DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling

In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.Comment: 18 pages, Accepted by Electronics 202

2011 Report of NSF Workshop Series on Scientific Software Security Innovation Institute

Over the period of 2010-2011, a series of two workshops were held in response to NSF Dear Colleague Letter NSF 10-050 calling for exploratory workshops to consider requirements for Scientific Software Innovation Institutes (S2I2s). The specific topic of the workshop series was the potential benefits of a security-focused software institute that would serve the entire NSF research and development community. The first workshop was held on August 6th, 2010 in Arlington, VA and represented an initial exploration of the topic. The second workshop was held on October 26th, 2011 in Chicago, IL and its goals were to 1) Extend our understanding of relevant needs of MREFC and large NSF Projects, 2) refine outcome from first workshop with broader community input, and 3) vet concepts for a trusted cyberinfrastructure institute. Towards those goals, the participants other 2011workshop included greater representation from MREFC and large NSF projects, and, for the most part, did not overlap with the participants from the 2010 workshop. A highlight of the second workshop was, at the invitation of the organizers, a presentation by Scott Koranda of the LIGO project on the history of LIGO’s identity management activities and how those could have benefited from a security institute. A key analysis he presented is that, by his estimation, LIGO could have saved 2 senior FTE-years of effort by following suitable expert guidance had it existed. The overarching finding from the workshops is that security is a critical crosscutting issue for the NSF software infrastructure and recommended a security focused activity to address this issue broadly, for example a security software institute (S2I2) under the SI2 program. Additionally, the 2010 workshop participants agreed to 15 key additional findings, which the 2011 workshop confirmed, with some refinement as discussed in this report.NSF Grant # 1043843Ope

Wizer: What-if analyzer for automated social model space exploration and validation

__________________________________________________________________ Complex social problems modeled by multi-agent systems have very large parameter and model space. The problem of how to model, validate, detect, and plan for the event of bioterrorism is one of the these, as it requires faithful modeling of dynamic signal (bioattack event) from complex dynamic noise (normal disease outbreaks and people activities). Indeed, the dynamic and very large space – numeric or symbolic or both – nature of the problem makes manual exploration spotty, cumbersome, implicitly-biased, and thus incomplete. Scaling up multi-agent systems exacerbates these and makes the automation of exploration, modeling, and validation more critical. WIZER – a social inference engine and simulation combination capable of principled exploration through meta-models and parameters based on empirical data and knowledge – addresses the above problems by knowledge-guided & simulation-guided search. This paper describes the design of WIZER and presents a preliminary result

Simulation Validation for Societal Systems

University. This work also leveraged work supported by the NSF on multi-agent modeling. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect those of the sponsors

A Dynamic Social Network Experiment with Multi-team Systems

This paper describes the use of VBS High-Fidelity 3D Game to perform experiments on multi-team systems. Multi-team systems (MTS) are a natural part of human social phenomena and online social networks as people form groups with shared goals and interests. We gathered data on human players (on communications and interactions) who were engaged in a VBS game scenario. Using Relational Event Modeling (REM), we analyzed the results. The results suggest some synchronization and cross-team communication have both direct effects with team performance and, in some cases, can moderate the effect of false information in environments of uncertainty