Patent Application: US-67624403-A

Abstract:
the deployment of probing technology for the purpose of problem determination in a server or network element is disclosed . one can actively select which probes to send in order to be able to diagnose problems which one is particularly interested in , allowing for greater flexibility and efficiency . the extra load imposed on a network by the use of probes is small , especially if the algorithms described in this disclosure are used .

Description:
the present invention employs a novel approach for fault localization and problem determination in networks . the approach utilizes an active probing technology , rather than traditional approaches which correlate alarm events received from multiple sources when a failure occurs . the problem is precisely formulated , a system architecture for addressing the problem in a modular way is described , and algorithms for finding small probe sets that are able to diagnose problems occurring anywhere in the network are implemented . the problem is formulated in terms of a dependency matrix methodology that captures the interactions between the paths which the probes traverse . the first step is to compute the initial dependency matrix using information about which probe - stations and probes are available to be used . the second step is to determine using the dependency matrix which problems can be diagnosed using the complete set of probes . the third step is to find smaller probe sets which are able to diagnose the same set of problems as the initial probe set . small probe sets are desirable to minimize the additional network load and data storage costs imposed by the use of probes . three preferred algorithms are provided for computing small probe sets . an exhaustive search algorithm will find the smallest possible probe set able to diagnose the full set of problems , but this is computationally expensive . a linear - time algorithm is provided which very quickly finds a small ( but not necessarily minimal size ) probe set . a quadratic - time algorithm is provided which finds a smaller ( but still not necessarily minimal size ) probe set . a system architecture is provided which facilitates the decision of which algorithm to use depending on the desired tradeoff between computational cost and probe set size . fig1 illustrates how probing technology works . it is important to note that the network model is quite general . for example , layering can be accommodated : if a web - server depends on tcp / ip running which depends on the box being up , this can be modeled as a node for the box with a link to tcp / ip from the box and a further link from tcp / ip to the web - server . thus nodes may represent applications and links dependencies between those applications . similarly , a node may represent a sub - network of many nodes whose inter - connections are unknown . in this case probing will determine that the problem lies somewhere in that sub - network , at which point some form of local system management ( perhaps including local probing ) may be used to pinpoint the problem . finding the minimal set of probes needed for fault localization requires providing answers to the following questions : ( 1 ) which probes are available as “ candidates ” for use in a network ? ( 2 ) which faults can be successfully identified by a given set of probes ? ( 3 ) what is the smallest number of probes that can identify the same collection of faults as a given set ? the present invention addresses these issues and integrates the solutions obtained into a system for performing fault localization . suppose the network has n nodes . each probe is represented as a binary string of length n , where a 1 in position j denotes that the probe passes through node n j . each probe that is sent out either returns successfully or fails to do so . if a probe is successful , then every node and link along its path must be up . conversely , if a node or link is down then any probe passing through that node or link fails to return . fig2 shows a probe going from n 1 through n 2 to n 5 . if this probe ( denoted by p 15 , indicating the starting and ending nodes ) fails , that indicates a problem with either n 1 , n 2 , or n 5 . if p 15 succeeds , that indicates that n 1 , n 2 , and n 5 are all ok , although there may be a problem with one of the other nodes that the probe does not pass through . a set of r probes defines a dependency matrix d , where d ( i , j )= 1 if probe p i passes through node n j and d ( i , j )= 0 otherwise . d is an r - by - n matrix , where r is the number of probes and n the number of nodes . fig3 shows an example of 2 probes and the resulting dependency matrix . if r probes are sent out , their results provide a “ signal ”— a binary string of length r , each digit denoting whether or not that probe returned successfully . for example , in fig3 if only n 2 is down then p 15 fails but p 16 succeeds . similarly if only n 5 is down then p 15 fails but p 16 succeeds . thus these two failures result in the same signal , because their columns in the dependency matrix are identical ; i . e . a failure in n 2 cannot be distinguished , by these 2 probes , from a failure in n 5 . any problem whose column in the dependency matrix is unique generates a unique signal and as a result can be unambiguously diagnosed . fig4 shows the 4 possible signals that might result from sending out these 2 s probes , and which problems might be the cause of each signal . a failure in node n 1 can be uniquely diagnosed , because both probes fail and no other single node failure results in the same signal ; n 1 &# 39 ; s column in the dependency matrix is unique . however , as explained above , a failure in n 2 cannot be distinguished from a failure in n 5 , and similarly a failure in n 3 cannot be distinguished from a failure in n 6 . although n 4 &# 39 ; s column is unique , a failure in n 4 cannot be distinguished from no failure anywhere in the network , because there is no probe passing through n 4 . adding an extra “ node ” whose column is all zeroes , representing no failure , avoids this technicality . any problem whose column in the dependency matrix is unique generates a unique signal and as a result can be unambiguously diagnosed . thus a dependency matrix decomposes the network into a disjoint collection of nodes , where each group consists of the nodes whose columns are identical ; i . e ., each group contains those nodes whose failure cannot be distinguished from one another by the given set of probes . this defines the diagnosable problems of a set of probes . for example , in fig4 the diagnostic problems are {{ 1 }, { 2 , 5 }, { 3 , 6 }, { 4 , nf }}, where index j represents node n j and nf is the extra “ node ” representing no failure anywhere in the network . a failure is diagnosable if and only if its group is a singleton set . fig5 shows an example where a third probe has been added to the previous 2 . this third probe , p 42 , goes from n 4 through n 3 to n 2 . note that these 3 probes are enough to diagnose a failure in any of the 6 nodes , because each column in the dependency matrix is unique . fig6 illustrates the system architecture . first the candidate probes are identified , 610 , and the dependency matrix and its diagnosable problems are determined , 620 . then a subset of the candidate probes is found which has the