Journal of the ACM (JACM)
Online computation and competitive analysis
Online computation and competitive analysis
On-line analysis of the TCP acknowledgment delay problem
Journal of the ACM (JACM)
Dynamic TCP acknowledgement: penalizing long delays
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
SWAT '02 Proceedings of the 8th Scandinavian Workshop on Algorithm Theory
Control Message Aggregation in Group Communication Protocols
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Latency constrained aggregation in sensor networks
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Algorithms for distributed functional monitoring
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Multi-dimensional online tracking
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Online algorithms to minimize resource reallocations and network communication
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Competitive analysis for service migration in VNets
Proceedings of the second ACM SIGCOMM workshop on Virtualized infrastructure systems and architectures
Online strategies for intra and inter provider service migration in virtual networks
IPTcomm '11 Proceedings of the 5th International Conference on Principles, Systems and Applications of IP Telecommunications
The Wide-Area Virtual Service Migration Problem: A Competitive Analysis Approach
IEEE/ACM Transactions on Networking (TON)
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We attend to the classic setting where an observer needs to inform a tracker about an arbitrary time varying function f: ℕ0 →ℤ. This is an optimization problem, where both wrong values at the tracker and sending updates entail a certain cost. We consider an online variant of this problem, i.e., at time t, the observer only knows f(t′) for all t′≤t. In this paper, we generalize existing cost models (with an emphasis on concave and convex penalties) and present two online algorithms. Our analysis shows that these algorithms perform well in a large class of models, and are even optimal in some settings.