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`METHOD AND SYSTEM FOR DETERMINING COMPATIBILITY
`OF COMPUTER SYSTEMS
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`{0001]
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`This application claims priority from US. provisional patent application
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`no. 60/745,322 filed April 21, 2006.
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`FIELD OF THE INVENTION:
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`[0002]
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`The present invention relates to information technology infrastructures and has
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`particular utility in determining compatibility of computer systems in such infrastructures.
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`DESCRIPTION OF THE PRIOR ART
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`[0003]
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`As organizations have become more reliant on computers for performing day to day
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`activities, so to has the reliance on networks and information technology (IT) infrastructures
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`increased. It is well known that large organizations having offices and other facilities in different
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`geographical locations utilize centralized computing systems connected locally over local area
`networks (LAN) and across the geographical areas through wide-area networks 0N AN).
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`[0004]
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`As these organizations grow, the amount of data to be processed and handled by the
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`centralized computing centers also grows. As a result, the IT infrastructures used by many
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`organizations have moved away from a reliance on centralized computing power and towards
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`18 more robust and efficient distributed systems. Distributed systems are decentralized computing
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`19
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`systems that use more than one computer operating in parallel to handle large amounts of data.
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`20 Concepts surrounding distributed systems are well known in the art and a complete discussion
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`can be found in,
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`"Distributed Systems: Principles and Paradigms"; Tanenbaum Andrew S.;
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`Prentice Hall; Amsterdam, Netherlands; 2002.
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`[0005]
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`'While the benefits of a distributed approach are numerous and well understood, there
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`has arisen significant practical challenges in managing such systems for optimizing efficiency
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`and to avoid redundancies and/or under-utilized hardware. In particular, one challenge occurs
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`due to the sprawl that can occur over time as applications and servers proliferate. Decentralized
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`control and decision making around capacity, the provisioning of new applications and hardware,
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`and the perception that the cost of adding server hardware is generally inexpensive, have created
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`environments with far more processing capacity than is required by the organization.
`21562813.l
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`VMware, Inc. Exhibit 1010 Page 1
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`

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`[0006] When cost is considered on a server-by-server basis, the additional cost of having
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`underutilized servers is often not deemed to be troubling. However, when multiple servers in a
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`large computing environment are underutilized, having too many servers can become a burden.
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`4 Moreover, the additional hardware requires separate maintenance considerations, separate
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`upgrades and requires the incidental attention that should instead be optimized to be more cost
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`effective for the organization. Even considering only the cost of having redundant licenses,
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`removing even a modest number of servers from a large computing environment can save a
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`significant amount of cost on a yearly basis.
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`10007]
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`As a result, organizations have become increasingly concerned with such
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`redundancies and how they can best achieve consolidation of capacity to reduce operating costs.
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`The heterogeneous nature of distributed systems makes consolidation ever more difficult to
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`achieve.
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`[0008)
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`It is therefore an object of the following to obviate or mitigate the above-described
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`disadvantages.
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`SUTvIMARY OF THE INVENTION
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`[0009}
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`In one aspect, a method for determining compatibilities for a plurality of computer
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`systems is provided comprising generating a configuration compatibility score for each pair of
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`the plurality of systems based on configuration data obtained for each of the plurality of systems;
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`generating a workload compatibility score for each pair of the plurality of systems based on
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`20 workload data obtained for each of the plurality of systems; and generating a co-habitation score
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`for each pair of the plurality of systems using the respective configuration compatibility score
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`and workload compatibility score, the co-habitation score indicating an overall compatibility for
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`each system with respect to the others of the plurality of systems.
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`[0010]
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`In another aspect, a computer program is provided for determining compatibilities for
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`a plurality of computer systems. The program comprises an audit engine for obtaining
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`information pertaining to the compatibility of the plurality of computer systems; an analysis
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`engine for generating a compatibility score for each pair of the plurality of systems based on the
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`21562813.1
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`VMware, Inc. Exhibit 1010 Page 2
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`information that is specific to respective pairs; and a client for displaying the compatibility score
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`on an interface.
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`(0011]
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`In yet another aspect, a method for determining configuration compatibilities for a
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`plurality of computer systems is provided comprising obtaining configuration data for each of
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`the plurality of computer systems; assigning a weight to one or more parameter in the
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`configuration data indicating the importance of the parameter to the compatibility of the plurality
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`of systems; generating a rule set comprising one or more of the parameters; and computing a
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`configuration score for each pair of the plurality of systems according to the weights in the rule
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`set.
