Abstract:
In one embodiment there is disclosed a method for tracking usage of system components such that for each system component to be tracked the value of that component is measured on a successive time unit basis and the measured value is stored together with the number of successive time units that value repeats. In another embodiment a system for handling data representative of system conditions is disclosed in which rapidly changing data values are received from at least one of the monitored sources such that each data value is representative of system conditions with respect to a small period of time; and such that the data is compressed while preserving the data values for each of the small periods of time.

Description:
TECHNICAL FIELD  
       [0001]     This invention relates to storage of data and more particularly to systems and methods for organizing and storing data.  
       BACKGROUND OF THE INVENTION  
       [0002]     There are a variety of methods available for managing data, particularly computer system performance data. These methods typically collect and store performance data, and produce a variety of reports based on that data. Such performance data tracks, for example, the amount of resources available on a system; the number of CPUs used at a particular time; the amount of physical memory available at a particular time, etc. In addition, such methods collect data on how such resources are utilized. For example, CPU utilization (the percent of time during the interval during which each CPU was busy and idle) is monitored as is the run queue length (average number of processes waiting in line to use the CPU), memory utilization (the percent of real memory in use), and the number of CPUs in a work group. The above lists just a few of the parameters that need to be monitored, stored, and analyzed.  
         [0003]     When a computer system is being troubleshot (a real-time operation), or when a system is being viewed in real-time, data is typically collected every 5 to 15 seconds and displayed for the user. Data this precise is often needed to diagnose a performance problem. However, when archiving data for future use, it is not practical to store samples for every 15 second period for each collected data parameter, especially when the data is typically archived for 6 months or longer. Thus, in order to store the data in a reasonable amount of storage space, management systems typically use sampling techniques where the metric is measured once in the sampling interval and stored. The assumption being that the data being sampled does not change significantly during the sampling interval, and thus, the value at the time of the measurement is deemed to be representative of the entire sampling interval. For fast changing systems, such as computer systems, such a method is ineffective.  
         [0004]     Another solution is to average the data. Thus, if the measurement system collects 20 samples during the interval, the values of those 20 samples are averaged when archiving, allowing the management system to store only one data point for the interval. Averaging does not work for interactive systems where users submit queries and wait for a response which is usually obtained in a matter of seconds. The demand on such workloads varies from one minute to the next. Thus, during a five minute interval, the computer system may be idle much of the time, and completely saturated for a small amount of time. Performance may be unacceptably slow during the brief periods of overload. This overload may not show up when averaged with long idle periods occurring in the same sampling interval. In this situation, a five minute average is not a good representation of actual system operation.  
         [0005]     Another major drawback to averaging type systems stems from a more recent change in the nature of computing systems where vendors are introducing various forms of virtual partitions or virtual machines. These systems are dynamic, allowing the system to add or remove resources very quickly. Thus, in any system where performance data is stored for subsequent use it is important to be able to drill down to small increments of time to determine resource usage.  
         [0006]     For example, assume a virtual machine that&#39;s idle for four minutes, and has only one CPU allocated to it during those four minutes. If that virtual machine becomes very busy for the final minute of a five minute measurement interval, and an additional five CPUs are added to handle the load, what should a management system report for the number of CPUs in the server during the five minute interval? The tool that uses sampling will report either a “1”, or a “6”. The system that stores the average value will report that the server had an average of 2 CPUs. None of these values are particularly useful for understanding system operation during that five minute interval.  
       SUMMARY OF THE INVENTION  
       [0007]     In one embodiment there is disclosed a method for tracking usage of system components such that for each system component to be tracked the value of that component is measured on a successive time unit basis and the measured value is stored together with the number of successive time units that value repeats.  
         [0008]     In another embodiment a system for handling data representative of system conditions is disclosed in which rapidly changing data values are received from at least one of the monitored sources such that each data value is representative of system conditions with respect to a small period of time; and such that the data is compressed while preserving the data values for each of the small periods of time. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0009]     For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which:  
         [0010]      FIG. 1A  is a chart showing examples of work group usage over a two minute period;  
         [0011]      FIG. 1B  shows an example of one embodiment of data compression based on the chart of  FIG. 1A ;  
         [0012]      FIG. 2A  is a chart showing the uncompressed time-series data for a four minute period;  
         [0013]      FIG. 2B  is a chart showing one embodiment of compressed time-series data for the example of  FIG. 2A , where the data has been quantized;  
         [0014]      FIG. 3  is a chart showing one embodiment of bins for quantizing data;  
         [0015]      FIGS. 4A, 4B ,  5 , and  6  show embodiments of system and method operation; and  
         [0016]      FIG. 7  shows one embodiment of system utilizing the concepts discusses herein.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0017]     In general, system parameters to be measured can be grouped into parameters measurable in discrete quantities and parameters that vary widely from instant to instant (non-discrete). CPU allocation is an example of a parameter that can be measured discretely because for any given period of time the number of CPU being allocated can be discretely counted. However, the usage (in % of total CPU capacity) could vary widely during any sampling interval and these usage measurements are examples of non-discrete parameters.  
