A novel bicriteria scheduling heuristics providing a guaranteed global system failure rate

International audienceWe propose a new framework for the (length,reliability) bicriteria static multiprocessor scheduling problem. Our first criterion remains the schedule's length, crucial to assess the system's real-time property. For our second criterion, we consider the global system failure rate, seen as if the whole system were a single task scheduled onto a single processor, instead of the usual reliability, because it does not depend on the schedule length like the reliability does (due to its computation in the classical exponential distribution model). Therefore, we control better the replication factor of each individual task of the dependency task graph given as a specification, with respect to the desired failure rate. To solve this bicriteria optimization problem, we take the failure rate as a constraint, and we minimize the schedule length. We are thus able to produce, for a given dependency task graph and multiprocessor architecture, a Pareto curve of non-dominated solutions, among which the user can choose the compromise that fits his requirements best. Compared to the other bicriteria (length,reliability) scheduling algorithms found in the literature, the algorithm we present here is the first able to improve significantly the reliability, by several orders of magnitude, making it suitable to safety critical systems

On the design of a cost-efficient resource management framework for low latency applications

The ability to offer low latency communications is one of the critical design requirements for the upcoming 5G era. The current practice for achieving low latency is to overprovision network resources (e.g., bandwidth and computing resources). However, this approach is not cost-efficient, and cannot be applied in large-scale. To solve this, more cost-efficient resource management is required to dynamically and efficiently exploit network resources to guarantee low latencies. The advent of network virtualization provides novel opportunities in achieving cost-efficient low latency communications. It decouples network resources from physical machines through virtualization, and groups resources in the form of virtual machines (VMs). By doing so, network resources can be flexibly increased at any network locations through VM auto-scaling to alleviate network delays due to lack of resources. At the same time, the operational cost can be largely reduced by shutting down low-utilized VMs (e.g., energy saving). Also, network virtualization enables the emerging concept of mobile edge-computing, whereby VMs can be utilized to host low latency applications at the network edge to shorten communication latency. Despite these advantages provided by virtualization, a key challenge is the optimal resource management of different physical and virtual resources for low latency communications. This thesis addresses the challenge by deploying a novel cost-efficient resource management framework that aims to solve the cost-efficient design of 1) low latency communication infrastructures; 2) dynamic resource management for low latency applications; and 3) fault-tolerant resource management. Compared to the current practices, the proposed framework achieves 80% of deployment cost reduction for the design of low latency communication infrastructures; continuously saves up to 33% of operational cost through dynamic resource management while always achieving low latencies; and succeeds in providing fault tolerance to low latency communications with a guaranteed operational cost

Resource Allocation in Networked and Distributed Environments

A central challenge in networked and distributed systems is resource management: how can we partition the available resources in the system across competing users, such that individual users are satisfied and certain system-wide objectives of interest are optimized? In this thesis, we deal with many such fundamental and practical resource allocation problems that arise in networked and distributed environments. We invoke two sophisticated paradigms -- linear programming and probabilistic methods -- and develop provably-good approximation algorithms for a diverse collection of applications. Our main contributions are as follows. Assignment problems: An assignment problem involves a collection of objects and locations, and a load value associated with each object-location pair. Our goal is to assign the objects to locations while minimizing various cost functions of the assignment. This setting models many applications in manufacturing, parallel processing, distributed storage, and wireless networks. We present a single algorithm for assignment which generalizes many classical assignment schemes known in the literature. Our scheme is derived through a fusion of linear algebra and randomization. In conjunction with other ideas, it leads to novel guarantees for multi-criteria parallel scheduling, broadcast scheduling, and social network modeling. Precedence constrained scheduling: We consider two precedence constrained scheduling problems, namely sweep scheduling and tree scheduling, which are inspired by emerging applications in high performance computing. Through a careful use of randomization, we devise the first approximation algorithms for these problems with near-optimal performance guarantees. Wireless communication: Wireless networks are prone to interference. This prohibits proximate network nodes from transmitting simultaneously, and introduces fundamental challenges in the design of wireless communication protocols. We develop fresh geometric insights for characterizing wireless interference. We combine our geometric analysis with linear programming and randomization, to derive near-optimal algorithms for latency minimization and throughput capacity estimation in wireless networks. In summary, the innovative use of linear programming and probabilistic techniques for resource allocation, and the novel ways of connecting them with application-specific ideas is the pivotal theme and the focal point of this thesis

A new hybrid meta-heuristic algorithm for solving single machine scheduling problems

A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution algorithms. While there is a plethora of research into various aspects of scheduling problems, little has been done in evaluating the performance of the Simulated Annealing algorithm for the fundamental problem, or using it in combination with other techniques. Speci cally, this has not been done for minimising total weighted earliness and tardiness, which is the optimisation objective of this work. If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible solution schedules. It is thus of de nite practical necessity to reduce the search space in order to nd an optimal or acceptable suboptimal solution in a shorter time, especially when scaling up the problem size. This is of particular importance in the application area of packet scheduling in wireless communications networks where the tolerance for computational delays is very low. The main contribution of this work is to investigate the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo methods before running the Simulated Annealing algorithm on the pruned search space can result in overall reduced running times. The search space is divided into a number of sections and Metropolis-Hastings Markov Chain Monte Carlo is performed over the sections in order to reduce the search space for Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time and number of sections of the pre-sampling algorithm, and the run time of Simulated Annealing for minimising the percentage deviation of the nal result from the optimal solution cost. Algorithm performance is determined both by computational complexity and the quality of the solution (i.e. the percentage deviation from the optimal). We nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation from the optimal, as compared to the basic Simulated Annealing algorithm on the full search space. More importantly, we are able to reduce the complexity of nding the optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis- Hastings iterations, r inner samples and m sections.MT 201

