Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Quality evaluation
Reexamination Certificate
2001-01-17
2002-09-17
Bui, Bryan (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Quality evaluation
C702S181000, C702S182000, C700S049000, C700S051000, C705S028000, C705S029000
Reexamination Certificate
active
06453255
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to any particular complex product that is susceptible to many types of desired selectable configurations. A sales method and sales system are developed to establish a guarantee of functionality for each specific configuration of the complex product.
BACKGROUND OF THE INVENTION
The co-pending application U.S. Ser. No. 9/249,171 “Estimator Program for Estimating the Availability of an Application Program That Runs in a Cluster of at Least Two Computers”, referenced above involves an estimator program to perform method steps for estimating the availability of an application program that runs on any server in a cluster of at least two servers. By “availability of an application program” is meant the probability that at any particular time instance, at least one of the servers in a cluster (server farm) will actually be servicing requests from external workstations able to use the application program.
In one embodiment, the so-called estimator program begins by receiving input parameters which include (i) multiple downtime periods for each computer in the cluster (server farm) that occur at respective frequencies due to various downtime sources, and (ii) an application failover time period for switching the running of the application program from any one computer to another operable computer. From these input parameters, the described estimator program estimates first and second annual stoppage times, then determines the availability of the application program on the cluster of computers which is derived from the sum of the first and second annual stoppage times.
Thus, as discussed, the estimator program of the previously-described application, U.S. Ser. No. 09/249,171, estimated a first annual stoppage time for the application program due solely to the concurrent stoppage of all of the computers, as a function of the ratio of a single computer virtual downtime period over the single computer virtual time between stops. Then subsequently, the estimator program was used to estimate a second annual stoppage time for the application program, due solely to switching the running application program from one computer to another computer as a function of the single virtual stoppage rate and the application failover time period. From this, the estimator program determined the availability of the application program on the cluster of computers by deriving the sum of the first and second annual stoppage times.
The estimator program method was based on the assumption that “application availability” was to be determined from four factors that were:
(i) single-server hardware reliability;
(ii) maintenance, support, and service strategies;
(iii) user application and environment;
(iv) failover or system reconnection mechanism and application recovery mechanism.
The prior estimation parameters that were described in the co-pending application, U.S. Ser. No. 08/550,603, did not take into consideration the total number of operating Server Farm clients and the normal single server workload of users involved with each single server. Further, this earlier application did not provide a recommendation or estimate regarding the Server Farm (or cluster) configuration or the number of servers required in the Server Farm (or cluster), which would meet the customers' performance and redundancy level requirements, nor did it establish an optimum farm configuration. Nevertheless, calculated availability or system reliability could eventually be used as a guarantee criterion for guarantee design and configuration offering. But, in the above-cited application, U.S. Ser. No. 08/550,603, the system configuration had been predefined and limited to a single server or a cluster of two servers.
Here below, is an example of a particular guarantee for the predefined system such as a single server or, a cluster of two servers described in U.S. Ser. No. 09/249,171 that was offered by one of the computer vendors (Unisys Corporation). The vendor company guarantees that a single server or two-server cluster systems will “experience no more than one unplanned system failure per year”. If this commitment is not met, the company will issue particular credits for each system type: $1,000 credit for a 4-processor single server system, $2,000 credit for an 8-processor single server system, $5,000 credit for a 4-processor clustered server system, $10,000 credit for a 8-processor clustered server system.
The method of the co-pending application U.S. Ser. No. 09/443,926, entitled “Method for Estimating the Availability of an Operating Server Farm”, extended the area of the original method application for Server Farms designed to serve user communities with a required particular number of customers “n”. This method involving the Server Farm size and availability calculations is based on (1) the single server parameters such as (a) the meantime to failure (MTTF), (b) the meantime to repair (MTTR), and (c) the single server application performance benchmarks, and (2) individual customer preferential requirements, involving (a) the total number of Server Farm application users and (b) a desirable redundancy level.
This estimation method (of co-pending U.S. Ser. No. 09/443,926) for “availability” uses the following definition of Server Farm availability. This definition is the probability that a Server Farm provides access to applications and data for a particular minimum number of users. As soon as the Server Farm cannot serve this particular minimum number of users, it is considered failed. When some of the users have lost connections but can reconnect to other servers and continue to work, and the majority of users do not experience any interruptions in their work, the farm is not considered failed, if it can still serve this particular minimum number of users.
A widely used approach to improve a system's availability beyond the availability of a single system is by using Server Farms with redundant servers. In this case, if one of the farm's servers fails, the “unlucky” users connected to this server will lose their connections, but they will have an opportunity to reconnect to other servers in the farm and get access to their applications and data. If all of the “unlucky” users get access to their applications and data, the farm is considered “available.” If at least one of the “unlucky” users fails to get access to his/her applications and data, it means that the Server Farm's redundancy was exhausted and the Server Farm is considered failed.
The parameters for MTTF and MTTR can be estimated, as indicated in the cited prior U.S. Ser. No. 09/249,171, as a single computer virtual time between failures and a single computer virtual downtime period, respectively, for a particular application and user environment.
Therefore, the availability estimation method of the prior application U.S. Ser. No. 09/443,926 allows one to estimate such parameters of the Server Farm as a server farm configuration (the total number of servers in the farm and the number of redundant servers in the farm), Server Farm availability, and Server Farm downtime, based on a set of input data. At the same time, however, this method does not provide any recommendations about “optimum” combinations of the Server Farm parameters that can be chosen at the Server Farm planning or design stage.
The method of the co-pending application U.S. Ser. No. 09/705,441, entitled “Method for Server Farm Configuration Optimization”, added a new configuration feature to the original method of the prior application U.S. Ser. No. 09/443,926 by involving the Server Farm size optimization based on the input data that include single server parameters similar to the prior application U.S. Ser. No. 09/443,926 and at least two new extra parameters: (i) single server cost and (ii) the downtime cost. Additionally, this method included steps of selecting an optimization parameter, selecting an optimization criterion, and using an optimization technique procedure to find the optimum value of the optimization parameter.
The method o
Eismann Leonard Eugene
Smorodinsky Lev
Bui Bryan
Kozak Alfred W.
Rode Lise A.
Starr Mark T.
Unisys Corporation
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