         [0018]     With respect to discretely measurable components the measurement interval can be variable and predetermined for any given section. For CPU allocation the time unit could be, for example, 15 seconds. This time unit matches the time unit used by some systems employing the CPU to reassign CPUs to other work groups. In such a system, a determination is made every 15 seconds as to how many CPUs are required for each work group. Thus, in a particular minute (and assuming 3 work groups and 8 CPUs), the first 15 seconds could be as shown in  FIG. 1A  where work group (WG)  1  has assigned to it 3 CPUs, WG  2  has 4 CPUs, and WG  3  has 1 CPU.  
         [0019]     Continuing in  FIG. 1A , the allocation of CPUs remains the same for the second 15 second period. Then in the third 15 second period WG  1  requires 6 CPUs, WG  2  uses 2, and WG  3  uses 1. For the remaining 15 second period (period  4 ) WGs  1  and  3  remain the same and WG  2  goes to 1. For the second minute (time periods  5 - 8 ) everything remains the same, except that in time period  8 , WG  1  falls to 2 CPUs.  
         [0020]     Using the concepts discussed herein, and as shown in  FIG. 1B , the run length encoding for WG  1  is 3, 2; 6, 5; 2, 2. This translates to: 3 CPUs for 2 consecutive time periods; 6 CPUs for 5 consecutive time periods, and 2 CPUs for 2 consecutive time periods. For WG  2  it would be 4, 2; 1, 6 and for WG  3  it would be 1, 8.  
         [0021]     In operation, the raw measurement data is maintained for long periods of time before run-length encoding occurs so as to allow for compacting the data for long term storage. Typically, the length of time the raw data is stored would be from two hours to two days before compression. Most commonly, the raw data would be stored for one day before compression.  
         [0022]      FIG. 2A  is a chart of uncompressed time-series data for data that must be quantized (non-discrete data) where starting at an arbitrary time of 12:00:00, CPU percent utilization is shown for 15 second intervals. Also shown for each CPU utilization value is a bin number, having within the bin the maximum utilization percentage shown. The bin number of  FIG. 2A  follows the bin example shown in  FIG. 3 .  
         [0023]     The data parameters in  FIG. 2A  are quickly varying unlike the “smooth” or integer data discussed with respect to  FIG. 1A . Thus, for an hour&#39;s worth of data, if each sample represents the value of the system performance metric for a 15 second interval, there would be 240 samples.  
         [0024]     Accordingly, if quickly varying data (i.e., % of CPU usage in this example) were to be stored in the manner shown for the integer data of  FIG. 1A , there would be no storage space savings. However, by establishing bins (for example 20% ranges) a CPU running at 15% capacity would be given a 1 and a CPU running at 3% would also be given a 1. The purpose of this step is to take “noisy” data and put it into large enough “bins” that minor variations in the individual values are smoothed. Using this arrangement, compression will not significantly change the values that are preserved. Thus, as shown in  FIG. 2B , and using the bin values shown in  FIG. 3 , the run-length encoding would be 0, 8; 1, 9; 0, 3 for a time period beginning at 12:00 and running for four minutes, as shown in  FIG. 2A .  
         [0025]     Note that in any time period (15 second interval) the CPU&#39;s percentage of use can go up or down wildly, but the average of CPU usage during that period is a single value, namely the “bin” number as shown in  FIG. 3 . Various averaging rules could be established for certain percentages (i.e. the bin sizes can be non-uniform), since a CPU near 100% has consequences different than does a CPU hovering around, say 50%. However, since the time period is relatively short and can be adjusted as desired, this averaging does not affect subsequent usage of the stored data. The selection of bin sizes is important. The sizes of the bins should be large to compress the data well but small enough that important changes are not lost. Thus, for most CPU utilization, bin sizes would typically be between 10% and 20%.  
         [0026]      FIG. 4A  shows one embodiment  40  of a flow diagram for controlling the operation of the storage of data. Process  401  determines if the data is smooth data (integer data) that can be counted directly for each given time period. If the data is smooth data, then a process, such as process  60  (to be discussed with respect to  FIG. 6 ), is followed to compress the data.  
         [0027]     If the data is not smooth data, then process  50  (to be discussed with respect to  FIG. 5 ) quantizes the data. Once such quantizing occurs, or if quantizing is not necessary, a process, such as process  60 , is followed to compress the data.  
         [0028]      FIG. 4B  illustrates one process  41  for analyzing the data. Process  410  determines if it is time to analyze the data. If it is, process  411  obtains the data and process  412  performs the analysis.  
         [0029]      FIG. 5  shows one embodiment  50  of a process for quantizing data. The inputs would be metric names or another identifier and a series of values. These could be the values, for example, that are shown in  FIG. 2A  under the heading CPU capacity. Note that while CPU&#39;s are discussed herein, any data stream can be handled in the manner discussed so as to preserve the integrity of the data over periods of time without unduly mathematically changing the value of the data.  
