Dynamic Resource Allocation

Computer systems are subject to continuously increasing performance demands. However, energy consumption has become a critical issue, both for high-end large-scale parallel systems [12], as well as for portable devices [34]. In other words, more work needs to be done in less time, preferably with the same or smaller energy budget. Future performance and efficiency goals of computer systems can only be reached with large-scale, heterogeneous architectures [6]. Due to their distributed nature, control software is required to coordinate the parallel execution of applications on such platforms. Abstraction, arbitration and multi-objective optimization are only a subset of the tasks this software has to fulfill [6, 31]. The essential problem in all this is the allocation of platform resources to satisfy the needs of an application.\ud \ud This work considers the dynamic resource allocation problem, also known as the run-time mapping problem. This problem consists of task assignment to (processing) elements and communication routing through the interconnect between the elements. In mathematical terms, the combined problem is defined as the multi-resource quadratic assignment and routing problem (MRQARP). An integer linear programming formulation is provided, as well as complexity proofs on the N P-hardness of the problem.\ud \ud This work builds upon state-of-the-art work of Yagiura et al. [39, 40, 42] on metaheuristics for various generalizations of the generalized assignment problem. Specifically, we focus on the guided local search (GLS) approach for the multi-resource quadratic assignment problem (MRQAP). The quadratic assignment problem defines a cost relation between tasks and between elements. We generalize the multi-resource quadratic assignment problem with the addition of a capacitated interconnect and a communication topology between tasks. Numerical experiments show that the performance of the approach is comparable with commercial solvers. The footprint, the time versus quality trade-off and available metadata make guided local search a suitable candidate for run-time mapping

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Incorporating Memory and Learning Mechanisms Into Meta-RaPS

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

Design of Heuristic Algorithms for Hard Optimization

This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content

An examination of heuristics for the shelf space allocation problem.

Wong, Mei Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 115-120).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Our Contributions --- p.4Chapter 1.3 --- Framework of Shelf Space Allocation Problem --- p.4Chapter 1.4 --- Organization --- p.6Chapter 2. --- Literature Review --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Commercial Approaches --- p.7Chapter 2.3 --- Experimental Approaches --- p.8Chapter 2.4 --- Optimization Approaches --- p.11Chapter 2.4.1 --- Exact Approaches --- p.11Chapter 2.4.2 --- Heuristics Approaches --- p.16Chapter 2.5 --- Summary --- p.19Chapter 3. --- Overview of Shelf Space Allocation Problem --- p.21Chapter 3.1 --- Introduction --- p.21Chapter 3.2 --- Problem description --- p.22Chapter 3.2.1 --- Mathematical Model --- p.24Chapter 3.2.1.1 --- Notations --- p.25Chapter 3.2.1.2 --- Model --- p.25Chapter 3.2.1.3 --- Assumption --- p.26Chapter 3.2.1.4 --- Notations of final model --- p.27Chapter 3.2.1.5 --- Final model --- p.27Chapter 3.3 --- Original Heuristic --- p.28Chapter 3.3.1 --- Yang (2001) Method --- p.28Chapter 3.3.2 --- Remarks on Original Heuristic --- p.29Chapter 3.4 --- Original Heuristic with Yang's Adjustment --- p.30Chapter 3.4.1 --- Remarks on Yang's Adjustment --- p.32Chapter 3.5 --- New Neighborhood Movements --- p.33Chapter 3.5.1 --- New Adjustment Phase --- p.33Chapter 3.6 --- Network Flow Model --- p.35Chapter 3.6.1 --- ULSSAP --- p.35Chapter 3.6.2 --- Transforming shelf space allocation problem (SSAP) --- p.38Chapter 3.7 --- Tabu Search --- p.41Chapter 3.7.1 --- Tabu Search Algorithm --- p.42Chapter 3.7.1.1 --- Neighborhood search moves --- p.42Chapter 3.7.1.2 --- Candidate list strategy --- p.45Chapter 3.7.1.3 --- Tabu list --- p.46Chapter 3.7.1.4 --- Aspiration criteria.........................................: --- p.47Chapter 3.7.1.5 --- Intensification and Diversification --- p.48Chapter 3.7.1.6 --- Stopping criterion --- p.49Chapter 3.7.1.7 --- Probabilistic choice --- p.50Chapter 3.7.2 --- General Process of Tabu Search --- p.51Chapter 3.7.3 --- Application of Tabu Search to SSAP --- p.54Chapter 3.7.4 --- Analysis of Tabu Search --- p.58Chapter 4. --- Tabu Search with Path Relinking --- p.60Chapter 4.1 --- Introduction --- p.60Chapter 4.2 --- Foundations of path relinking --- p.62Chapter 4.3 --- Path Relinking Template --- p.65Chapter 4.4 --- Identification of Reference set --- p.69Chapter 4.5 --- Choosing initial and guiding solution --- p.73Chapter 4.6 --- Neighborhood structure --- p.74Chapter 4.7 --- Moving along paths --- p.81Chapter 4.8 --- Application of Tabu Search with Path Relinking --- p.87Chapter 4.9 --- Conclusion --- p.90Chapter 5. --- Computational Studies --- p.92Chapter 5.1 --- Introduction --- p.92Chapter 5.2 --- General Parameter Setting --- p.92Chapter 5.3 --- Parameter values for Tabu search --- p.94Chapter 5.4 --- Sensitivity test for Tabu search with Path Relinking --- p.95Chapter 5.4.1 --- Reference Set Strategies and Initial and Guiding Solution Criteria --- p.96Chapter 5.4.2 --- Frequency of Path Relinking --- p.99Chapter 5.4.3 --- Size of reference set --- p.101Chapter 5.4.4 --- Comparison with Tabu Search --- p.102Chapter 5.5 --- Comparison with other heuristics --- p.105Chapter 5.6 --- Conclusion --- p.109Chapter 6. --- Conclusion --- p.111Chapter 6.1 --- Summary of achievements --- p.112Chapter 6.2 --- Future Works --- p.113Bibliography --- p.11