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`[0012}
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`In yet another aspect, a method for determining workload compatibilities for a
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`plurality of computer systems is provided comprising obtaining workload data for each of the
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`plurality of systems; computing a stacked workload value for each pair of the plurality of
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`systems at one or more time instance according to the workload data; and computing a workload
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`score for each pair of the plurality of systems using the stacked workload values.
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`(0013]
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`In yet another aspect, a graphical interface for displaying compatibility scores for a
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`plurality of computer systems is provided comprising a matrix of cells, each the cell
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`corresponding to a pair of the plurality of computer systems, each row of the matrix indicating
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`one of the plurality of computer systems and each column of the matrix indicating one of the
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`plurality of computer systems, each cell displaying a compatibility score indicating the
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`compatibility of the respective pair of the plurality of systems indicated in the corresponding row
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`and column, and computed according to predefined criteria.
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`22 BRIEF DESCRIPTION OF THE DR.A WINGS
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`[00141
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`An embodiment of the invention will now be described by way of example only with
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`reference to the appended drawings wherein:
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`{0015]
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`Figure la is a schematic representation of a system for evaluating computer systems.
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`21562813.1
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`

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`[0016]
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`Figure 1 b is schematic representation of a network of systems analyzed by a
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`compatibility analysis program.
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`[0017]
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`Figure 2 is a schematic block diagram of an underlying architecture for implementing
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`the analysis program of Figure lb.
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`[0018)
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`Figure 3 is flow diagram illustrating a system consolidation analysis.
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`(0019]
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`Figure 4 is a flow diagram illustrating strategies for a server consolidation analysis.
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`[0020]
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`Figure 5 is a table illustrating data enablement for system consolidation and
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`virtualization.
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`[0021]
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`Figure 6 is a system compatibility index (SCI) matrix.
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`[0022)
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`Figure 7 is a workload compatibility index (WCI) matrix.
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`[0023]
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`Figure 8 is a co-habitation index (CHI) matrix for the SCI and WCI matrices of
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`Figures 6 and 7.
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`[0024)
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`Figure 9 is a graphical display showing server consolidation information.
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`[0025]
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`Figure IO is an audit request template.
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`[00261
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`Figure 11 is a detailed configuration report containing audited configuration data.
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`[0027]
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`Figure 12 is a table containing workload data.
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`[0028]
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`Figure 13 is a table containing a rule set used in generating an SCI matrix.
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`[0029}
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`Figure 14 is a screenshot of a program for generating compatibility reports.
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`(0030)
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`Figure 15 is an SCI matrix for an example environment having four server systems.
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`[0031}
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`Figure 16 is a table containing a summary of differences between a pair of systems in
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`the environment.
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`21562813.1
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`(0032}
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`Figure 17 is a table containing details of the differences listed in Figure I 6.
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`[0033]
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`Figure 18 is a WCl matrix for the environment.
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`[0034]
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`Figure 19 is a workload compatibility report for a pair of systems being analyzed.
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`[0035]
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`Figure 20 is a CHI matrix for the environment.
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`[0036)
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`Figure 21 is a flowchart illustrating a system compatibility analysis procedure.
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`[0037]
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`Figure 22 is a flowchart illustrating a configuration data extraction procedure.
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`[0038]
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`Figure 23 is a flowchart illustrating a configuration compatibility analysis procedure.
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`[0039]
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`Figure 24 is a flowchart illustrating a rnle set application procedure.
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`(0040}
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`Figure 25 is a flowchart illustrating a workload data extraction procedure.
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`10
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`[00411
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`Figure 26 is a flowchart illustrating a workload compatibility analysis procedure.
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`[0042}
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`Figure 27 is a flowchart illustrating a workload multi-stacking procedure.
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`[0043]
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`Figure 28 is a flowchart illustrating another workload multi-stacking procedure using
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`a hypothetical target system.
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`Figure 29 is a is a hierarchical block diagram illustrating metadata, rules and rule
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`[0044]
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`sets.
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`[0045]
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`Figure 30 is a schematic flow diagram showing the application of a rule set in
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`analyzing a pair of computer systems.
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`[0046]
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`Figure 31 illustrates a general mle definition.
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`[0047]
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`Figure 32 shows an example rule set.
