Adaptive run-time performance optimization through scalable client request rate control (abstracts only)

Today's Internet-scale computing systems often run at a low average load with only occasional peak performance demands. Consequently, computing resources are often overdimensioned, leading to high costs. While load control techniques between clients and servers can help to better utilize a given system, these techniques can place a significant communication and computation load on servers. To improve on these issues, we contribute with scalable techniques for client-request rate control, achieved through integration of (i) a scalable distributed feedback channel to transmit control information from the server to the clients with (ii) decoupling strategies that allow to constrain and filter client requests directly at the client, illustrated in the area of first-price sealed-bid online auctions, and (iii) a PID (Proportional-Integral-Derivative) controller that adaptively controls the input parameters of those decoupling strategies to facilitate an optimal server utilization. In contrast to related work, we can hence optimize server load directly at the source through rate control of the clients. Our evaluations show that this setup supports large sets of clients before the controller becomes unstable.