same diagnosable problems as the entire set . to do this first the diagnosable problems are found , 630 . various algorithms are available for computing the final probe set , depending on whether the user wants the guaranteed minimal set of probes or if a small but non - minimal set is adequate . the user selects the algorithm , 640 , and the final set of probes is then output , 650 . the dependency matrix can be stored in a database as an intermediate step , 660 , so that later computations can make use of it without having to repeat earlier computations . the architecture described above allows the set of candidate probes to be provided from whatever sources are available ; for example a human expert may specify which probes are possible . it may also be useful , however , to compute the available probes from the network structure and the location of the probe stations . this provides a specific instantiation of step 610 . from the n nodes a subset of k nodes are selected as the probe stations . potentially any node may be a probe station , but in practice only a small number are used ; they are usually chosen based on extraneous considerations , such as controlled access to the machines . a probe can be sent to any node from any probe station . thus the candidate set of probes could theoretically contain a probe for every possible route between every probe station and every node . in practice it cannot be guaranteed that a probe follows a particular path through the network , and thus routing strategies restrict the set of available probes ; for example a probe may follow the shortest ( i . e ., least - cost ) path through the network . this creates a candidate set of probes whose size r is a linear function of the number of nodes n ; this set is sufficient to diagnose any single node being down because one can simply use one probe station and send a probe to every node . fig7 describes the algorithm for computing the initial dependency matrix . for each probe - station s i and each node n j , a row of the dependency matrix corresponds to the shortest path through the network from s i to n j ; each value in this row is set to either 1 or 0 depending on whether or not the path passes through that node . fig8 gives an example of the output of this algorithm for the network shown there . there are 2 probe - stations , n 1 and n 4 , and one probe from each probe - station to each node , following shortest path routing . given a dependency matrix , the decomposition into diagnosable problems places all problems with the same column into the same group . the decomposition is incrementally computed row - by - row . the key is that adding a row ( i . e ., a probe ) always results in a more extensive decomposition , because nodes in distinct groups remain distinguishable ; an additional probe can only have the effect of distinguishing previously indistinguishable nodes . fig9 shows the algorithm for computing the diagnosable problems from a given dependency matrix . the algorithm starts with the decomposition { 1 , 2 , . . . , n }— all nodes are indistinguishable , 910 . each additional probe decomposes every group of the current decomposition , 920 , into two subgroups depending on which nodes the probe passes through , 930 , and all the nonempty subgroups are collected together , 940 . this process is repeated for each probe , 950 . thus each of the nodes remains grouped with precisely those nodes it has not yet been distinguished from . the algorithm terminates with the complete decomposition after considering each probe only once . fig1 illustrates how the diagnosable problem algorithm works for the set of 3 probes shown previously in fig5 . the disclosure now turns to a discussion of finding the minimal set of probes that has the same diagnostic power as a given set . for example , the 3 probes shown in fig5 is the smallest subset of the full set of candidate probes shown in fig8 that is able to diagnose any single node failure . clearly the minimal set of probes may not be unique , although the minimal number of probes is . three algorithms for finding the minimal probe set are now examined : an exponential time exhaustive search and two approximation algorithms — one requiring linear time and the other quadratic time . fig1 provides an exhaustive search algorithm for finding the minimal probe set . since a node can only be diagnosed if there is at least one probe passing through it , probes can be added incrementally in all feasible combinations until the minimal set is reached . the computational complexity of this algorithm increases exponentially as the number of candidate probes increases — this is clearly prohibitive unless the network is quite small . two approximation algorithms that heuristically attempt to find the minimum set , but are not guaranteed to do so , are now presented . fig1 presents quick search — starting with the initial set of r probes , consider each probe in turn and discard it if it is not needed ; i . e ., a probe is discarded if the diagnosable problems remain the same even if it is dropped from the probe set . this process terminates in a subset with the same diagnostic power as the original set , but this subset may not necessarily be minimal . the complexity of this algorithm increases linearly in the size of the original probe set . fig1 presents greedy search — starting with an empty set of probes , at each step add what looks like the “ best ” probe among the remaining probes . this process terminates in a subset with the same diagnostic power as the original set , but this subset may not necessarily be minimal . the complexity of this algorithm increases quadratically in the size of the original probe set . greedy search usually finds a smaller subset than quick search , but takes a little longer in computation time . for large networks the probe sets found by these algorithms are quite close to the true minimal probe set . it is to be understood that the present invention , in accordance with at least one presently preferred embodiment , includes an arrangement for determining the candidate probe set , an arrangement for determining which problems can be diagnosed by the candidate probe set , and an arrangement for finding small probe sets which can diagnose the same problems as the candidate probe set , all of which may be implemented on at least one general - purpose computer running suitable software programs . these may also be implemented on at least one integrated circuit or part of at least one integrated circuit . thus , it is to be understood that the invention may be implemented in hardware , software , or a combination of both . if not otherwise stated herein , it is to be assumed that all patents , patent applications , patent publications and other publications ( including web - based publications ) mentioned and cited herein are hereby fully incorporated by reference herein as if set forth in their entirety herein . although illustrative embodiments of the present invention have been described herein with reference to the accompanying drawings , it is to be understood that the invention is not limited to those precise embodiments , and that various other changes and modifications may be affected therein by one skilled in the art without departing from the scope or spirit of the invention . 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