Server-side resource configuration and allocation for QoS guarantee is a challenge in performance-critical Internet applications. To overcome the difficulties caused by the highvariability and burstiness of Internet traffic, I applied a decay function model [1] of request scheduling algorithms for the resource configuration and allocation problem [2]. Under the decay function model, request scheduling is modelled as a transferfunction based filter system that has an input process of requests and an output process of server load. Unlike conventional queueing network models that rely on mean-value analysis for input renewal or Markovian processes, this decay function model works for general time-series based or measurement based processes and hence facilitates the study of statistical correlations between the request traffic, server load, and QoS of requests.
In [1], a time-invariant scheduler was proposed to minimize the impact of input high-order moments in support of requests with the same delay constraints. I extended the model to support requests with different deadlines and derived a close-form expression for the time-invariant resource allocation function. Further, I describes an optimal time-variant scheduling policy that minimizes load variances and capacity requirement. The scheduler assumes no knowledge about the input request distribution.
The relationship between the server capacity, scheduling policy, service deadline was also extended to get tightened capacity bound by utilizing the information of request arrival distribution. The relation facilitates the extension of the scheduler for statistical deadline miss guarantee. The deadline misses can be bounded by incorporating an admission control mechanism or approximated by blocking requests missing their deadlines in a waiting queue.
Simulation results validate the extended decay function model and show the superiority of the scheduler in comparison with other scheduling algorithms.
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