate Network Design Produced by NEDAT Goals Met? NEDAT Discrete Time Event driven OPNET Simulation Scenario/Traffic Load Connectivity, Capacity, Survivability Design Equations Heuristics From Network Science Network Design High Level Node Representation Ideas for Refinement NoYes Recommend Lab/Field Experimentation Network Engineering Design Analytic Toolset (NEDAT)
CERDEC-021.252/8/201410/21/04 What is Cognitive Networking (CN)? Cognitive Networks (CN) are characterized by advanced hardware and software that -Interacts proactively with the environment (RF, traffic load, mobility, mission profile, etc) -Uses learning, knowledge representation, estimation, predictive and optimization techniques for (near) real time network control (i.e. protocols), and network and spectrum management -Provides enhanced performance (user thruput, delay, loss, survivability) and spectral efficiency over conventional techniques »Conventional techniques use fixed algorithms with variable parameters, while Cognitive Networks use variable algorithms with variable parameters in stable, non-oscillatory, adaptive fashion (MILITARY) Cognitive Networking is the application of the above techniques to the MANET Technology
CERDEC-021.262/8/201410/21/04 Cognitive Networking vs Traditional Networking Cognitive Networking will solve the well known MANET networking problems in a different and better way when compared to traditional approaches -Interact, learn, and respond to networking environment -Algorithms change their computation logic (based on the environment) as well as usual algorithm parameters -Provide less overhead thru the use of advanced techniques (i.e. compute, dont communicate) such as predictive, estimation, local reasoning, etc. The Networking problems to be addressed remain the same -Network Ops, Control/Management -Protocols / Cross layer stack design -Security -Multi-tiered Network Transport
CERDEC-021.272/8/201410/21/04 Why Cognitive Networking Design (CND) is hard? Mobile Ad Hoc Networking design is hard in general; the addition of cognitive capabilities increase the design space/options Many options and alternatives need to be examined and explored Traditional simulation (i.e. OPNET) approaches have limitations regarding model development, fidelity, scalability and are actually analysis and not design tools Many of learning, estimation, reasoning, optimization and other techniques have not been applied to the highly mobile, large scale, distributed, dynamic ad hoc networking for the hostile battlefield environment.
CERDEC-021.282/8/201410/21/04 Why a CN Design Tool? MANETs in general are hard to design due to the large design space Using Cognitive Networking technology only increases the design space options and adds complexity to the process with the expectation of enhanced performance and efficiency of operation. Using a modeling approach to present both cognitive and non-cognitive functions in an engineering tool environment will allow rapid and effective exploration of a large number of design options and alternatives. Using a modeling approach, critical issues such as scalability, performance, and behaviors may be explored and investigated at minimal cost without committing to large amounts of physical hardware or expensive testing
CERDEC-021.292/8/201410/21/0410/21/04 Cognitive Network Design (CND) Considerations Design metrics for MANETs -Connectivity ( in mobile ad hoc environment ) -End-to-end User Requirements -Survivability ( in mobile ad hoc environment ) -Optimality or goodness of design to be used in comparisons of different designs Knowledge oriented representation of RF connectivity (including mobility), network operation/behaviors Effectiveness of learning/prediction techniques in dynamic environment Steady state vs. transients in network operation (stability issues) Design solution should be insensitive to traffic loads, operational scenarios/requirements and environments -Typically these are uncertain and/or subject to change
CERDEC-021.302/8/201410/21/04 CN Design Tool Software Functional Architecture Functional Model Repository Design Manager Analytic Tool-box Input Module Output Module ??? DES Adaptor External DES Prediction/EstimationLearning Knowledge Database
CERDEC-021.312/8/201410/21/0410/21/04 The Payoff of Cognitive Networking The ability to say, with a high level of confidence that the network will work in the military dynamic environment A potential to perform cost-performance trade offs for various cognitive network design through analysis for large military ad-hoc networks A new capability to optimize the design for performance functionality, and capabilities of mobile ad-hoc networks A better understanding of complex network behaviors New capability to optimize engineering design of large, multi-tiered, highly mobile, ad hoc networks