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作者： Corentin Dupont University of Trento

学位级别：博士

With the raising concerns about the environment, the ICT equipments have been pointed out as a major and ever rising source of energy consumption and pollution. Among those ICT equipments, data centres play obviously a major role with the rise of the Cloud computing paradigm. In the recent years, researchers have focused on reducing the energy consumption of data centres. Furthermore, future environmentally friendly data centres are also expected to prioritize the usage of renewable energies over brown energies. However, managing the energy consumption within a data centre is challenging because data centres are complex facilities which supports a huge variety of hardware, computing styles and SLAs. Those may evolve through time as user requirements can change rapidly. Furthermore, differently from non-renewable energy sources, the availability of renewable energies is very volatile and time dependent: e. g. solar power is obtainable only during the day, and is subject to variations due to the meteorological conditions. The goal in this case is to shift the workload of running applications, according to the forecasted availability of the renewable energy. In this thesis we propose a flexible framework called Plug4Green able to reduce the energy consumption of a Cloud data centre. Plug4Green is based on the constraint programming paradigm, allowing it to take into account a great number of constraints regarding energy, hardware and SLAs in data centres. We also propose the concept of an energy adaptive software controller (EASC), able to augment the usage of renewable energies in data centres. The EASC supports two kind of applications: service-oriented and task-oriented applications; and two kind of computing environments: Infrastructure as a Service and Platform as a Service. We evaluated our solutions in several trials executed in the testbeds of Milan and Trento, Italy. Results show that Plug4Green was able to reduce the power consumption by 27% in the Milan tria

关键词： Data Centre Renewable Energy VM consolidation Job Scheduling constraint programming Platform as a Service Infrastructure as a Service

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constraint programming with Arbitrarily Large Integer Variables

Constraint Programming with Arbitrarily Large Integer Variab...

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7th International Conference Integration of AI and OR Techniques in constraint programming for Combinatorial Optimization Problems

作者： Moss, Anna Intel Corp Haifa Israel

ISBN: (纸本)9783642135194

In the standard constraint programming (CP) framework, an integer variable represents a signed integer and its domain is bounded by some minimal and maximal integer type values. In existing CP tools, the integer type is used to represent domain values, and hence domain bounds are inherently limited by the minimal and maximal signed integer values representable on a given platform. However, this implementation of integer variable fails to satisfy use cases where modeled integers can be arbitrarily large. An example of such CP application is the functional test generation where integer variables are used to model large architectural fields like memory addresses or operand data. In addition, in such applications, the set of standard arithmetic operations on an integer variable provided by the traditional CP framework does not represent the whole range of operations required for modeling. In this paper, we define a new type of integer variables with arbitrarily large domain size and a modified operation set. We show how this variable type can be realized on top of a traditional CP framework by means of global constraints over standard integer variables. The ideas presented in this paper can also be used to implement a native variable of the introduced type in a CP tool. The paper provides experimental results to demonstrate the effectiveness of the proposed approach.

关键词： constraint programming

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Autonomous Search in constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions

Autonomous Search in Constraint Satisfaction via Black Hole:...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Soto, Ricardo Crawford, Broderick Olivares, Rodrigo Niklander, Stefanie Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Univ Adolfo Ibanez Vina Del Mar Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile

ISBN: (纸本)9783319410005;9783319409993

Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.

关键词： Autonomous Search constraint programming constraint satisfaction Optimization Choice functions

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CP-based framework for software product lines engineering 8

CP-based framework for software product lines engineering

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8e Forum Jeunes Chercheurs du Congres INFORSID, INFORSID 2016 - 8th Young Researchers Forum during the INFORSID Congress, INFORSID 2016

作者： Villota-Gomez, Angela Centre de Recherche en Informatique Université Paris 1 Panthéon-Sorbonne 90 rue de Tolbiac Paris75013 France

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Improving IntSat by expressing disjunctions of bounds as linear constraints

Improving IntSat by expressing disjunctions of bounds as lin...

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作者： Asín, Roberto Bezem, Marc Nieuwenhuis, Robert Universidad Católica de la Santísima Concepción Concepción Chile Department of Informatics University of Bergen Bergen Norway Technical University of Catalonia Barcelona Spain

Conflict-Driven Clause Learning (CDCL) SAT solvers can automatically solve very large real-world problems. IntSat is a new technique extending CDCL to Integer Linear programming (ILP). For some conflicts, IntSat generates no strong enough ILP constraint, and the backjump has to be done based on the conflicting set of bounds. The techniques given in this article precisely analyze how and when that set can be translated into the required new ILP constraint. Moreover, this can be done efficiently. This obviously strengthens learning and significantly improves the performance of IntSat (as confirmed by our experiments). We also briefly discuss extensions and other applications. © 2016 - IOS Press and the authors. All rights reserved.

关键词： constraint programming

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Cost-efficient vacation planning with variable workforce demand and manpower

Cost-efficient vacation planning with variable workforce dem...

