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Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,*,*,An Information-theoretic Approach to Network Measurement and Monitoring,Yong Liu, Don Towsley, Tao Ye, Jean Bolot,1,Outline,motivation,background,flow-based network model,full packet trace compression,marginal/joint,coarser granularity,netflow and SNMP,future work,2,Motivation,network monitoring: sensing a network,traffic engineering, anomaly detection, ,single point v.s. distributed,different granularities,full traffic trace: packet headers,flow level record: timing, volume,summary statistics: byte/packet counts,challenges,growing scales: high speed link, large topology,constrained resources: processing, storage, transmission,30G headers/hour at UMass gateway,solutions,sampling: temporal/spatial,compression: marginal/distributed,3,Questions,how much can we compress monitoring traces?,how much information is captured by different monitoring granularity?,packet trace/NetFlow/SNMP,how much joint information is there in multiple monitors?,joint compression,trace aggregation,monitor placement,4,Our Contribution,flow-based network models,explore temporal/spatial correlation in network traces,projection to different granularity,information theoretic framework,entropy: bound/guideline on trace compression,quantitative approach for more general problems,validation against measurement from operational network,5,Entropy & Compression,Shannon entropy of discrete r.v.,compression of i.i.d. symbols (length M) by coding,coding:,expected code length:,info. theoretic bound on compression ratio:,Shannon/Huffman coding,assign short codeword to frequent outcome,achieve the H(X) bound,6,Entropy & Correlation,joint entropy,entropy rate of stochastic process,exploit,temporal correlation,Lempel-Ziv Coding: (LZ77, gzip, winzip),asymptotically,achieve the bound for stationary process,joint entropy rate of correlated processes,exploit,spatial correlation,Slepian-Wolf Coding: (distributed compression) encode each process,individually, achieve,joint,entropy rate in limit,7,Network Trace Compression,nave way: treat as byte stream, compress by generic tools,gzip compress UMass traces by a factor of 2,network traces are highly structured data,multiple fields per packet,diversity in information richness,correlation among fields,multiple packets per flow,packets within a flow share information,temporal correlation,multiple monitors traversed by a flow,most fields unchanged within the network,spatial correlation,network models,explore correlation structure,quantify information content of network traces,serves as lower bounds/guidelines for compression algorithms,8,Packet Header Trace,source IP address,destination IP address,data sequence number,acknowledgment number,time stamp (sec.),time stamp (sub-sec.),total length,ToS,vers.,HLen,IPID,flags,TTL,protocol,header checksum,destination port,source port,window size,Hlen,fragment offset,TCP flags,urgent pointer,checksum,Timing,IP Header,TCP Header,0,16,31,9,Header Field Entropy,source IP address,destination IP address,data sequence number,acknowledgment number,time stamp (sec.),time stamp (sub-sec.),total length,ToS,vers.,HLen,IPID,flags,TTL,protocol,header checksum,destination port,source port,window size,Hlen,fragment offset,TCP flags,urgent pointer,checksum,Timing,IP Header,TCP Header,0,16,31,flow id,time,10,Single Point Packet Trace,T0,F0,T1,F1,T3,F0,Tn,Fn,Tm,F0,temporal correlation introduced by flows,packets from same flow closely spaced in time,they share header information,packet inter-arrival: # bits per packet:,T0,F0,T3,F0,Tm,F0,flow based trace:,flow record:,F0,K,T0,flow,ID,flow,size,arrival,time,packet inter-arrival,11,Network Models,flow-based model,flow arrivals follow Poisson with rate,flows are classified to independent flow classes according to routing (the set of routers traversed),flow i is described by:,flow inter-arrival time:,flow ID:,flow length:,packet inter-arrival time within the flow:,packet arrival stochastic process:,12,Entropy in Flow Record,# bits per flow:,# bits per second:,marginal compression ratio,determined by flow length (pkts.) and variability in pkt. inter-arrival.,13,Single Point Compression: Results,Trace,H (total),Model,Ratio,Compression Algorithm,C1-in,706.3772,0.2002,0.6425,BB1-out,736.1722,0.2139,0.6574,BB2-out,689.9066,0.2186,0.6657,Compression ratio lower bound calculated by entropy much lower than real compression algorithm,Real compression algorithm difference,Records IPID, packet size, TCP/UDP fields,Fixed packet buffer for each flow = many flow records for long flows,14,Distributed Network Monitoring,single flow recorded by multiple monitors,spatial correlation: traces collected at distributed monitors are correlated,marginal node view:#bits/sec to represent flows seen by one node, bound on single point compression,network system view:#bits/sec to represent flows cross the network, bound on joint compression,joint compression ratio: quantify gain of joint compression,15,“perfect” network,fixed routes/constant link delay/no packet loss,flow classes based on routes,flows arrive with rate:,# of monitors traversed:,#bits per flow record:,info. rate at node v:,network view info. rate:,joint compression ratio:,Baseline Joint Entropy Model,dependence on # of monitors travered,16,Joint Compression: Results,Set of Traces,Joint Compression Ratio,C1-in, BB1-out, C2-in, BB2-out,0.5,C1-in, BB1-out,0.8649,C1-in, BB2-out,0.8702,C2-in, BB1-out,0.7125,C2-in, BB2-out,0.6679,17,Coarser Granularity Models,NetFlow model,similar to flow model:,joint compression result similar to full trace,SNMP model,any link SNMP rate process is sum of rate processes of all flow classes passing through that link,traffic rates of flow classes are independent Gaussian,entropy can be calculated by covariance of these processes,information loss due to summation,small joint information between monitors,difficult to recover rates of flow classes from SNMP data,18,Joint Compression Ratio of Different Granularity,Set of Traces,SNMP,NetFlow,Packet Trace,C1-in, BB1-out,1.0021,0.8597,0.8649,C1-in, BB2-out,0.9997,0.8782,0.8702,19,Conclusion,information theoretic bound on marginal compression ratio - 20% (time+flow id, even lower if include other low entropy fields),marginal compression ratio high (not very compressible) in SNMP, lower in NetFlow, and the lowest in full trace,joint coding is much more useful/nessassary in full trace case than in SNMP,“More entropy for your buck”,20,Future Work,network impairments,how many more bits for delay/loss/route change,model netflow with sampling,distributed compression algorithms,lossless v.s. lossy compression,entropy based monitor placement,maximize information under constraints,21,Thanks!,22,
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