  These days enterprise applications try to integrate online processing and
batch jobs into a common software stack for seamless monitoring and driverless
operations. Continuous integration of these systems results in choking of the
poorly performing sub-systems, when the service demand and throughput are not
synchronized. A poorly performing sub-system may become a serious performance
bottleneck for the entire system if its serviceability and the capacity is over
utilized by increased service demand from upstream systems. From all the
integrated sub-systems, queuing systems are majorly categorized as choking
elements due to their limited service length and lack of processing details.
The situation becomes more pronounced in multiprogramming environments where
the queue performance exponentially degrades with increased degree of
multiprogramming at upstream levels. This paper presents an approach to compute
the queue length and devise a distribution model such that the queue length is
dynamically adjusted depending on the sudden growth or decline of transmission
packets. The idea is to design a heat map of the memory and correlate it with
the queue length distribution. With each degree of multi-programmability, the
data processing logic is adjusted by the distribution model to arrive at an
endurance level queue for long term service under variable load conditions. It
will take away the current implementation of using delayed processing logic
and/or batch processing of data at downstream systems.
