A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

Designing a Hadoop system based on computational resources and network delay for wide area networks

This paper proposes a Hadoop system that considers both slave server’s processing capacity and network delay for wide area networks to reduce the job processing time. The task allocation scheme in the proposed Hadoop system divides each individual job into multiple tasks using suitable splitting ratios and then allocates the tasks to different slaves according to the computational capability of each server and the availability of network resources. We incorporate software-defined networking to the proposed Hadoop system to manage path computation elements and network resources. The performance of proposed Hadoop system is experimentally evaluated with fourteen machines located in the different parts of the globe using a scale-out approach. A scale-out experiment using the proposed and conventional Hadoop systems is conducted by executing both single job and multiple jobs. The practical testbed and simulation results indicate that the proposed Hadoop system is effective compared to the conventional Hadoop system in terms of processing time

Designing, Building, and Modeling Maneuverable Applications within Shared Computing Resources

Extending the military principle of maneuver into war-fighting domain of cyberspace, academic and military researchers have produced many theoretical and strategic works, though few have focused on researching actual applications and systems that apply this principle. We present our research in designing, building and modeling maneuverable applications in order to gain the system advantages of resource provisioning, application optimization, and cybersecurity improvement. We have coined the phrase “Maneuverable Applications” to be defined as distributed and parallel application that take advantage of the modification, relocation, addition or removal of computing resources, giving the perception of movement. Our work with maneuverable applications has been within shared computing resources, such as the Clemson University Palmetto cluster, where multiple users share access and time to a collection of inter-networked computers and servers. In this dissertation, we describe our implementation and analytic modeling of environments and systems to maneuver computational nodes, network capabilities, and security enhancements for overcoming challenges to a cyberspace platform. Specifically we describe our work to create a system to provision a big data computational resource within academic environments. We also present a computing testbed built to allow researchers to study network optimizations of data centers. We discuss our Petri Net model of an adaptable system, which increases its cybersecurity posture in the face of varying levels of threat from malicious actors. Lastly, we present work and investigation into integrating these technologies into a prototype resource manager for maneuverable applications and validating our model using this implementation