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The industry standard Packet CAPture (PCAP) format for storing network packet traces is normally only readable in serial due to its lack of delimiters, indexing, or blocking. This presents a challenge for parallel analysis of large networks, where packet traces can be many gigabytes in size. In this work we present RAPCAP, a novel method for random access into variable-length record collections like PCAP by identifying a record boundary within a small number of bytes of the access point. Unlike related heuristic methods that can limit scalability with a nonzero probability of error, the new method offers a correctness guarantee with a well formed file and does not rely on prior knowledge of the contents. We include a practical implementation of the algorithm with an extension to the Hadoop framework, and a performance comparison to serial ingestion. Finally, we present a number of similar storage types that could utilize a modified version of RAPCAP for random access.

The concept of Internet of Things (IoT) is rapidly moving from a vision to being pervasive in our everyday lives. This can be observed in the integration of connected sensors from a multitude of devices such as mobile phones, healthcare equipment, and vehicles. There is a need for the development of infrastructure support and analytical tools to handle IoT data, which are naturally big and complex. But, research on IoT data can be constrained by concerns about the release of privately owned data. In this paper, we present the design and implementation results of a synthetic IoT data generation framework. The framework enables research on synthetic data that exhibit the complex characteristics of original data without compromising proprietary information and personal privacy.

Academic data can be classified into multiple categories and come from a large number of sources. Many research areas require combining data from different sources into a unified set on which analytical techniques can be applied. In this research paper the authors introduce the High Performance Computing Cluster (HPCC) as a platform to streamline the process of ingesting, curating, integrating and transforming scholarly data from multiple sources and in varying formats, particularly when several of these datasets lack common attributes to support the integration process.

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses.

This work seeks to bridge areas of academic institutional research, social network analysis, and content analysis through the application of a social media paradigm to the academic research publishing environment. The concept is built upon the analysis of similarities and differences that exist in the structural and functional building blocks of academic publishing and social media. The potential impact in the form of new research directions that arise from this application are presented.

In this paper, we present JUMMP, the Job Uninterrupted Maneuverable MapReduce Platform, an automated scheduling platform that provides a customized Hadoop environment within a batch-scheduled cluster environment. JUMMP enables an interactive pseudo-persistent MapReduce platform within the existing administrative structure of an academic high performance computing center by “jumping” between nodes with minimal administrative effort. Jumping is implemented by the synchronization of stopping and starting daemon processes on different nodes in the cluster. Our experimental evaluation shows that JUMMP can be as efficient as a persistent Hadoop cluster on dedicated computing resources, depending on the jump time. Additionally, we show that the cluster remains stable, with good performance, in the presence of jumps that occur as frequently as the average length of reduce tasks of the currently executing MapReduce job. JUMMP provides an attractive solution to academic institutions that desire to integrate Hadoop into their current computing environment within their financial, technical, and administrative constraints.

Higher education has undergone considerable change in the past decades. As a result, the higher education community is collecting and disseminating a great deal of data that is typically used to benchmark performance or satisfy reporting requirements. This data is a rich source for scholarly inquiry, and particularly interesting for questions related to investment strategies within the academy. However, the real value of these data sets can often only realized when the data is viewed and studied across the aggregate collection of data sources. This is a complex task that requires gathering, cleaning, and applying consistent metadata standards to data sets. This paper presents a Unified Data Framework that allows the aggregation of high demand data sources into a single useful research resource that is relevant to research in higher education. The Unified Data Framework guides the aggregation of existing and new data sets, and provides the option of connecting and automatically, or semi-automatically, updating data from the original sources. The Unified Data Framework presents to researchers of higher education a robust suite of analytic tools for data mining and visualization of combined and complex data sources.

This research utilizes several well known forecasting techniques in combination with Empirical Mode Decomposition (EMD) to investigate the trade-offs of EMD's decomposition (sifting) step for forecasting the arrival workload of an enterprise cluster. The research is based on earlier work on the forecasting potential of EMD. Results show that EMD helps to improve forecasting results. Parallelization is used to perform extensive investigation across the full range of data. Future research is to increase the statistical confidence in the level of improvements possible when EMD is used as a decomposition method for forecasting.

This paper demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) to forecast the arrival time behaviors in a parallel batch system. An analysis of the workload records shows the existence of daily and weekly patterns within the workload. Results show that the intrinsic mode functions (IMF), products of the sifting/decomposition process of EMD, produce a better prediction than the original arrival histogram when used in a simple weight-matching prediction technique. Promising applications include the implementation of an EMD/neural network combination.

A version control repository structure has been created based on open source utility software. The structure consists of a subversion repository with an Apache Web interface that is protected by a Shibboleth authentication system. This structure can allow authorized and authenticated data sharing between institutions, yet retains simplicity and protects privacy for users. In addition, it also helps local administrators from having to perform extra account management for new users from other institutions

The goal of this project is to implement a 10-bit segmented current steering TSMC 0.25 /spl mu/m CMOS digital to analog converter. Binary coded 10-bit data is input to the converter. The converter converts all combinations of ten bits from digital form into the corresponding "staircase" voltage levels. A low pass filter placed after the output terminal of the converter is necessary to make the voltage level smooth.

We are developing interactive web pages for distance learning that use Macromedia Flash animation in undergraduate electrical engineering courses in circuits and signals and systems. We have animated key concepts such as steady-state analysis, time scale, time reversal, and convolution to allow students to better visualize these ideas. We are also developing ways to allow student input and immediate feedback in problem solving. The interactive web pages will be formally evaluated this year.

The authors report measurements taken on an electronic neural system configured for content addressable memory (CAM) using a high-capacity architecture. It is shown that Boltzmann and mean-field learning networks can be implemented in a parallel, analog VLSI system. This system was used to perform experiments with mean-field CAM. The hardware settles on a stored codeword in about 10 mu s roughly independent of code length. The capacity is far higher than that of the standard Hopfield architecture. >