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Grand Challenges in Methodologies for Complex Networks

Grand Challenges in Methodologies for Complex Networks. Ness Shroff Dept. of ECE and CSE The Ohio State University E-mail: shroff@ece.osu.edu. September 20, 2012. Complex Networks. Heterogeneous Mobile Dynamic System Rule-based or Selfish “agents” interact Multi-time scale

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Grand Challenges in Methodologies for Complex Networks

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  1. Grand Challenges in Methodologies for Complex Networks Ness Shroff Dept. of ECE and CSE The Ohio State University E-mail: shroff@ece.osu.edu September 20, 2012

  2. Complex Networks • Heterogeneous • Mobile • Dynamic System • Rule-based or Selfish “agents” interact • Multi-time scale • Varied Aggregation • Limited feedback • Uncertainty (stochasticity) • Local and Global (Resource) Constraints

  3. Examples of Complex Networks • Communication Networks • Internet • Wireless & Sensor Networks • Online Social Networks • Professional (LinkedIn…) • Personal (Facebook, Twitter…) • Cyber-physical • Smart-grid • Actuator based sensor networks • Cloud • Data-center networks…

  4. Methodological Successes • Stochastic optimization and control unified with combinatorial techniques • Mathematical Decomposition Framework • Distributed and robust low-complexity protocols • Opportunistic scheduling (MAC) • Congestion control • Routing • Energy/Power control… • Glauber Dynamics (statistical physics) • Global optima can be achieved through purely local interactions • Focus: • Long-term metrics (stability, throughput, lifetime, energy…) • Less so on short-term metrics (delay, convergence speeds…)

  5. Grand Challenges • Analytical framework to design solutions that simultaneously achieve: low complexity, high-throughput, and low delay • Deep connections between calculus of variations, probabilistic methods, limit theorems, and combinatorial techniques • Control “meta-dynamics” taking into account user preferences, social interactions, cyber-physical interplay to achieve global behavior (optimality, consensus, equilibria…) • New methodologies involving dynamic game theory, but nowwith underlying social/cyberphysical graph structures and user behavior (rational vs myopic behavior) • Manage uncertainty and sensitivities to imperfections (e.g., feedback delays, errors, non-observability…) • Breakthroughs in partially observable decision processes (POMDP) • New learning techniques to infer system and user behavior in this highly dynamic setting

  6. Thank you

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