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`21562813.1
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`

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`DETAILED DESCRIPTION OF THE INVENTION
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`[0048)
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`Referring therefore to Figure 1 a, an analysis program 10 is in communication with a
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`set of computer systems 28 (3 are shown in Figure la as an example). The analysis program 10,
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`using a computer station I 6, evaluates the computer systems 28 and provides a report showing
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`how the systems differ. The computer systems 28 may be physical systems as well as virtual
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`systems or models. A distinct data set is preferably obtained for each system 28.
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`(0049]
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`Each data set comprises one or more parameter that relates to characteristics or
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`features of the respective system 28. The parameters can be evaluated by scrutinizing program
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`definitions, properties, objects, instances and any other representation or manifestation of a
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`component, feature or characteristic of the system 28. In general, a parameter is anything related
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`to the system 28 that can be evaluated, quantified, measured, compared etc.
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`12 Exemplary Environment
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`[0050]
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`Referring to Figures lb and 2, a compatibility analysis program, generally referred to
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`by numeral l 0 for clarity is deployed to gather data from the exemplary architecture shown for a
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`computing environment 12 (shown in Figure lb). The analysis program JO analyzes the
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`environment 12 to determine whether or not compatibilities exist within the environment 12 for
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`consolidating systems such as servers, desktop computers, routers, storage devices etc. The
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`analysis program l O is preferably part of a client-server application that is accessible via a web
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`browser client 34 running on, e.g. a computer station 16. The analysis program 10 operates in
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`the environment 12 to collect, analyze and report on audited data for not only consolidation but
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`other functions such as inventory analysis, change and compliance analysis etc. In the following
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`examples, the systems are exemplified as servers.
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`[0051]
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`As shown in Figure 1 b, the example environment 12 generally comprises a master
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`server 14 that controls the operations of a series of slave servers 28 arranged in a distributed
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`system. In the example shown, the master server 14 audits a local network 18 having a series of
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`servers 28 some having local agents and others being agentless. The master server also audits a
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`pair of remote networks 20, 22 having firewalls 24. The remote network 20 includes a proxy for
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`avoiding the need to open a port range. The remote network 22 comprises a collector 30 for
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`concentrating traffic through a single point allowing an audit to be performed through the
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`firewall 24, and also comprises a proxy 32. The proxy 32 is used to convert between
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`4 Windows TM protocols and UNIXTM/LinuxTM servers, and can also concentrate traffic. The
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`proxy 32 may be required for auditing agentless Windows TM based server if the master server 14
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`is running another operating system such as UNIXTM or LinuxTM_
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`[0052]
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`The master server 14 is capable of connecting to the slave servers 28 for performing
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`audits of configuration settings, workload etc. and thus can communicate over several applicable
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`protocols, e.g. simple network management protocol (SNivIP). As shown, a computer station 16
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`running a web browser and connected to a web server (not shown) on the master server 14, e.g.
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`over HTTP, can be used to operate the analysis program 10 in the environment 12. The analysis
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`program 10 may reside on the master server 14 or may run on a remote server (not shown). The
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`analysis program 10 can gather data as it is available or retrieve a block of data from the master
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`server I 4 either via electronic means or other physical means. As such, the analysis program 10
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`can operate in the environment 12 or independently (and remote thereto) so long as it can obtain
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`audited data from the environment 12. The computer station 16 enables the analysis program 10
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`to display reports and gather user input for executing an audit or analysis.
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`18 Analysis Program
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`[0053)
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`A example block diagram of the analysis program 10 is shown in Figure 2. The flow
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`of data through the program 10 begins as an audit engine 46 pulls audit data from audited
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`environments 50. The data works its way up to the web client 34 which displays an output on a
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`user interface,
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`on computer system 16. The program 10 is preferably a client-server
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`application that is accessed via the web client or interface.
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`[0054)
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`An audit engine 46 communicates over one or more connections referred to generally
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`by numeral 48 with audited environments 50 which are the actual systems 28,
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`server
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`26 machines, that are being analysed. The audit engine 46 typically uses data acquisition (DAQ)
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`28
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`adapters to communicate with the end points ( e.g. servers 28) or software systems that manage
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`the end points (e.g. management frameworks 52 and/or agent instrumentation 54). The program
`21562813.1
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`10 can utilize management framework adapters 52 in the audited environments 50 for
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`communicating with ESM frameworks and agent instrumentation and for communicating with
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`other agents such as a third party or agents belonging to the program 10. The audit engine 46
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`can also communicate directly with candidate and/or target systems 28 using agentless adapters
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`( central arrow in Figure 2) to gather the necessary audit information.
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`(0055]
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`An auditing data repository 42 is used to store audit information and previous reports.
