Search engine

A search engine is a software system that finds web pages that match a web search. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a line of results, often referred to as search engine results pages (SERPs). The information may be a mix of hyperlinks to web pages, images, videos, infographics, articles, and other types of files. Some search engines also mine data available in databases or open directories. Unlike web directories and social bookmarking sites, which are maintained by human editors, search engines also maintain real-time information by running an algorithm on a web crawler. Any internet-based content that cannot be indexed and searched by a web search engine falls under the category of deep web. History Pre-1990s A system for locating published information intended to overcome the ever-increasing difficulty of locating information

Scale-Out Processors

Pejman Lotfi Kamran

Global-scale online services, such as Google’s Web search and Facebook’s social networking, run in large-scale datacenters. Due to their massive scale, these services are designed to scale out (or distribute) their respective loads and datasets across thousands of servers in datacenters. The growing demand for online services forced service providers to build networks of datacenters, which require an enormous capital outlay for infrastructure, hardware, and power consumption. Consequently, efficiency has become a major concern in the design and operation of such datacenters, with processor efficiency being of, utmost importance, due to the significant contribution of processors to the overall datacenter performance and cost. Scale-out workloads, which are behind today’s online services, serve independent requests, and have large instruction footprints and little data locality. As such, they benefit from processor designs that feature many cores and a modestly sized Last-Level Cache (LLC), a fast access path to the LLC, and high-bandwidth interfaces to memory. Existing server-class processors with large LLCs and a handful of aggressive out-of-order cores are inefficient in executing scale-out workloads. Moreover, the scaling trajectory for these processors leads to even lower efficiency in future technology nodes. This thesis presents a family of throughput-optimal processors, called Scale-Out Processors, for the efficient execution of scale-out workloads. A unique feature of Scale-Out Processors is that they consist of multiple stand-alone modules, called pods, wherein each module is a server running an operating system and a full software stack. To design a throughput-optimal processor, we developed a methodology based on performance density, defined as throughput per unit area, to quantify how effectively an architecture uses the silicon real estate. The proposed methodology derives a performance-density optimal processor building block (i.e., pod), which tightly couples a number of cores to a small LLC via a fast interconnect. Scale-Out Processors simply consist of multiple pods with no inter-pod connectivity or coherence. Moreover, they deliver the highest throughput in today’s technology and afford near-ideal scalability as process technology advances. We demonstrate that Scale-Out Processors improve datacenters’ efficiency by 4.4x-7.1x over datacenters designed using existing server-class processors.

EPFL2013

Scalable peer-to-peer Web search using highly discriminative keys

Vinh Toan Luu

Standard general-purpose Web retrieval relies on centralized search engines that do not realistically scale when applied to the exponentially growing number of documents available on the Web. By taking advantage of the resource sharing principle, Peer-to-Peer (P2P) techniques represent a promising architectural alternative for building decentralized Web search engines offering true Web-size scalability, provided that enough peers are available. However, in all such P2P approaches proposed so far, excessive network bandwidth consumption during retrieval, caused by the necessary transmission of possibly very long posting lists, was identified as the major bottleneck for implementing truly scalable P2P full-text Web retrieval. The main objective of the present research is thus to find a decentralized indexing/retrieval strategy that fully exploits the distributed computation possibilities provided by P2P networks, but keeps the required network bandwidth consumption scalable, while guaranteeing an acceptable retrieval quality. To address this problem we introduce a novel indexing/retrieval model based on Highly Discriminative Keys (HDKs), which correspond to carefully selected indexing terms and term sets associated with posting lists truncated to the top-k most relevant documents with respect to the associated key. Using HDKs for indexing thus increases the number of indexing features but, at the same time, strictly limits the size of the associated posting lists. When combined with an adequate retrieval model, this leads to strongly reduced network traffic. More precisely, our experimental results show that HDK-based indexing and retrieval lead to storage and bandwidth requirements that remain manageable with respect to the growth of document collection while preserving a fully acceptable retrieval quality.

EPFL2007

Scale-Out Processors

Pejman Lotfi Kamran

EPFL2013

Web Search Engine for a Social Network

Scale-Out Processors

Scalable peer-to-peer Web search using highly discriminative keys

Web Search Engine for a Social Network

Scale-Out Processors

Scalable peer-to-peer Web search using highly discriminative keys