CISE - CNS

COMPUTER SYSTEMS RESEARCH (CSR)

The CSR program seeks proposals focused on compelling research problems with potential to advance the state-of-the-art on how to improve the design, use, behavior and stability of computer and software systems within and across the following systems areas: distributed and Internet scale computing, massively parallel and data intensive computing, and pervasive and ubiquitous computing.

Broad categories of research interest within CSR include, but are not limited to:

• Scalable and robust frameworks, methodologies, algorithms and tools for resource management, protection and virtualization in large-scale heterogeneous, potentially failure prone, environments,
  
  
• New directions and innovative approaches for the design, implementation and management of modern storage and file systems, including energy-efficient and self-managing storage, access-anywhere and personal storage,
  
  
• Innovative approaches to massive data and metadata access, caching, replication and consistency,
  
  
• Scalable, context-aware and energy-efficient system services,
  
  
• Scalable and robust system and software architectures, models and programming abstractions to support changing trends and emerging technologies, such as sizes or speeds of processors, access memory and storage, data-intensive computing, ubiquitous and pervasive computing and peer-to-peer computing,
  
  
• Energy and context aware paradigms, methodologies and tools to improve system manageability, configurability, operational sustainability and performance, and to reduce vulnerabilities while improving usability,
  
  
• Frameworks, methodologies and tools for quantitative and qualitative evaluation, monitoring and prediction of complex computer systems behavior and performance.
  
  
• Fundamental and system-level research on energy-aware architectures and design methodologies,
  
  
• Novel parallel programming models and abstractions, compiler and dynamic run-time support for parallel programming and coordination languages, and advanced resource management frameworks, methods and tools to support highly parallel data-intensive computing environments,
  
  
• Innovative energy-efficient, fault-tolerant run-time execution environments, service architectures and coordination frameworks to enable large-scale concurrent execution across heterogeneous parallel and distributed computational platforms,
  
  
• Advanced frameworks, programming abstractions and models for parallel computing environments,
  
  
• New paradigms, frameworks and tools for automatic parallelization, synchronization and concurrency control,
  
  
• Power and energy-aware compilation and runtime optimization techniques for parallel computing, including dynamic and adaptive compilation, automatic code generation, program characterization and phase analysis techniques for optimized performance, and computation steering for reliability, scalability and improved performance,
  
  
• Advanced resource management frameworks, methods and tools, including scalable scheduling algorithms for resource and data intensive parallel systems, multi-criteria scheduling frameworks and algorithms, tools and environments for workflow scheduling in parallel systems, and adaptive and dynamic load balancing algorithms and tools,
  
  
• Application and system level methodologies and tools that exploit the characteristics of the hardware and execution environment to achieve high-level parallelism; and novel frameworks, methodologies and tools for performance prediction and evaluation of complex parallel systems and large-scale parallel applications,
  
  
• New paradigms, methodologies, algorithms and tools to enable robust and highly reliable real-time systems, across diverse computing and software platforms, capable of operating in widely distributed and highly interactive and uncertain environments.
  
  
• Novel frameworks, methodologies and software systems for distributed sensing and data
  
  
• New paradigms, mechanisms and tools for real-time resource management that address and integrates multiple resource constraints and performance requirements, such as power, clock frequency and thermal gain, task dependence, real-time guarantees, and criticality level.