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`The audit engine 46, using a set of audit templates 45, controls the acquisition of data that is used
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`by the other software modules to eventually generate a set of reports to display on the interface
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`34. A contex1: engine 40 utilizes metadata 39 stored by the program 10, which indicates the
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`nature of the data, to filter out extraneous information.
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`[0056]
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`An analysis engine 4 I evaluates data compared in a differential engine 38 based on a
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`set of rules 43. The analysis engine 41 performs the compatibility and, in this example, the
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`consolidation analysis to determine if the environment 12 can operate with fewer systems 28.
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`[0057]
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`The program 10 has a report generation tool 36 that utilizes a set of report templates
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`3 5 for generating custom reports for a particular environment 12. The report generation tool 3 6
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`utilizes the information generated by the analysis engine 41. Typically, the program 10 includes
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`a web client 34 for communicating with a web interface ( e.g. on computer system 16). The web
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`interface allows a user to enter settings, initiate an audit or analysis, display reports etc.
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`System Compatibility Analysis Visualization
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`[0058)
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`In the following examples, a source system refers to a system from which
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`applications and/or data are to be moved, and a target server or system is a system to which such
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`applications and/or data are to be moved. For example, an underutilized environment having two
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`systems 28 can be consolidated to a target system (one of the systems) by moving applications
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`and/or data from the source system (the other of the systems) to the target system.
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`21562Sl3.l
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`[0059]
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`As best seen in Figure 3, the systems 28 are audited in stage A to generate
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`configuration reports and workload patterns, which are in tum used to create statistical
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`scorecards in stages B, C and D.
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`[00601
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`The first stage, stage A, involves data collection, which includes the collection of
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`detailed configuration and workload information from the environment 12. Stage A also
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`includes data extraction to obtain the relevant configuration and workload data from the per(cid:173)
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`system data gathered in an audit according to compatibility rule sets and workload data types to
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`obtain filtered per-system configuration and workload data sets. In addition, per-system
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`benchmark data and usage limits are considered in Stage A for performing the workload analysis
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`(Stage C).
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`(0061]
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`Stages B and C are then performed using the data that has been collected. Stage B
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`involves performing the system compatibility analysis to generate a system compatibility index
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`(SCI) matrix 60 and Stage C involves performing the workload compatibility analysis to
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`generate a workload compatibility index (WCI) matrix 70. The results from stages Band Care
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`then used in stage D to perform an overall compatibility analysis which involves the generation
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`of a co-habitation index (CHI) matrix 80 and its visual mapping of overall system compatibility.
`The analysis results may then be used to identify the best server consolidation candidates. It will
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`be appreciated that the principles described herein support many strategies and consolidation is
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`only one example.
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`20 Objectives
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`{0062]
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`From an analysis perspective, as shown in Figure 4, a cost-savings objective can be
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`evaluated on the basis of certain strategies such as, but not limited to, database consolidation
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`strategies, application stacking strategies, operating system (OS) level stacking strategies and
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`virtualization strategies that can also be visualized graphically using a comprehensive
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`consolidation roadmap 90 incorporating the SCI 60, WCI 70 and CHI 80 matrices as will be
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`explained in greater detail below.
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`21562813.1
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`-9-
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`System Configuration Compatibility Visualization
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`[0063]
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`A configuration analysis in Stage B of N systems 18 computes NxN system
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`compatibility scores by individually considering each system 18 as a consolidation source and as
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`a target. Preferably, the scores range from O to 100 with higher scores indicating greater system
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`compatibility. The analysis will thus also consider the trivial cases where systems are
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`consolidated with themselves and would be given a maximum score,
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`100. For display and
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`reporting purposes, the scores are preferably arranged in an NxN matrix form.
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`[0064]
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`An example of an SCI matrix 60 is shown in Figure 6. The SCI matrix 60 provides
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`an organized graphical mapping of system compatibility for each source/target system pair on
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`the basis of configuration data. The SCI matrix 60 shown in Figure 6 is structured having each
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`server 28 in the environment 12 listed both down the leftmost column 64 and along the
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`uppermost row 62. Each row represents a consolidation source system, and each column
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`represents the possible consolidation target. Each cell contains the score corresponding to the
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`case where the row system is consolidated onto the column (target) system.