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作者： Teemu Kinnunen Aalto University

学位级别：硕士

Vacation planning can be a complicated process as multiple law and contract based rules must be respected, while at the same time the wishes of employees must be taken into account. The problem is especially difficult in transit industry, where demand and available manpower can vary and the products of transit industry have no shelf life. Also, temporary workers cannot be recruited as long training is need- ed. In this thesis, a constraint programming formulation for solving vacation planning problems is developed. constraint programming allows modeling each vacation as a single interval variable. This makes the approach more effective than modeling the problem as MILP, which would require a large amount of additional constraints and variables to model the problem, especially the consecutiveness of vacations. The objective of vacation planning is to find a solution, which has as large as possible minimum reserve of employees after all vacations are assigned. An additional ob- jective of minimizing maximum reserve is introduced to even out the distribution of reserve. The problem is solved to optimality with a commercial optimization solver with running times varying from a few seconds to three minutes. The results of two real world cases of a transportation company show that the model provides im- provement in solution quality and the planning time needed is reduced considerably. The issue of planning vacations has received little attention in literature. In many cases the vacations are planned by mutual agreement or a named employee assigns vacations by hand. This can result in a lot of manual labor after which the solution quality might still be poor. This thesis presents the first constraint programming based approach for planning employees' vacations. It allows the modeling of multi- ple constraints that are used to improve solution quality, and takes into account the preferences of the employees, the planning personnel and the company.

关键词： vacation planning scheduling constraint programming variable demand rail transportation interval variable

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A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第2期23卷 229-248页

作者： Zeballos, L. J. Quiroga, O. D. Henning, G. R. Univ Nacl Litoral CONICET INTEC RA-3000 Santa Fe Argentina Univ Nacl Litoral Fac Ingn Quim RA-3000 Santa Fe Argentina

This contribution presents an integrated constraint programming (CP) model to tackle the problems of tool allocation, machine loading, part routing. and scheduling in a flexible manufacturing system (FMS) The formulation, Which is able to take into account a variety of constraints found in industrial environments, as well as several objective functions. has been Successfully applied to the Solution of various case studies of different sizes. Though some of the problem instances have bigger sizes than the examples reported to date in literature, very good-quality Solutions were reached in quite reasonable CPU times. This good computational performance is due to two essential characteristics of the proposed model. The most significant one IS the use of two sets of two-index variables to capture manufacturing activities instead of having Just one set of four indexes. Thus, dimensionality is greatly reduced. The other relevant feature is the fact that the model relies oil an Indirect representation of tool needs by means of tool types. thus avoiding the consideration of tool copies. (C) 2009 Elsevier Ltd. All rights reserved

关键词： constraint programming Flexible manufacturing systems Scheduling Resource-constrained systems Computer-integrated manufacturing Multi-objective

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Efficient Optimization Strategies with constraint programming

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AICHE JOURNAL 2010年第2期56卷 387-404页

作者： Kotecha, Prakash R. Bhushan, Mani Gudi, Ravindra D. Indian Inst Technol Dept Chem Engn Bombay 400076 Maharashtra India

In this article, we propose novel strategies for the efficient determination of multiple solutions for a single objective, as well as globally optimal pareto fronts for multiobjective, optimization problems using constraint programming (CP). In particular, we propose strategies to determine, (i) all the multiple (globally) optimal solutions of a single objective optimization problem, (ii) K-best feasible solutions of a single objective optimization problem, and (iii) globally optimal pareto fronts (including non-convex pareto fronts) along with their multiple realizations for multiobjective optimization problems. It is shown here that the proposed strategy for determining K-best feasible solutions can be tuned as per the requirement of the user to determine either K-best distinct or nondistinct solutions. Similarly, the strategy for determining globally optimal pareto fronts can also be modified as per the requirement of the user to determine either only the distinct set of pareto points or determine the pareto points along with all their multiple realizations. All the proposed techniques involve appropriately modifying the search techniques and are shown to be computationally efficient in terms of not requiring successive re-solving of the problem to obtain the required solutions. This work therefore convincingly addresses the issue of efficiently determining globally optimal pareto fronts;in addition, it also guarantees the determination of all the possible realizations associated with each pareto point. The uncovering of such solutions can greatly aid the designer in making informed decisions. The proposed approaches are demonstrated via two case studies, which are nonlinear, combinatorial optimization problems, taken from the area of sensor network design. (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 387-404, 2010

关键词： optimization design multiobjective optimization constraint programming sensor network design multiple solutions pareto fronts

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A hybrid approach to protein folding problem integrating constraint programming with local search

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BMC BIOINFORMATICS 2010年第s1期11卷 S39-S39页

作者： Ullah, Abu Dayem Steinhoefel, Kathleen Kings Coll London Dept Comp Sci London WC2R 2LS England

Background: The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search. Results: Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach. Conclusion: constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.

关键词： Local Search Hybrid Approach constraint programming constraint Satisfaction Problem Local Search Method

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Reactive scheduling framework based on domain knowledge and constraint programming

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COMPUTERS & CHEMICAL ENGINEERING 2010年第12期34卷 2129-2148页

作者： Novas, Juan M. Henning, Gabriela P. Univ Nacl Litoral CONICET INTEC RA-3450 Guemes Santa Fe Argentina

Industrial environments frequently face disruptive events. This contribution presents a support framework, aimed at addressing the repair-based reactive scheduling problem. It is based on an explicit object-oriented domain representation and a constraint programming (CP) approach. When an unforeseen event occurs, the framework captures the in-progress agenda status, as well as the event effect on it. Based on this information, a rescheduling problem specification is developed. Tasks to be rearranged are recognized and the set of the most suitable rescheduling action types (e.g. shift-jump, reassign, freeze) is identified. Since a given specification may lead to several solutions, the second stage relies on a CP model to address the problem just defined. To create such model, action types are automatically transformed into constraints. Provided that good quality schedules can be reached in low CPU times, alternative solution scenarios focusing on stability and regular performance measures can be posed for each problem. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Reactive scheduling Batch plants Decision support systems Knowledge-based scheduling constraint programming

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