Distributed and parallel applications are critical information technology systems in multiple industries, including academia, military, government, financial, medical, and transportation. These applications present target rich environments for malicious attackers seeking to disrupt the confidentiality, integrity and availability of these systems. Applying the military concept of defense cyber maneuver to these systems can provide protection and defense mechanisms that allow survivability and operational continuity. Understanding the tradeoffs between information systems security and operational performance when applying maneuver principles is of interest to administrators, users, and researchers. To this end, we present a model of a defensive maneuver cyber platform using Stochastic Petri Nets. This model enables the understanding and evaluation of the costs and benefits of maneuverability in a distributed application environment, specifically focusing on moving target defense and deceptive defense strategies.

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.

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Software-defined networking combined with distributed and parallel applications has the potential to deliver optimized application performance at runtime. In order to investigate this enhancement and design future implementation, a datacenter with a programmable topology integrated with application state is needed. Towards this goal, we introduce the Flow Optimized Route Configuration Engine (FORCE). The FORCE is an emulated datacenter testbed with a programmable interconnection controlled by an SDN controller. We also utilize Hadoop as a case study of distributed and parallel applications along with a simulated Hadoop shuffle traffic generator. The testbed provides initial experimental evidence of support to our hypothesis for future SDN research. Our experiments on the testbed show a difference in application runtime a factor of over 2.5 times on shuffle traffic for Hadoop MapReduce jobs and the potential for significant speedup in warehouse scale data centers.