Probabilistic alternatives for competitive analysis

In the last 20 years competitive analysis has become the main tool for analyzing the quality of online algorithms. Despite of this, competitive analysis has also been criticized: it sometimes cannot discriminate between algorithms that exhibit significantly different empirical behavior or it even favors an algorithm that is worse from an empirical point of view. Therefore, there have been several approaches to circumvent these drawbacks. In this survey, we discuss probabilistic alternatives for competitive analysis.operations research and management science;

Efficient Algorithms for Online Task Placement on Runtime Partially Reconfigurable FPGA

Recent generations of FPGAs allow run-time partial reconfiguration. One of the challenging problems in such a multitasking systems is online placement of task. Many online task placement algorithms designed for such partially reconfigurable systems have been proposed to provide efficient and fast task placement. In this paper two different approaches are being used to place the incoming tasks. The first method is uses a run-length based representation that defines the vacant slots on the FPGA. This compact representation allows the algorithm to locate a vacant area suitable to accommodate the incoming task quickly. In the proposed FPGA model, the CLBs are numbered according to Peano Space filling curve model. The second approach is based on harmonic packing. Simulation experiments indicate that proposed techniques result in low ratio of task rejection compared to existing techniques

Online Optimization with Lookahead

The main contributions of this thesis consist of the development of a systematic groundwork for comprehensive performance evaluation of algorithms in online optimization with lookahead and the subsequent validation of the presented approaches in theoretical analysis and computational experiments

Probabilistic alternatives for competitive analysis

In the last 20 years competitive analysis has become the main tool for analyzing the quality of online algorithms. Despite of this, competitive analysis has also been criticized: it sometimes cannot discriminate between algorithms that exhibit significantly different empirical behavior, or it even favors an algorithm that is worse from an empirical point of view. Therefore, there have been several approaches to circumvent these drawbacks. In this survey, we discuss probabilistic alternatives for competitive analysis