43,383 research outputs found
Dynamic remapping decisions in multi-phase parallel computations
The effectiveness of any given mapping of workload to processors in a parallel system is dependent on the stochastic behavior of the workload. Program behavior is often characterized by a sequence of phases, with phase changes occurring unpredictably. During a phase, the behavior is fairly stable, but may become quite different during the next phase. Thus a workload assignment generated for one phase may hinder performance during the next phase. We consider the problem of deciding whether to remap a paralled computation in the face of uncertainty in remapping's utility. Fundamentally, it is necessary to balance the expected remapping performance gain against the delay cost of remapping. This paper treats this problem formally by constructing a probabilistic model of a computation with at most two phases. We use stochastic dynamic programming to show that the remapping decision policy which minimizes the expected running time of the computation has an extremely simple structure: the optimal decision at any step is followed by comparing the probability of remapping gain against a threshold. This theoretical result stresses the importance of detecting a phase change, and assessing the possibility of gain from remapping. We also empirically study the sensitivity of optimal performance to imprecise decision threshold. Under a wide range of model parameter values, we find nearly optimal performance if remapping is chosen simply when the gain probability is high. These results strongly suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change; precise quantification of the decision model parameters is not necessary
Optimal Information-Theoretic Wireless Location Verification
We develop a new Location Verification System (LVS) focussed on network-based
Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we
develop is based on an information-theoretic framework which uses the received
signal strength (RSS) from a network of base-stations and the claimed position.
Based on this information we derive the optimal decision regarding the
verification of the user's location. Our algorithm is optimal in the sense of
maximizing the mutual information between its input and output data. Our
approach is based on the practical scenario in which a non-colluding malicious
user some distance from a highway optimally boosts his transmit power in an
attempt to fool the LVS that he is on the highway. We develop a practical
threat model for this attack scenario, and investigate in detail the
performance of the LVS in terms of its input/output mutual information. We show
how our LVS decision rule can be implemented straightforwardly with a
performance that delivers near-optimality under realistic threat conditions,
with information-theoretic optimality approached as the malicious user moves
further from the highway. The practical advantages our new
information-theoretic scheme delivers relative to more traditional Bayesian
verification frameworks are discussed.Comment: Corrected typos and introduced new threat model
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