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`[0065)
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`The preferred output shown in Figure 6 arranges the servers 28 in the matrix such that
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`a 100% compatibility exists along the diagonal 63 where each server is naturally 100%
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`compatible with itself The SCI matrix 60 is preferably displayed such that each cell 66 includes
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`a numerical score and a shade of a certain colour. As noted above, the higher the score (from
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`zero (0) to one hundred (100)), the higher the compatibility. The scores are pre-classified into
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`predefined ranges that indicate the level of compatibility between two systems I 8. Each range
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`maps to a corresponding colour or shade for display in the matrix 60. For example, the
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`following ranges and colour codes can be used: score= l 00, 100% compatible, dark green; score
`= 75-99, highly compatible, green; score= 50-74, somewhat compatible, yellow; score 25-49,
`low compatibility, orange; and score= 0-24, incompatible, red.
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`[0066]
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`The above ranges are only one example. Preferably, the ranges can be adjusted to
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`reflect more conservative and less conservative views on the compatibility results. The ranges
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`can be adjusted using a graphical tool similar to a contrast slider used in graphics programs.
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`21562813,l
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`1 Adjustment of the slider would correspondingly adjust the ranges and in turn the colours. This
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`allows the results to be tailored to a specific situation.
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`[0067]
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`It is therefore seen that the graphical output of the SCI matrix 60 provides an intuitive
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`4 mapping between the source/target pairs in the environment 12 to assist in visualizing where
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`5
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`compatibilities exist and do not exist. In Figure 6, it can be seen that the server pair identified
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`6 with an asterisk (*) and by the encircled cell indicates complete compatibility between the two
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`servers for the particular strategy being observed, e.g. based on a chosen rule set. It can also be
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`seen that the server pair identified with an X and the encircled cell at the corresponding
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`row/column crossing comprises a particularly poor score and thus for the strategy being
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`observed, the servers 28 in that pair are not very compatible.
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`[0068]
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`The scores are calculated based on configuration data that is acquired through a
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`configuration audit performed by the analysis program 10. The data is acquired using tools such
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`as the table 100 shown in Figure 5 that illustrate the various types of configuration settings that
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`are of interest and from which sources they can be obtained. Figure 5 also provided a mapping
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`to where the sample workload data can be obtained. In Figure 5, a number of strategies 104 and
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`sub-strategies l 05 map to various configuration and workload sources, collectively referred to by
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`numeral l 02. As discussed making reference to Figure 4, the strategies I 04 may relate to
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`database consolidation, OS-level stacking, application server stacking and virtualization. Each
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`strategy 104 includes a set of sub-strategies 105, which in tum map to specific rule sets 43. The
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`rule sets, which will be explained in greater detail below, determine whether or not a particular
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`setting or system criterionf criteria have been met and thus how different one server 28 is to the
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`next.
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`[0069]
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`The table l 00 lists the supported consolidation strategies and the relevant data sources
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`that should be audited to perform the corresponding consolidation analysis. In general,
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`collecting more basis data improves the analysis results. The table 100 enables the analysis
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`program 10 to locate the settings and information of interest based on the strategy I 04 or sub-
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`strategy 105 (and in tum the rule set) that is to be used to evaluate the systems 28 in the
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`environment 12. The results can be used to determine source/target candidates for analysing the
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`environment for the purpose of, e.g. consolidation, compliance measures etc.
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`System Workload Compatibility Visualization
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`[0070]
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`An example WCI matrix 70 is shown in Figure 7. The WCI matrix 70 is the analog
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`of the SCI matrix 60 for workload analysis. The WCI matrix 70 includes a similar graphical
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`display that indicates a score and a colour or shading for each cell to provide an intuitive
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`7 mapping between candidate source/target server pairs. The workload data is obtained using tools
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`such as the table l 00 shown in Figure 5 and corresponds to a particular workload factor, e.g.
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`CPU utilization, network I/0, disk 1/0, etc. A high workload score indicates that the candidate
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`server pair being considered has a high compatibility for accommodating the workload on the
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`target system. The specific algorithms used in determining the score are discussed in greater
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`detail below. The servers are listed in the upper row 72 and leftmost column 74 and each cell 76
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`represents the compatibility of its corresponding server pair in the matrix. The encircled cell
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`identified by the asterisk (*) in Figure 7 indicates a high workload compatibility for
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`consolidating to the target server, and the one marked by the X indicates an unlikely candidate
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`pair for workload consolidation, compliance etc.
