Ahmct-Knowledpc of thc up-to-dah physical topology of an IF network Is cmcisl to 8 numhcr of crltlcai network mnnagcment tasb, Including reactive and praactlvc remum management, event carrclath, and mnt-cewe nnalysls. Given lhe dynamlc nalurc of today's IP nelworh, keeping track of topolugy infarmation manunlly Ir a dauntlng (if not Impossible) tmk. Thus, effective algnrltlims for automat i d l y dlrcovcr[np physlcnl network topolngy arc necessary, Earlier work hllrl typtcally concentrated on elther (a) dlscoverlng loglcal &@, , layer-3) topology, whkh implies thot lhr: connwtlvity of 811 Inyer-2 demcnla (cog., swltclw and bridges) b Ignored, or (b) proprletary soliitlons fargctlng spcclfic product fmiilles.In thls paper, wc pment novcl algoritlims for dhcoverlng physIca1 topology in hcterngcncous (Le., miillhvcndor) IF networks. Our algorlthms rcly on slantlard SNMP MIB Information that Ls wldcly supported by modern IP network elemcnh find rqiiire no modlflcations to the operatlng ayatem sohwarc runnlng on elements or hosts. WE Iiwe Implemented the algorithms pracntcd in thio paper in the contcxt of R topology discovery tool that has hecn tpstcd an Lucent's own rescarch nctwork. The cxperlmentnl results clearly volidalc our nppronch, demonstrating tliflt our tool cmn consistently discover the accirratc physical network topology In tlme that b mnglily qiiadmtic iii the number of nctwork elements. I. INTRODUCTIONPhysical rielwork topology refers to the characterization of the physical connectivity relationships that exist among entities in a communication network. Discovering the physical layout and interconnections of network elements is a prerequisite to many critical nctwork managcmcnt tasks, including reactive nnd proactive resource management, server siting, went correlation, and root-cause analysis. For example, consider n fault monitoring and analysis application running on a ceniral IP network management platform. qpically, a single fault in the network will causc a flood of alarm signals emanating from different interrelated network elements. Knowledge of element interconnections is essential to filter aut secondary alarm signals and correlate primary alarms to pinpoint thc original source of fai1-ut% in the network [l], [2]. Furthermore, n full physical map of the network enables a proactive analysis of thc impact of link and device failures. Early identification of single points of failiirc that could disrupt a large fraction of the user community allows the network mmager i o improve the survivabiIity of the network (e.g., by ndding nlternnte routing pnthr) bcforc outages occur.at layer-2 is definitely not straightforward. 2. Thnsparency of elements across protocol luyers. The algo-ritlim should correctly establish inlerconnections between network clcments operating ai different layers of the IS0 protocol stack. This is not trivial, since layer-2 elements in switched subncts are complctely transparcot to the layer-) router@) directing traffic in and uut of the subnets. 3. Heierageneiry of nerwark eleme...

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Replication of documents on geographically distributed servers can improve both performance and reliability of the Web service. Server selection algorithms allow Web clients to select one of the replicated servers which is "close" to them and thereby minimize the response time of the Web service. Using client proxy server traces, we compare the effectiveness of several "proximity" metrics including the number of hops between the client and server, the ping round trip time and the HTTP request latency. Based on this analysis, we design two new algorithms for selection of replicated servers and compare their performance against other existing algorithms. We show that the new server selection algorithms improve the performance of other existing algorithms on the average by 55%. In addition, the new algorithms improve the performance of the existing nonreplicated Web servers on average by 69%.

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Abstract-Knowledge of the up-to-date physical (i.e., layer-2) topology of an Ethernet network is crucial to a number of critical network management tasks, including reactive and proactive resource management, event correlation, and root-cause analysis. Given the dynamic nature of today's IP networks, keeping track of topology information manually is a daunting (if not impossible) task. Thus, effective algorithms for automatically discovering physical network topology are necessary. In this paper, we propose the first complete algorithmic solution for discovering the physical topology of a large, heterogeneous Ethernet network comprising multiple subnets as well as (possibly) dumb or uncooperative network elements. Our algorithms rely on standard SNMP MIB information that is widely supported in modern IP networks and require no modifications to the operating system software running on elements or hosts. Furthermore, we formally demonstrate that our solution is complete for the given MIB data; that is, if the MIB information is sufficient to uniquely identify the network topology then our algorithm is guaranteed to recover it. To the best of our knowledge, ours is the first solution to provide such a strong completeness guarantee.