Deliverable DJRA1.2. Solutions and protocols proposal for the network control, management and monitoring in a virtualized network context

This deliverable presents several research proposals for the FEDERICA network, in different subjects, such as monitoring, routing, signalling, resource discovery, and isolation. For each topic one or more possible solutions are elaborated, explaining the background, functioning and the implications of the proposed solutions.This deliverable goes further on the research aspects within FEDERICA. First of all the architecture of the control plane for the FEDERICA infrastructure will be defined. Several possibilities could be implemented, using the basic FEDERICA infrastructure as a starting point. The focus on this document is the intra-domain aspects of the control plane and their properties. Also some inter-domain aspects are addressed. The main objective of this deliverable is to lay great stress on creating and implementing the prototype/tool for the FEDERICA slice-oriented control system using the appropriate framework. This deliverable goes deeply into the definition of the containers between entities and their syntax, preparing this tool for the future implementation of any kind of algorithm related to the control plane, for both to apply UPB policies or to configure it by hand. We opt for an open solution despite the real time limitations that we could have (for instance, opening web services connexions or applying fast recovering mechanisms). The application being developed is the central element in the control plane, and additional features must be added to this application. This control plane, from the functionality point of view, is composed by several procedures that provide a reliable application and that include some mechanisms or algorithms to be able to discover and assign resources to the user. To achieve this, several topics must be researched in order to propose new protocols for the virtual infrastructure. The topics and necessary features covered in this document include resource discovery, resource allocation, signalling, routing, isolation and monitoring. All these topics must be researched in order to find a good solution for the FEDERICA network. Some of these algorithms have started to be analyzed and will be expanded in the next deliverable. Current standardization and existing solutions have been investigated in order to find a good solution for FEDERICA. Resource discovery is an important issue within the FEDERICA network, as manual resource discovery is no option, due to scalability requirement. Furthermore, no standardization exists, so knowledge must be obtained from related work. Ideally, the proposed solutions for these topics should not only be adequate specifically for this infrastructure, but could also be applied to other virtualized networks.Postprint (published version

Unveiling Hidden Values of Optimization Models with Metaheuristic Approach

Considering that the decision making process for constrained optimization problem is based on modeling, there is always room for alternative solutions because there is usually a gap between the model and the real problem it depicts. This study looks into the problem of finding such alternative solutions, the non-optimal solutions of interest for constrained optimization models, the SoI problem. SoI problems subsume finding feasible solutions of interest (FoIs) and infeasible solutions of interest (IoIs). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making come into play and for this purpose the SoIs can be very valuable. An evolutionary computation approach (in particular, a population-based metaheuristic) is proposed for solving the SoI problem and a systematic approach with a feasible-infeasible- two-population genetic algorithm is demonstrated. In this study, the effectiveness of the proposed approach on finding SoIs is demonstrated with generalized assignment problems and generalized quadratic assignment problems. Also, the applications of the proposed approach on the multi-objective optimization and robust-optimization issues are examined and illustrated with two-sided matching problems and flowshop scheduling problems respectively