Introduction to algorithms
Pricing in computer networks: motivation, formulation, and example
IEEE/ACM Transactions on Networking (TON)
Computers and Operations Research
Link-sharing and resource management models for packet networks
IEEE/ACM Transactions on Networking (TON)
Uniform versus priority dropping for layered video
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Promoting the use of end-to-end congestion control in the Internet
IEEE/ACM Transactions on Networking (TON)
Algorithms for Scheduling Independent Tasks
Journal of the ACM (JACM)
Adaptive Packet Marking for Providing Differentiated Services in the Internet
ICNP '98 Proceedings of the Sixth International Conference on Network Protocols
Lex-Optimal Online Multiclass Scheduling with Hard Deadlines
Mathematics of Operations Research
Scheduling real-time traffic in ATM networks
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Parallel Rollout for Online Solution of Partially Observable Markov Decision Processes
Discrete Event Dynamic Systems
Scheduling an active camera to observe people
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Surveillance camera scheduling: a virtual vision approach
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Parallelizing parallel rollout algorithm for solving Markov decision processes
WOMPAT'03 Proceedings of the OpenMP applications and tools 2003 international conference on OpenMP shared memory parallel programming
Multi-query scheduling for time-critical data stream applications
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
| Hi-index | 0.00 |
We consider the problem of scheduling an arriving sequence of packets at a single server. Associated with each packet is a deadline by which the packet must be scheduled. Each packet belongs to one of a predetermined set of classes, and each class has an associated weight value. The goal is to minimize the total weighted value of the packets that miss their deadlines. We first prove that there is no policy that minimizes this weighted loss for all finite arrival sequences of packets. We then present a class of greedy scheduling policies, called the current-minloss throughput-optimal (CMTO) policies. We characterize all CMTO policies, and provide examples of easily implementable CMTO policies. We compare CMTO policies with a multiclass extension of the earliest-deadline-first (EDF) policy, called EDF+, establishing that a subclass of CMTO policies achieves no more weighted loss than EDF+ for any traffic sequence, and at the same time achieves a substantial weighted-loss advantage over EDF+ for some traffic sequences – this advantage is shown to be arbitrarily close to the maximum possible achievable advantage. We also provide empirical results to quantify the weighted-loss advantage of CMTO policies over EDF+ and the static-priority (SP) policy, showing an advantage exceeding an order of magnitude when serving heavy-tailed aggregations of MPEG traces.