Infrastructure as Code (IaC) has become the de-facto standard method for managing cloud resources. Just like general source code (e.g., Java, etc.), infrastructure code also has numerous bugs so it needs to be tested. While several testing frameworks for IaC for cloud systems have been developed in practice, researchers have paid little attention to their testing. This study presents an empirical investigation of the use of tests for IaC for cloud systems. Our empirical results show that (i) 55.2% of the repositories using Terratest have at least one server infrastructure test; (ii) developers often maintain server infrastructure tests (1.7%-11.3% commits out of all the commits); (iii) many repositories have tests for system functionality (28%), deployment (20%), and configuration (17%).

Web spam is one of the most important problems which degrade quality and efficiency of web search engines. In this paper, we present a novel link-based ant colony optimization learning algorithm for spam host detection. The host graph is first constructed by aggregating pages' hyperlink structure. Following the Trust Rank assumption, ants start walking from a normal host and randomly follow host links with a probability distribution. Then, the classification rules are appropriately generated according to common features of normal hosts sequentially discovered by ants. From the experiments with the WEBSPAM-UK2006 dataset, the proposed learning model provides much accuracy in classifying both normal and spam hosts than several baselines, including a state of the art C4.5. Moreover, we also provide an analysis in parameter tuning for better results.

We investigate the use of graphics processing units (GPUs) in accelerating Page Rank computation. We first introduce a compact web graph representation which requires much less memory allocation than a well-known compressed sparse row format. The web graph is then simply partition into smaller chunks to fit the GPUs' device memory. We propose a fast Page Rank algorithm to run on the GPU cluster. The design of algorithm is general and does not constrain on any large web graph fitting to the limited size of device memory. In the experiments, we test our Page Rank algorithm on a small GPU cluster, using a set of real web data. We compare the parallel Page Rank computation utilizing GPUs with CPUs. The results show that the proposed Page Rank computation on GPUs gives promising result.

Link analysis is a critical component of current Internet search engines' results ranking software, which determines the ordering of query results returned to the user. The ordering of query results can have an enormous impact on web traffic and the resulting business activity of an enterprise; hence businesses have a strong interest in having their Web pages highly ranked in search engine results. This has led to attempts to artificially inflate page ranks by spamming the link structure of the Web. Building an artificial condensed link structure called a "link farm" is one technique to influence a page ranking system, such as the popular PageRank algorithm. In this paper, we present an approach to remove the bias due to link farms from PageRank computation. We propose a method to first measure the PageRank weight accumulated by link farms, and then distribute the weight to other web pages by a modification of the transition matrix in the standard PageRank algorithm. We present results of a selected Web graph that is manually spammed. The results show that the proposed approach can effectively reduce the bias from link farms in PageRank computation.

Association rule mining is one of the most important techniques in data mining. It extracts significant patterns from transaction databases and generates rules used in many decision support applications. Many organizations such as industrial, commercial, or even scientific sites may produce large amount of transactions and attributes. Mining effective rules from such large volumes of data requires much time and computing resources. In this paper, we propose a parallel FI-growth association rule mining algorithm for rapid extraction of frequent itemsets from large dense databases. We also show that this algorithm can efficiently be parallelized in a cluster computing environment. The preliminary experiments provide quite promising results, with nearly ideal scaling on small clusters and about half of ideal (15 fold speedup) on a thirty-two processor cluster.

Re-ranking the search results using PageRank is a well-known technique used in modern search engines. Running an iterative algorithm like PageRank on a large Web graph consumes both much computing resource and time. This paper therefore proposes a parallel adaptive technique for computing PageRank using the PC cluster. Following the study of the Stanford WebBase group on convergence patterns of PageRank scores of pages using the conventional PageRank algorithm, PageRank scores of most pages converge more quickly than the remainder, we then devise our parallel adaptive algorithm to reiterate the computation for pages whose PageRank scores are still not converged. From experiments using a synthesized Web graph of 28 million pages and around 227 million hyperlinks, we obtain the acceleration rate up to 6-8 times using 32 PC processors.

Efficient computing the PageRank scores for a large Web graph is actually one of the hot issues in Web-IR community. Recent research projects have been proposed to accelerate the computation, both in algorithmic and architectural ways. We focus on a parallel PageRank computational architecture on a cluster of Opteron PCs networked via a gigabit Ethernet. We propose both an efficient parallel algorithm of the standard PageRank computation, and a simple pairwise communication model needed to synchronize local PageRank scores between processors. Our experimental results conducted on a large Web graph, over 1.5 billion links, synthesized from the real set of crawled Web pages in the TH domain, are quite promising. The current implementation takes less than 15 seconds for an iteration run.