CWC: Simple and Stateless AQM Capable of Handling High Priority Thin Flows to Prevent Bufferbloat Bufferbloat is a serious problem where a router increases its queueing delay with the result that timecritical applications do not work properly. To overcome this problem, many researchers focus on AQM (Active Queue Management) and numerous methods have been proposed for implementing AQM. For example, in CoDel, routers may delete a packet in the queue based on the queueing delay as a metric. FQCoDel extends CoDel by introducing multiple queues to support priority control for high priority thin flows, but the complexity of queue management becomes higher. This paper proposes stateless and simple AQM referred to as CWC (CoDel with CHOKe), which combines two drop schemes, CoDel and CHOKe, to provide FQCoDelu0027s performance using simpler control. Two methods called Packets CWC and Bytes CWC are proposed. In the simulation experiments, the average total throughput, sojourn time, bufferbloat occupancy, fairness index, and packet loss rate are compared. The results show that, for example, even when high priority thin UDP flows exist with many TCP flows, CWC decreases the average sojourn time by more than 50% compared with those of CoDel and FQCoDel, while it achieves a low average loss rate for thin UDP flows like FQCoDel and the same average total throughput compared with those of CoDel and FQCoDel. In addition, the above results are achieved under a TCP transient situation caused by TCP slow start.

Active and Adaptive ApplicationLevel Flow Control for Latency Sensitive RPC Applications The Remote Procedure Call (RPC) frameworks are widely deployed in industry. Applications supported by RPC frameworks are often latencysensitive which strictly require to be responded before the deadline. For meeting this requirement, RPC frameworks adopt the applicationlevel flow control mechanism. This mechanism gives an appropriate threshold determining the number of RPC requests that the server can process, thus avoids missing the deadline. However, this threshold at the applicationlevel is a fixed empirical value so that it is hard to obtain respectable performance because an endpointu0027s processing capacity can take a huge quantity of values by varying workload and different hardware configurations. While other methods based on specialized transport protocols are adaptive, they will introduce extra costs for message reporting from server to client. Furthermore, adopting specialized transport protocols will also introduce extra transplanting efforts for TCPbased applications. In this paper, we provide an active and adaptive application level flow control mechanism at the client side. We first design an algorithm to find the appropriate threshold to achieve the desired response time. Then based on this algorithm, we control the threshold to bound the response time as expected. We implement our flow control mechanism using a memcached testbed. Experiments prove that our mechanism can accurately reduce the mean and 99th percentile response time by at least 71.3% and 69.4% respectively, while keeping a relatively high QPS. Furthermore, compared to staticthreshold mechanism, our flow control mechanism is more efficient under low latency constraints.