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

Maneuverable Applications: Advancing Distributed Computing

Extending the military principle of maneuver into the war-fighting domain of cyberspace, academic and military researchers have produced many theoretical and strategic works, though few have focused on researching the applications and systems that apply this principle. We present a survey of our research in developing new architectures for the enhancement of parallel and distributed applica-tions. Specifically, we discuss our work in applying the military concept of maneuver in the cyberspace domain by creating a set of applications and systems called “ma-neuverable applications.” Our research investigates resource provisioning, application optimization, and cybersecurity enhancement through the modification, relocation, addition or removal of computing resources. We first describe our work to create a system to provision a big data computational re-source within academic environments. Secondly, we present a computing testbed built to allow researchers to study network optimizations of data centers. Thirdly, we discuss our Petri Net model of an adaptable system, which increases its cyber security posture in the face of varying levels of threat from malicious actors. Finally, we present evidence that traditional ideas about extending maneuver into cyberspace focus on security only, but computing can benefit from maneuver in multiple manners beyond security

JUMMP: Job Uninterrupted Maneuverable MapReduce Platform

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