Methodology

System model: We model a single video server as a group of virtual servers with First Come First Served (FCFS) queues. The virtual server number corresponds to the physical server's capacity, which is defined as the maximum number of concurrent streams delivered by the server without loosing a quality of stream. In the analysis, the number $300$ is chosed for the capacity of a video server based on the emperical results in [3]. In this way, we model the video service center as a queueing system with multiple FCFS servers.

Workload Scheduling Schemes: We choose two well-known scheduling schemes to study: random dispatching, which doesn't make use of any information of the servers and just sends each incoming job to one of $s$ server uniformly with probability ; Least workload Left (LWL) scheme, which tries to achieve load balancing among servers by making use of the per-server workload information and assigns the job to the server with the least workload left at the arrival instant.

Service Level Agreements: We consider two QoS metrics for SLAs: the stream quality for an accepted connection, and the waiting time of a video request in the queue before accepted for streaming. Assume enough network bandwidth, then QoS on stream quality within the data center side can be guaranteed through admission control based on server capacity. For the waiting time $W$, we consider the bound on the tail of the waiting time distribution (called $W_{tail}$), defined as $P[W>x]<y$ with $x>0$, $y<1$. For example, SLA could be that $90\%$ of the requests experience no more than $5$ seconds delays, i.e., $x=5$ and $y=90\%$.