Data networks
Journal of the ACM (JACM)
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
A calculus approach to energy-efficient data transmission with quality-of-service constraints
IEEE/ACM Transactions on Networking (TON)
Utility maximization for delay constrained QoS in wireless
INFOCOM'10 Proceedings of the 29th conference on Information communications
Scheduling heterogeneous real-time traffic over fading wireless channels
INFOCOM'10 Proceedings of the 29th conference on Information communications
How internet concepts and technologies can help green and smarten the electrical grid
Proceedings of the first ACM SIGCOMM workshop on Green networking
Optimal transmission scheduling over a fading channel with energy and deadline constraints
IEEE Transactions on Wireless Communications
Performance of global load balancing by local adjustment
IEEE Transactions on Information Theory
A delay based optimization scheme for peak load reduction in the smart grid
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Online optimization for the smart (micro) grid
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
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The smart power grid harnesses information and communication technologies to enhance reliability and enforce sensible use of energy through effective management of demand load. We envision a scenario with real-time communication between the grid operator and the consumers. The operator controller receives consumer power demand requests with different power requirements, durations, and deadlines by which they are to be activated. The objective of the operator is to devise a power demand task scheduling policy that minimizes the grid operational cost over a time horizon. The cost is a convex function of total instantaneous power consumption and reflects the fact that each additional unit of power needed to serve demands is more expensive as the demand load increases. First, we study the off-line demand scheduling problem, where parameters are known a priori. If demands can be scheduled preemptively, the problem is a load balancing one, and we present an iterative algorithm that optimally solves it. If demands need to be scheduled non-preemptively, the problem is a bin packing one. Next, we devise a stochastic model for the case when demands are generated continually and scheduling decisions are taken online, and we focus on long-term average cost. We present two types of demand load control based on current power consumption. In the first one, the controller may choose to serve a new demand request upon arrival or postpone it to the end of its deadline. The second one, termed Controlled Release (CR) activates a new request if the current power consumption is less than a threshold, otherwise the demand is queued. Queued demands are activated when their deadlines expire, or if consumption drops below the threshold. We derive a lower performance bound for all policies, which is asymptotically achieved by the CR policy as deadlines increase. For both types above, optimal policies are of threshold nature. Numerical results validate the benefit of our approaches compared to the default policy of serving demands upon arrival.