Wagner Meira

Energy conservation in heterogeneous server clusters

The previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we argue that designing efficient servers for heterogeneous clusters requires defining an efficiency metric, modeling the different types of nodes with respect to the metric, and searching for request distributions that optimize the metric. To concretely illustrate this process, we design a cooperative Web server for a heterogeneous cluster that uses modeling and optimization to minimize the energy consumed per request. Our experimental results for a cluster comprised of traditional and blade nodes show that our server can consume 42% less energy than an energy-oblivious server, with only a negligible loss in throughput. The results also show that our server conserves 45% more energy than an energy-conscious server that was previously proposed for homogeneous clusters.

A hierarchical characterization of a live streaming media workload

We present a thorough characterization of what we believe to be the first significant live Internet streaming media workload in the scientific literature. Our characterization of over 3.5 million requests spanning a 28-day period is done at three increasingly granular levels, corresponding to clients, sessions, and transfers. Our findings support two important conclusions. First, we show that the nature of interactions between users and objects is fundamentally different for live versus stored objects. Access to stored objects is user driven, whereas access to live objects is object driven. This reversal of active/passive roles of users and objects leads to interesting dualities. For instance, our analysis underscores a Zipf-like profile for user interest in a given object, which is in contrast to the classic Zipf-like popularity of objects for a given user. Also, our analysis reveals that transfer lengths are highly variable and that this variability is due to client stickiness to a particular live object, as opposed to structural (size) properties of objects. Second, by contrasting two live streaming workloads from two radically different applications, we conjecture that some characteristics of live media access workloads are likely to be highly dependent on the nature of the live content being accessed. This dependence is clear from the strong temporal correlation observed in the traces, which we attribute to the impact of synchronous access to live content. Based on our analysis, we present a model for live media workload generation that incorporates many of our findings, and which we implement in GISMO.

In search of invariants for e-business workloads

ABSTRACT Understanding the nature and hara teristi s of e-business workloads is a ru ial step to improve the quality of servi e o ered to ustomers in ele troni business environments. However, the variety and omplexity of the intera tions between ustomers and sites make the hara terization of ebusiness workloads a hallenging problem. Using a multilayer hierar hi al model, this paper presents a detailed hara terization of the workload of two a tual e-business sites: an online bookstore and an ele troni au tion site. Through the hara terization pro ess, we found the presen e of autonomous agents, or robots, in the workload and used the hierar hi al stru ture to determine their hara teristi s. We also found that sear h terms follow a Zipf distribution.

Rank-preserving two-level caching for scalable search engines

We present an e ective caching scheme that reduces the computing and I/O requirements of a Web search engine without altering its ranking characteristics. The novelty is a two-level caching scheme that simultaneously combines cached query results and cached inverted lists on a real case search engine. A set of log queries are used to measure and compare the performance and the scalability of the search engine with no cache, with the cache for query results, with the cache for inverted lists, and with the two-level cache. Experimental results show that the two-level cache is superior, and that it allows increasing the maximum number of queries processed per second by a factor of three, while preserving the response time. These results are new, have not been reported before, and demonstrate the importance of advanced caching schemes for real case search engines.

Limiting the power consumption of main memory

The peak power consumption of hardware components affects their powersupply, packaging, and cooling requirements. When the peak power consumption is high, the hardware components or the systems that use them can become expensive and bulky. Given that components and systems rarely (if ever) actually require peak power, it is highly desirable to limit power consumption to a less-than-peak power budget, based on which power supply, packaging, and cooling infrastructure scan be more intelligently provisioned. In this paper, we study dynamic approaches for limiting the powerconsumption of main memories. Specifically, we propose four techniques that limit consumption by adjusting the power states of thememory devices, as a function of the load on the memory subsystem. Our simulations of applications from three benchmarks demonstrate that our techniques can consistently limit power to a pre-established budget. Two of the techniques can limit power with very low performance degradation. Our results also show that, when using these superior techniques, limiting power is at least as effective an energy-conservation approach as state-of-the-art technique sexplicitly designed for performance-aware energy conservation. These latter results represent a departure from current energy management research and practice.

Dengue surveillance based on a computational model of spatio-temporal locality of Twitter

Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and public perception. First we explore the public perception dimension by performing sentiment analysis. This analysis enables us to filter out content that is not relevant for the sake of Dengue surveillance. Then, we verify the high correlation between the number of cases reported by official statistics and the number of tweets posted during the same time period (i.e., R2 = 0.9578). A clustering approach was used in order to exploit the spatio-temporal dimension, and the quality of the clusters obtained becomes evident when they are compared to official data (i.e., RandIndex = 0.8914). As an application, we propose a Dengue surveillance system that shows the evolution of the dengue situation reported in tweets, which is implemented in www.observatorio.inweb.org.br/dengue/.

Lazy Associative Classification

Decision tree classifiers perform a greedy search for rules by heuristically selecting the most promising features. Such greedy (local) search may discard important rules. Associative classifiers, on the other hand, perform a global search for rules satisfying some quality constraints (i.e., minimum support). This global search, however, may generate a large number of rules. Further, many of these rules may be useless during classification, and worst, important rules may never be mined. Lazy (non-eager) associative classification overcomes this problem by focusing on the features of the given test instance, increasing the chance of generating more rules that are useful for classifying the test instance. In this paper we assess the performance of lazy associative classification. First we demonstrate that an associative classifier performs no worse than the corresponding decision tree classifier. Also we demonstrate that lazy classifiers outperform the corresponding eager ones. Our claims are empirically confirmed by an extensive set of experimental results. We show that our proposed lazy associative classifier is responsible for an error rate reduction of approximately 10% when compared against its eager counterpart, and for a reduction of 20% when compared against a decision tree classifier. A simple caching mechanism makes lazy associative classification fast, and thus improvements in the execution time are also observed.

From bias to opinion: a transfer-learning approach to real-time sentiment analysis

Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution. Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.

Mining attribute-structure correlated patterns in large attributed graphs

In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Existing methods are not able to extract relevant knowledge regarding how vertex attributes interact with dense subgraphs. Structural correlation pattern mining combines aspects of frequent itemset and quasi-clique mining problems. We propose statistical significance measures that compare the structural correlation of attribute sets against their expected values using null models. Moreover, we evaluate the interestingness of structural correlation patterns in terms of size and density. An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented. We apply our method for the analysis of three real-world attributed graphs: a collaboration, a music, and a citation network, verifying that it provides valuable knowledge in a feasible time.

VM-based shared memory on low-latency, remote-memory-access networks

Recent technological advances have produced network interfaces that provide users with very low-latency access to the memory of remote machines. We examine the impact of such networks on the implementation and performance of software DSM. Specifically, we compare two DSM systems---Cashmere and TreadMarks---on a 32-processor DEC Alpha cluster connected by a Memory Channel network.Both Cashmere and TreadMarks use virtual memory to maintain coherence on pages, and both use lazy, multi-writer release consistency. The systems differ dramatically, however, in the mechanisms used to track sharing information and to collect and merge concurrent updates to a page, with the result that Cashmere communicates much more frequently, and at a much finer grain.Our principal conclusion is that low-latency networks make DSM based on fine-grain communication competitive with more coarse-grain approaches, but that further hardware improvements will be needed before such systems can provide consistently superior performance. In our experiments, Cashmere scales slightly better than TreadMarks for applications with false sharing. At the same time, it is severely constrained by limitations of the current Memory Channel hardware. In general, performance is better for TreadMarks.