Abstract. Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to users' queries. While the performance of an information retrieval (IR) system can be enhanced through the compression of its posting lists, there is little recent work in the literature that thoroughly compares and analyses the performance of modern integer compression schemes across different types of posting information (document ids, frequencies, positions). In this paper, we experiment with different modern integer compression algorithms, integrating these into a modern IR system. Through comprehensive experiments conducted on two large, widely used document corpora and large query sets, our results show the benefit of compression for different types of posting information to the space-and time-efficiency of the search engine. Overall, we find that the simple Frame of Reference compression scheme results in the best query response times for all types of posting information. Moreover, we observe that the frequency and position posting information in Web corpora that have large volumes of anchor text are more challenging to compress, yet compression is beneficial in reducing average query response times.

Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs, but they are necessary to ensure low latencies, since users expect sub-second response times (e.g., 500 ms). However, users can hardly notice response times that are faster than their expectations. Hence, we propose the Predictive Energy Saving Online Scheduling Algorithm (PESOS) to select the most appropriate CPU frequency to process a query on a per-core basis. PESOS aims at process queries by their deadlines, and leverage high-level scheduling information to reduce the CPU energy consumption of a query processing node. PESOS bases its decision on query efficiency predictors, estimating the processing volume and processing time of a query. We experimentally evaluate PESOS upon the TREC ClueWeb09B collection and the MSN2006 query log. Results show that PESOS can reduce the CPU energy consumption of a query processing node up to ∼48% compared to a system running at maximum CPU core frequency. PESOS outperforms also the best state-of-the-art competitor with a ∼20% energy saving, while the competitor requires a fine parameter tuning and it may incurs in uncontrollable latency violations.

Web search engine companies require power-hungry data centers with thousands of servers to efficiently perform searches on a large scale. This permits the search engines to serve high arrival rates of user queries with low latency, but poses economical and environmental concerns due to the power consumption of the servers. Existing power saving techniques sacrifice the raw performance of a server for reduced power absorption, by scaling the frequency of the server's CPU according to its utilization. For instance, current Linux kernels include frequency governors i.e., mechanisms designed to dynamically throttle the CPU operational frequency. However, such general-domain techniques work at the operating system level and have no knowledge about the querying operations of the server. In this work, we propose to delegate CPU power management to search engine-specific governors. These can leverage knowledge coming from the querying operations, such as the query server utilization and load. By exploiting such additional knowledge, we can appropriately throttle the CPU frequency thereby reducing the query server power consumption. Experiments are conducted upon the TREC ClueWeb09 corpus and the query stream from the MSN 2006 query log. Results show that we can reduce up to ∼24% a server power consumption, with only limited drawbacks in effectiveness w.r.t. a system running at maximum CPU frequency to promote query processing quality.