         [0030]     Process  502  selects the quantization table Qt [ ] ( FIG. 3 ) for the given metric. This table sets the bin ranges. Process  503  sets the bin_id to zero. Process  504  obtains the next value from the data stream and process  505  determines if the new value is less than or equal to Qt[bin-id] maximum value. ( FIG. 3  shows one example of bin maximum values.)  
         [0031]     If the value is less than or equal to the bin-id max, then the bin-id identity is outputted (saved) by process  507  and more data is obtained by process  508 . Processes  503 - 508  are continued until all data is given a bin-id.  
         [0032]     When process  505  determines that a value greater than the current bin-id. maximum has arrived, then process  506  increments the bin-id and this new value is iterated with respect to process  505 .  
         [0033]      FIG. 6  shows one embodiment  60  of a process for compressing data in accordance with the concepts discussed herein. The inputs would be the metric name (or other identifier), a time stamp of when the metric was generated and a sequence of samples measured over time, each sample representing the value of a performance metric at each point in time. These performance metrics would be, for example, CPU percentage utilization, or number of CPU&#39;s in a work group, etc. The data values input to this routine can be either integer values or quantized values (process  50 ).  
         [0034]     Process  601  stores the metric name and time stamp of an initial data value. Process  602  uses the input sequence to identify a NewValue and process  603  determines if the NewValue being presented is a FirstValue in a time sequence of values. If it is, then process  606  sets the CurrentValue to the NewValue and records a “1” for the NumberOfOccurrences. This means that this particular “new” value has appeared once.  
         [0035]     Process  609  then obtains another NewValue working in conjunction with process  602 . Process  603  then again determines if the NewValue is the beginning of a time sequence. This usually would not be determined from the actual data value but rather by a block of data corresponding to a period of time to be compressed.  
         [0036]     If, in process  603 , the NewValue is not a FirstValue then process  604  determines if the NewValue equals the CurrentValue. If it does then the NewValue must be a repeat of the CurrentValue and process  607  increments the NumberOfOccurrences. Processes  609 ,  602 ,  603 ,  604 , and  607  then repeat continuously until such time as process  604  determines that a NewValue is different from the CurentValue. When that occurs process  605  stores the CurrentValue together with the NumberOfOccurrences of that value.  
         [0037]     Process  608  then resets the CurrentValue to be the NewValue and again processes  609 ,  602 ,  603 ,  604  and  605  repeat until such time as process  609  stops asking for more data. This is occasioned by the input stream ending from the current block of data.  
         [0038]     When process  609  determines that no more data is to be gathered for this sequence then process  610  stores the CurrentValue of the data along with the NumberOfOccurrences. Process  60  then takes the input data and stores it as a run length encoded string in the form discussed with respect to  FIGS. 1A, 1B ,  2 A and  2 B.  
         [0039]      FIG. 7  shows one embodiment  70  of a system in which computer  71  is shown with multiple CPU&#39;s  72 , storage  73 , multiple applications  75 , and multiple work groups  74 . One of the applications, for example, can control processes  40 ,  50  and  60  to gather, store (for example, in storage  73 ), and analyze the data being gathered. This will allow a user to observe, over a past interval, data at a finer granularity than could be possible using simple data averaging. Statistical analysis, controlled locally by an application or remotely by data transfer or otherwise, can then be achieved for capacity planning or for other purposes.  
         [0040]     An important measurement in computer system analysis is determining how long a metric exceeded a threshold value. For example, how long was CPU utilization greater than 90%. This can be determined much more efficiently when the data is compressed using the concepts discussed herein. The bin-id that represents values larger than the threshold value is selected from the table. Then the compressed data is scanned for data pairs (a data pair is bin-id and number of occurrences) whose bin id matches that of the threshold. The time that the value was above the threshold is computed by taking the number of occurrences and multiplying by the interval. The data need not be uncompressed to make this calculation thereby making this arrangement much more efficient than other compression mechanisms.  
         [0041]     Another important measurement in computer systems analysis is determining how often a metric exceeded a threshold value for longer than a selected duration. For example, “how often did CPU utilization exceed 90% for longer than five minutes?”.  
         [0042]     The analysis described above illustrates how to locate periods where the metric was above a threshold value and to determine how long it was above that value. Given a set of such data, it is straight forward to count the number of such occurrences which exceeded a time duration.  
         [0043]     Another important tool in analyzing computer system performance is generating a histogram for a selected metric. For example, for the last six months, generate a histogram that shows what percent of time a computer system&#39;s CPU utilization was between zero and ten percent; what percent of the time it was between ten and twenty percent, and so forth.  
         [0044]     Given data that is compressed according to the concepts discussed, a histogram can be generated by taking each data pair (bin-id and number of occurrences), and adding the number of occurrences into the appropriate bin in the histogram. This analysis can be performed without uncompressing the data. Also, in the special case of a histogram, consisting of only 2 bins the question can be answered as to what percent of the time the CPU utilization was greater than 90%. This can be computed in the manner discussed above.