Fair Scheduling over multiple servers with flow-dependent server rate

We address the quality of service (QoS) provisioning problem in an aggregated multi-server environment. The input packet stream traffic to the system is categorized at a macro level into a few "flow-classes" that require service differentiation; each class is further subdivided into "flows" at the micro-level. Flows can be serviced by any server: however; the service rate is a function of the class and the particular server. Given such an environment, we have a multi-criteria optimization objective: (i) provide differentiated service to flows, (ii) achieve load balancing of the servers and (iii) maximize their throughput (amount of bytes serviced). We present an on-line fluid-based approximation scheme to schedule packets. Modeling the accumulated traffic in a class as fluid we use linear programming to first determine the optimal fractions that should be directed to different servers while ensuring fairness and high server throughput. We propose a packet scheduling strategy for the multi-server framework (by extending a well-known fair round-robin algorithm for single link systems) that effectively incorporates the optimal service fractions determined in the previous step. We validate the proposed algorithm with extensive simulations. The results show that the algorithm imparts high throughput with good service differentiation. We also evaluate reordering of packet requests within flow streams by presenting relevant metrics that quantify reordering