Competitive algorithms for layered graph traversal
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Information and Computation
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
On-line choice of on-line algorithms
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Competitive non-preemptive call control
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Improved combination of online algorithms for acceptance and rejection
Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures
Admission control to minimize rejections and online set cover with repetitions
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Buyback problem: approximate matroid intersection with cancellation costs
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
| Hi-index | 0.00 |
Resource allocation and admission control are critical tasks in a communication network, that often must be performed online. Algorithms for these types of problems have been considered both under benefit models (e.g., with a goal of approximately maximizing the number of calls accepted) and under cost models (e.g., with a goal of approximately minimizing the number of calls rejected). Unfortunately, algorithms designed for these two measures can often be quite different, even polar opposites (e.g., [1, 8]). In this work we consider the problem of combining algorithms designed for each of these objectives in a way that simultaneously is good under both measures. More formally, we are given an algorithm A which is cA competitive w.r.t. the number of accepted calls and an algorithm R which is cR competitive w.r.t. the number of rejected calls. We derive a combined algorithm whose competitive ratio is O(cR cA) for rejection and O(cA2) for acceptance. We also show building on known techniques that given a collection of k algorithms, we can construct one master algorithm which performs similar to the best algorithm among the k for the acceptance problem and another master algorithm which performs similar to the best algorithm among the k for the rejection problem. Using our main result we can combine the two master algorithms to a single algorithm which guarantees both rejection and acceptance competitiveness.