Mathematical models for the design and planning of transportation on demand in urban logistics networks

Falta palabras claveThe freight-transport industry has made enormous progress in the development and application of logistics techniques that has transformed its operation, giving raise to impressive productivity gains and improved responsiveness to its consumers. While the separation of passenger and freight traffic is a relatively new concept in historic terms, recent approaches point out that most freight-logistics techniques are transferable to the passenger-transport industry. In this sense, passenger logistics can be understood as the application of logistics techniques in urban contexts to the passenger-transport industry. The design of an urban logistic network integrates decisions about the emplacement, number and capacities of the facilities that will be located, the flows between them, demand patterns and cost structures that will validate the profitability of the process. This strategic decision settles conditions and constraints of latter tactical and operative decisions. In addition, different criteria are involved during the whole process so, in general terms, it is essential an exhaustive analysis, from the mathematical point of view, of the decision problem. The optimization models resulting from this analysis require techniques and mathematical algorithms in constant development and evolution. Such methods demand more and more a higher number of interrelated elements due to the increase of scale used in the current logistics and transportation problems. This PhD dissertation explores different topics related to Mathematical models for the design and planning of transportation on demand in urban logistics networks. The contributions are divided into six main chapters since and, in addition, Chapter 0 offers a basic background for the contents that are presented in the remaining six chapters. Chapter 1 deals with the Transit Network Timetabling and Scheduling Problem (TNTSP) in a public transit line. The TNTSP aims at determining optimal timetables for each line in a transit network by establishing departure and arrival times of each vehicle at each station. We assume that customers know departure times of line runs offered by the system. However, each user, traveling later of before their desired travel time, will give rise to an inconvenience cost, or a penalty cost if that user cannot be served according to the scheduled timetable. The provided formulation allocates each user to the best possible timetable considering capacity constraints. The problem is formulated using a p-median based approach and solved using a clustering technique. Computational results that show useful applications of this methodology are also included. Chapter 2 deals with the TNTSP in a public transit network integrating in the model the passengers' routings. The current models for planning timetables and vehicle schedules use the knowledge of passengers' routings from the results of a previous phase. However, the actual route a passenger will take strongly depends on the timetable, which is not yet known a priori. The provided formulation guarantees that each user is allocated to the best possible timetable ensuring capacity constraints. Chapter 3 deals with the rescheduling problem in a transit line that has suffered a eet size reduction. We present different modelling possibilities depending on the assumptions that need to be included in the modelization and we show that the problem can be solved rapidly by using a constrained maxcost- ow problem whose coe_cient matrix we prove is totally unimodular. We test our results in a testbed of random instances outperforming previous results in the literature. An experimental study, based on a line segment of the Madrid Regional Railway network, shows that the proposed approach provides optimal reassignment decisions within computation times compatible with real-time use. In Chapter 4 we discuss the multi-criteria p-facility median location problem on networks with positive and negative weights. We assume that the demand is located at the nodes and can be different for each criterion under consideration. The goal is to obtain the set of Pareto-optimal locations in the graph and the corresponding set of non-dominated objective values. To that end, we first characterize the linearity domains of the distance functions on the graph and compute the image of each linearity domain in the objective space. The lower envelope of a transformation of all these images then gives us the set of all non-dominated points in the objective space and its preimage corresponds to the set of all Pareto-optimal solutions on the graph. For the bicriteria 2-facility case we present a low order polynomial time algorithm. Also for the general case we propose an efficient algorithm, which is polynomial if the number of facilities and criteria is fixed. In Chapter 5, Ordered Weighted Average optimization problems are studied from a modeling point of view. Alternative integer programming formulations for such problems are presented and their respective domains studied and compared. In addition, their associated polyhedra are studied and some families of facets and new families of valid inequalities presented. The proposed formulations are particularized for two well-known combinatorial optimization problems, namely, shortest path and minimum cost perfect matching, and the results of computational experiments presented and analyzed. These results indicate that the new formulations reinforced with appropriate constraints can be effective for efficiently solving medium to large size instances. In Chapter 6, the multiobjective Minimum cost Spanning Tree Problem (MST) is studied from a modeling point of view. In particular, we use the ordered median objective function as an averaging operator to aggregate the vector of objective values of feasible solutions. This leads to the Ordered Weighted Average Spanning Tree Problem (OWASTP), which we study in this work. To solve the problem, we propose different integer programming formulations based in the most relevant MST formulations and in a new one. We analyze several enhancements for these formulations and we test their performance over a testbed of random instances. Finally we show that an appropriate choice will allow us to solve larger instances with more objectives than those previously solved in the literature.Premio Extraordinario de Doctorado U

A survey of strategies for communication networks to protect against large-scale natural disasters

Recent natural disasters have revealed that emergency networks presently cannot disseminate the necessary disaster information, making it difficult to deploy and coordinate relief operations. These disasters have reinforced the knowledge that telecommunication networks constitute a critical infrastructure of our society, and the urgency in establishing protection mechanisms against disaster-based disruptions

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities