Quality-Of-Control-Aware Scheduling of Communication in TSN-Based Fog Computing Platforms Using Constraint Programming

In this paper we are interested in real-time control applications that are implemented using Fog Computing Platforms consisting of interconnected heterogeneous Fog Nodes (FNs). Similar to previous research and ongoing standardization efforts, we assume that the communication between FNs is achieved via IEEE 802.1 Time Sensitive Networking (TSN). We model the control applications as a set of real-time streams, and we assume that the messages are transmitted using time-sensitive traffic that is scheduled using the Gate Control Lists (GCLs) in TSN. Given a network topology and a set of control applications, we are interested to synthesize the GCLs for messages such that the quality-of-control of applications is maximized and the deadlines of real-time messages are satisfied. We have proposed a Constraint Programming-based solution to this problem, and evaluated it on several test cases

An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem

Although Path-Relinking is an effective local search method for many combinatorial optimization problems, its application is not straightforward in solving the MAX-SAT, an optimization variant of the satisfiability problem (SAT) that has many real-world applications and has gained more and more attention in academy and industry. Indeed, it was not used in any recent competitive MAX-SAT algorithms in our knowledge. In this paper, we propose a new local search algorithm called IPBMR for the MAX-SAT, that remedies the drawbacks of the Path-Relinking method by using a careful combination of three components: a new strategy named Path-Breaking to avoid unpromising regions of the search space when generating trajectories between two elite solutions; a weak and a strong mutation strategies, together with restarts, to diversify the search; and stochastic path generating steps to avoid premature local optimum solutions. We then present experimental results to show that IPBMR outperforms two of the best state-of-the-art MAX-SAT solvers, and an empirical investigation to identify and explain the effect of the three components in IPBMR

Improving probability selection based weights for satisfiability problems

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively

Learning sequential and parallel runtime distributions for randomized algorithms

In cloud systems, computation time can be rented by the hour and for a given number of processors. Thus, accurate predictions of the behaviour of both sequential and parallel algorithms has become an important issue, in particular in the case of costly methods such as randomized combinatorial optimization tools. In this work, our objective is to use machine learning to predict performance of sequential and parallel local search algorithms. In addition to classical features of the instances used by other machine learning tools, we consider data on the sequential runtime distributions of a local search method. This allows us to predict with a high accuracy the parallel computation time of a large class of instances, by learning the behaviour of the sequential version of the algorithm on a small number of instances. Experiments with three solvers on SAT and TSP instances indicate that our method works well, with a correlation coefficient of up to 0.85 for SAT instances and up to 0.95 for TSP instances

A Study of Local Minimum Avoidance Heuristics for SAT

Stochastic local search for satisfiability (SAT) has successfully been applied to solve a wide range of problems. However, it still suffers from a major shortcoming, i.e. being trapped in local minima. In this study, we explore different heuristics to avoid local minima. The main idea is to proactively avoid local minima rather than reactively escape from them. This is worthwhile because it is time consuming to successfully escape from a local minimum in a deep and wide valley. In addition, revisiting an encountered local minimum several times makes it worse. Our new trap avoidance heuristics that operate in two phases: (i) learning of pseudo-conflict information at each local minimum, and (ii) using this information to avoid revisiting the same local minimum. We present a detailed empirical study of different strategies to collect pseudo-conflict information (using either static or dynamic heuristics) as well as to forget the outdated information (using naive or time window smoothing). We select a benchmark suite that includes all random and structured instances used in the 2011 SAT competition and three sets of hardware and software verification problems. Our results show that the new heuristics significantly outperform existing stochastic local search solvers (including Sparrow2011 - the best local search solver for random instances in the 2011 SAT competition) on all tested benchmarks