With the high integration density and complexity of the modern multi-core platform, thermal problems become more and more significant for both the manufacture and system designer. Dynamic thermal management technique is one effective and efficient way to mitigate and avoid thermal emergences. In this paper, we propose a novel predictive dynamic thermal management algorithm to maximize the multi-core system throughput while satisfying the peak temperature constraints. Different from the conventional approaches, we found that it is not necessarily always a good choice to migrate a hot task to the core with the lowest temperature. Instead, in our algorithm, we develop a new temperature prediction technique and migration scheme that take the local temperature of a core as well as the impacts from neighboring cores into considerations. According to our experiment results on a practical Intel desktop platform, the proposed algorithm can significantly improve the throughput compared with the conventional approach.

An important question for secure all-optical networks (AONs) is how to incorporate security against attacks in the design and engineering of network architectures. In this work, we study the resilience of AON architectures under in-band crosstalk attacks. Crosstalk attack propagation depends on both optical devices at the physical layer and wavelength usage at the network layer. This motivates us to employ probabilistic graphical models to model attack propagation. At the physical layer, we use a directed probabilistic graph (Bayesian Belief Network) to model the attack propagation under static network traffic and a given source of attack. At the network layer, we use an undirected probabilistic graph to represent the probability distribution of active connections in the network. The cross-layer model is obtained by combining the physical- and the network-layer models into a factor graph representation. Graphical models provide an explicit representation of interactions between the physical- and the network layer. Furthermore, graphical models facilitate derivations of analytical results on resilience with respect to physical-layer vulnerability, physical topology, and network load. Specifically, we derive bounds on the network resilience for regular topologies. For ring, star, and mesh-torus networks with link-shortest path routing and all-to-all traffic, we show that the average network resilience loss grows linearly with respect to the network load when the network load is small, and polynomially with respect to the probability of attack propagation from node to node along the attacker's route. In addition, numerical results suggest that the sum-product algorithm based on the factor graph representation can be used for computationally efficient evaluation of network resilience for irregular/large topologies

We study the resilience of all-optical network (AON) architectures under in-band crosstalk attacks. We first develop a cross-layer model that captures attack propagation based on probabilistic graphical models. At the physical layer, we use a directed probabilistic graph (Bayesian belief network) to model the attack propagation under static network traffic and a given source of attack. At the network layer, we use an undirected probabilistic graph (random field) to represent the probability distribution of active connections in the network. The cross-layer model is obtained by combining the physicaland the network-layer models into a factor graph representation. We then derive bounds on the network resilience for regular topologies. We show that for ring, star, and mesh-torus networks with link-shortest path routing and all-to-all traffic, the average network resilience loss grows linearly with respect to the network load when the network load is light; grows polynomially with respect to the probability of attack propagation from node to node along the attacker's route. We then show that the sum-product algorithm can be used for computationally efficient evaluation of network resilience for irregular topologies

We investigate the necessary amount of network management information for light-path assessment to dynamically set up end-to-end light-paths across administrative domains in optical networks. Our focus is on the scalability of partial management information. We pose light-path assessment as a decision problem, and define the performance as the Bayes probability of an erroneous decision. We then characterize the scalability of management information as its growth rate with respect to the total resources of the network to achieve a desired performance. Scalability is achieved if the management information needed is only a negligible fraction of the total network resources. Specifically, we consider in this work one type of partial information that grows only logarithmically with the number of wavelengths supported per link. We derive an upper bound for the Bayes error in terms of the blocking probability when a new call is presented at the entrance of the network. We evaluate the upper bound using both independent and dependent models of wavelength usage for intra- and inter-domain calls. Our study shows that there exists a "threshold effect": The Bayes error decreases to zero exponentially with respect to the load when the load is either below or above a threshold value; and is nonnegligible when the load is in a small duration around the threshold. This suggests that the partial information considered can indeed provide the desired performance, and a small percentage of erroneous decisions can be traded off to achieve a large saving in the amount of management information.

Network management information for light-path assessment to dynamically set up end-to-end lightpaths across administrative domains is investigated. Our focus is on investigating what performance can be possibly achieved given partial management information, and whether a small loss in performance can trade off with a large saving in management information. The partial information we consider includes aggregated characterization of subnetworks, and local states from wavelength converters. We cast the light-path assessment as a decision problem, and define the performance as the probability of an erroneous decision. We apply the decision theory to show that the optimal performance using the partial information is the Bayes probability of error. We derive an upper bound of the Bayes error in terms of the blocking probability. We evaluate the upper bound using both independent and dependent models of wavelength usage. Our study shows that there exits a "threshold effect": the Bayes error decreases exponentially to 0 with respect to the load when the load is either below or above a threshold value; and is nonnegligible when the load is in a small duration around the threshold. This suggests that a small percentage of error decisions can trade off with a large saving in management information.