Online recommender systems have proven to have ubiquitous applications in various domains. To provide accurate recommendations in real time it is imperative to constantly train and deploy models with the latest data samples. This retraining involves adjusting the model weights by incorporating newly-arrived streaming data into the model to bridge the accuracy gap. To provision resources for the retraining, typically the compute is hosted on VMs, however, due to the dynamic nature of the data arrival patterns, stateless functions would be an ideal alternative over VMs, as they can instantaneously scale on demand. However, it is non-trivial to statically configure the stateless functions because the model retraining exhibits varying resource needs during different phases of retraining. Therefore, it is crucial to dynamically configure the functions to meet the resource requirements, while bridging the accuracy gap. In this paper, we propose Sandpiper, an adaptive framework that leverages stateless functions to deliver accurate predictions at low cost for online recommender systems. The three main ideas in Sandpiper are (i) we design a data-drift monitor that automatically triggers model retraining at required time intervals to bridge the accuracy gap due to incoming data drifts; (ii) we develop an online configuration model that selects the appropriate function configurations while maintaining the model serving accuracy within the latency and cost budget; and (iii) we propose a dynamic synchronization policy for stateless functions to speed up the distributed model retraining leading to cloud cost minimization. A prototype implementation on AWS shows that Sandpiper maintains the average accuracy above 90%, while 3.8× less expensive than the traditional VM-based schemes.

In this paper, we have investigated the performance implications of SSL protocol for providing secure service in a cluster-based application server, and have proposed a backend forwarding scheme for improving server performance through better load balance. The proposed SSL/spl I.bar/with/spl I.bar/bf scheme exploits the underlying user-level communication minimizing the intra-cluster communication overhead. All results in this paper indicate that the proposed backend forwarding scheme is a viable mechanism for improving the performance of application servers in cluster-based data centers.

Design of high performance Web servers has become a recent research thrust to meet the increasing demand of network-based services. In this paper, we propose a new Web server architecture, called multi-threaded PIPELINED Web server, suitable for Symmetric Multi-Processor (SMP) or System-on-Chip (SoC) architectures. The proposed PIPELINED model consists of multiple thread pools, where each thread pool consists of five basic threads and two helper threads. The main advantages of the proposed model are global information sharing by the threads, minimal synchronization overhead due to less number of threads, and non-blocking I/O operations, possible with the helper threads.We have conducted an in-depth performance analysis of the proposed server model along with four prior Web server models (Multi-Process (MP), Multi-Thread (MT), Single-Process Event-Driven (SPED) and Asynchronous Multi-Process Event-Driven (AMPED)) via simulation using six Web server workloads. The experiments are conducted to investigate the impact of various factors such as the memory size, disk speed and numbers of clients. The simulation results indicate that the proposed PIPELINED Web server architecture shows the best performance across all system and workload parameters compared to the MP, MT, SPED and AMPED models. Although the MT and AMPED models show competitive performance with less number of processors, the advantage of the PIPELINED model becomes obvious as the number of processors or clients in an SMP/SoC machine increases. The MP model shows the worst performance in most of the cases. The results indicate that the proposed server architecture can be used in future large-scale SMP/SoC machines to boost system performance.