Query compilation and hardware acceleration are important technologies for optimizing the performance of data processing engines. There have been many works on the exploration and adoption of these techniques in recent years. However, a number of engines still refrain from adopting them because of some reasons. One of the common reasons claims that the intricacies of these techniques make engines too complex to maintain. Another major barrier is the lack of widely accepted architectures and libraries of these techniques, which leads to the adoption often starting from scratch with lots of effort. In this paper, we propose Intel Big Data Analytic Toolkit (BDTK), an open-source C++ acceleration toolkit library for analytical data processing engines. BDTK provides lightweight, easy-to-connect, reusable components with interoperable interfaces to support query compilation and hardware accelerators. The query compilation in BDTK leverages vectorized execution and data-centric code generation to achieve high performance. BDTK could be integrated into different engines and helps them to adapt query compilation and hardware accelerators to optimize performance bottlenecks with less engineering effort.

Hardware accelerators have been a promising solution to reduce the cost of cloud datacenters. This article investigates the acceleration of an important datacenter workload: the web server (or proxy) that faces high computational consumption originated from SSL/TLS processing and HTTP compression. Our study reveals that for the widely-deployed event-driven web architecture, the straight offloading of SSL/TLS or compression tasks suffers from frequent blockings in the offload I/O, leading to the underutilization of both CPU and accelerator resources. To achieve efficient acceleration, we propose QWEB, a comprehensive offload solution based on Intel QuickAssist Technology (QAT). QWEB introduces an asynchronous offload mode for SSL/TLS processing and a pipelining offload mode for HTTP compression, both allowing concurrent offload tasks from a single application process/thread. With these two novel offload modes, the blocking penalty is amortized or even eliminated, and the utilization rate of the parallel computation engines inside the QAT accelerator is greatly increased. The evaluation shows that QWEB provides up to 9x handshake performance with TLS-RSA (2048-bit) over the software baseline. Additionally, the secure data transfer throughput is enhanced by 2x for the SSL/TLS offloading only, 3.5x for the compression offloading only and 5x for the combined offloading.

One of the characteristics of the current Web services is that many clients request the same or similar service from a group of replicated servers, e.g. music or movie downloading in peer-to-peer networks. Most of the time, servers are heterogeneous ones in terms of service rate. Much of research has been done in the homogeneous environment. However, there is has been little done on the heterogeneous scenario. It is important and urgent that we have models for heterogeneous server groups for the current Internet applications design and analysis. In this paper, we deploy an approximation method to transform heterogeneous systems into a group of homogeneous system. As a result, the previous results of homogeneous studies can be applied in heterogeneous cases. In order to test the approximation ratio of the proposed model to real applications, we conducted simulations to obtain the degree of similarity. We use two common strategies: random selection algorithm and Firs-Come-First-Serve (FCFS) algorithm to test the approximation ratio of the proposed model. The simulations indicate that the approximation model works well.