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`System Co-Habitation Compatibility Visualization
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`[0071]
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`An example CHI matrix 80 is shown in Figure 8. The CHI matrix 80 comprises a
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`similar arrangement as the SCI and WCI matrices 60, 70, which lists the servers in the uppermost
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`row 82 and leftmost column 84 to provide 100% compatibility along the diagonal. Preferably
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`the same scoring and shading convention is used as shown in Figure 8. The CHI matrix 80
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`provides a visual display of scoring for candidate system pairs that considers both the
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`configuration compatibility from the SCI matrix 60 and the workload compatibility from the
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`24 WCI matrix 70.
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`(0072]
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`The score provided in each cell 86 indicates the co-habitation compatibility for
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`consolidating servers. It should be noted that in some cases two servers 28 can have a high
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`configuration compatibility but a low workload compatibility and thus end up with a reduced or
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`VMware, Inc. Exhibit 1010 Page 12
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`l
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`relatively low co-habitation score. It is therefore seen that the C:HI 80 provides a comprehensive
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`score that considers not only the compatibility of systems 28 at the setting level but also in its
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`utilization. By displaying the SCI matrix 60, WCI matrix 70 and CHI matrix 80 in the
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`consolidation roadmap 90, a complete picture of the entire system can be ascertained in an
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`organized manner. The matrices 60, 70 and 80 provide a visual representation of the
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`compatibilities and provide an intuitive way to evaluate the likelihood that systems can be
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`consolidated and have associated tools (as explained below) that can be used to analyse
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`compliance and remediation measures to modify systems 28 so that they can become more
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`compatible with other systems 28 in the environment 12. It can therefore be seen that a
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`significant amount of quantitative data can be analysed in a convenient manner using the
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`graphical matrices 60, 70, 80 and associated reports and graphs (described below).
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`[0073]
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`For example, a server pair that is not compatible only for the reason that certain
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`critical software upgrades have not been implemented, the information can be uncovered through
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`analysis tools used with the SCI matrix 60, and then investigated, so that upgrades can be
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`implemented, referred to herein as remediation. Remediation can be determined by modeling
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`cost ofirnplementing upgrades, fixes etc that are needed in the rule sets. Ifremediation is then
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`implemented, a subsequent analysis may then show the same server pair to be highly compatible
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`and thus suitable candidates for consolidation.
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`Sorting Examples
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`[0074]
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`The matrices 60, 70 and 80 can be sorted in various ways to convey different
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`information. For example, sorting algorithms such as a simple row sort, a simple column sort
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`and a sorting by group can be used.
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`(0075]
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`A simple row sort involves computing the total scores for each source system (by
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`row), and subsequently sorting the rows by ascending total scores. In this arrangement, the
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`highest total scores are indicative of source systems that are the best candidates to consolidate
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`onto other systems.
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`21562813.l
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`VMware, Inc. Exhibit 1010 Page 13
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`[0076}
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`A simple column sort involves computing the total scores for each target system (by
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`column) and subsequently sorting the columns by ascending total score. In this arrangement, the
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`highest total scores are indicative of the best consolidation target systems.
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`[0077)
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`Sorting by group involves computing the difference between each system pair, and
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`arranging the systems to minimize the total difference between each pair of adjacent systems in
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`the matrix. The difference between a system pair can be computed by taking the square root of
`the sum of the squares of the difference of a pair's individual compatibility score against each
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`other system in the analysis. In general, the smaller the total difference between two systems,
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`the more similar the two systems with respect to their compatibility with the other systems. The
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`group sort promotes the visualization of the logical breakdown of an environment by producing
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`clusters of compatible systems 18 around the matrix diagonal. These clusters are indicative of
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`compatible regions in the environment 12.
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`Consolidation Roadmap
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`[0078)
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`The consolidation roadmap 90 shown in Figure 9 illustrates how the matrices 60, 70
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`and 80 can be used to provide a complete visualization. As shown in Figure 9, configuration
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`deviation examples 92 can be generated based on the SCI matrix 60 and displayed in a chart or
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`table to show where compatibilities are lacking. Similarly, work.load stacking examples 94 can
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`be generated based on the WCI 70 to show how a candidate server pair would operate when the
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`respective workloads are stacked. Ultimately, a current server utilization versus capacity
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`comparison 96 can also be illustrated to show combined server capacities and other information
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`pulled from the CHI 80.
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`System Compatibility Analysis Overview
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`[0079]
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`The following illustrates an example for generating the matrices 60, 70 and 80
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`discussed above. The analysis program 10 generally executes four primary stages as shown in
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`Figures 3 and 21.
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`21562813.1
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`VMware, Inc. Exhibit 1010 Page 14
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`System Configuration Compatibility
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`[0080]
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`In stage A, a configuration data e;;.iraction st