Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and ...
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Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and Scheduling in the general case are well-known NP-hard problems, forcing commercial schedulers to adopt greedy approaches to improve performance and QoS. Search-based approaches featuring the exploration of the solution space have seldom been employed in this setting, but mostly applied in off-line scenarios. In this paper, we present the first search-based approach to job allocation and scheduling for HPC machines, working in a production environment. The scheduler is based on constraint programming, an effective programming technique for optimization problems. The resulting scheduler is flexible, as it can be easily customized for dealing with heterogeneous resources, user-defined constraints and different metrics. We evaluate our solution both on virtual machines using synthetic workloads, and on the Eurora HPC with production workloads. Tests on a wide range of operating conditions show significant improvements in waitings and QoS in mid-tier HPC machines w.r.t state-of-the-art commercial rule-based dispatchers. Furthermore, we analyze the conditions under which our approach outperforms commercial approaches, to create a portfolio of scheduling algorithms that ensures robustness, flexibility and scalability.
This contribution introduces an efficient constraint programming (CP) model that copes with largescale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial env...
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This contribution introduces an efficient constraint programming (CP) model that copes with largescale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial environments, such as topology constraints, forbidden product-equipment assignments, sequence-dependent changeover tasks, dissimilar parallel units at each stage, limiting renewable resources and multiple-batch orders, among other relevant plant characteristics. Moreover, the contribution deals with various inter-stage storage and operational policies. In addition, multiple-batch orders can be handled by defining a campaign operating mode, and lower and upper bounds on the number of batches per campaign can be fixed. The proposed model has been extensively tested by means of several case studies having various problem sizes and characteristics. The results have shown that the model can efficiently solve medium and large-scale problems with multiple constraining features. The approach has also rendered good quality solutions for problems that consider multiple-batch orders under a campaign-based operational policy. (C) 2016 Elsevier Ltd. All rights reserved.
constraint satisfaction problem(CSP) can be widely applied in many areas. This paper investigates the maximum restricted path consistency algorithm. There is a large quantity of useless checks in the process of search...
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ISBN:
(纸本)9781510845541
constraint satisfaction problem(CSP) can be widely applied in many areas. This paper investigates the maximum restricted path consistency algorithm. There is a large quantity of useless checks in the process of searching for a PC-support with the most popular algorithm lmaxRPC3 rm. Since lmaxRPC3 rm has to examine the whole domain of a variable to ascertain whether a PC-support exists. The efficiency of the search can be improved by eliminating such useless checks. Firstly, this paper analyses the features which accounts for the existence of these ineffective checks. And then, this paper discusses some methods of solving these problems. Afterwards, a new data structure is put forward to strengthen residual supports and weaken the use of multidirectionality to narrow the range of search. A new algorithm, lmaxRPCls, which exploits the results above is proposed and it is proved that lmaxRPCls is correct and complete. It is also proved that the time complexity of this new algorithm is better than that of lmaxRPC3 rm. Experimental results show that lmaxRPCls performs better in most benchmark instances and it can improve the performance by 65% in the best case.
During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obta...
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During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
Systems of mobile Systems are intermittently connected networks that use store-carry-forward routing for data transfers. Independent systems collaborate and exchange data to achieve a common goal. Data transfers are o...
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Systems of mobile Systems are intermittently connected networks that use store-carry-forward routing for data transfers. Independent systems collaborate and exchange data to achieve a common goal. Data transfers are only possible between systems that are close enough to each other, when a so-called contact occurs. During a contact, a sending system can transmit to a receiving system a fixed amount of data held in its interna then assume it holds at a til buffer. We assume that the trajectories of component systems are predictable, and consequently that a sequence of contacts may be considered. This dissemination problem is aimed at finding a transfer plan such that a set of data can be transferred from a given subset of source systems to all the recipient systems. In this paper, we propose an original constraint-programming -based algorithm for solving this problem. Computational results show that this approach is an improvement on the integer-linear-programming-based approach that we proposed in a previous paper. (C) 2016 Elsevier Ltd. All rights reserved.
Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of acces...
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Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of accesses that occur frequently in the web log file. There are in the literature many efficient algorithms to solve SMP (e.g., GSP, SPADE, PrefixSpan, WAP-tree, LAPIN, PLWAP). Despite the effectiveness of these methods, they do not allow to express and to handle new constraints defined on patterns, new implementations are required. Recently, many approaches based on constraint programming (CP) was proposed to solve SPM in a declarative and generic way. Since no CP-based approach was applied for mining web access patterns, the authors introduce in this paper an efficient CP-based approach for solving the web log mining problem. They bring back the problem of web log mining to SPM within a CP environment which enables to handle various constraints. Experimental results on non-trivial web log mining problems show the effectiveness of the authors' CP-based mining approach.
We present a declarative framework for the compilation of constraint logic programs into variable-free relational theories which are then executed by rewriting. This translation provides an algebraic formulation of th...
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We present a declarative framework for the compilation of constraint logic programs into variable-free relational theories which are then executed by rewriting. This translation provides an algebraic formulation of the abstract syntax of logic programs. Logic variables, unification, and renaming apart are completely elided in favor of manipulation of variable-free relation expressions. In this setting, term rewriting not only provides an operational semantics for logic programs, but also a simple framework for reasoning about program execution. We prove the translation sound, and the rewriting system complete with respect to traditional SLD semantics.
We study the application of constraint programming (CP) to the planning and scheduling of multiple social robots interacting with residents in a retirement home. The robots autonomously organize and facilitate group a...
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ISBN:
(纸本)9783319449531;9783319449524
We study the application of constraint programming (CP) to the planning and scheduling of multiple social robots interacting with residents in a retirement home. The robots autonomously organize and facilitate group and individual activities among residents. The application is a multi-robot task allocation and scheduling problem in which task plans must be determined that integrate with resident schedules. The problem involves reasoning about disjoint time windows, inter-schedule task dependencies, user and robot travel times, as well as robot energy levels. We propose mixed-integer programming (MIP) and CP approaches for this problem and investigate methods for improving our initial CP approach using symmetry breaking, variable ordering heuristics, and large neighbourhood search. We introduce a relaxed CP model for determining provable bounds on solution quality. Experiments indicate substantial superiority of the initial CP approach over MIP, and subsequent significant improvements in the CP approach through our manipulations. This work is one of the few, of which we are aware, that applies CP to multi-robot task allocation and scheduling problems. Our results demonstrate the promise of CP scheduling technology as a general optimization infrastructure for such problems.
ITIL is a large collection of best practices, tools and methods used in the management and handling of IT services. It's composed of five books related to the most important IT management fields. In this paper, we...
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ISBN:
(纸本)9781467391870
ITIL is a large collection of best practices, tools and methods used in the management and handling of IT services. It's composed of five books related to the most important IT management fields. In this paper, we will place the emphasis on the Service Operation field in ITIL and more precisely, on the incident management process used for managing the life cycle of IT incidents. The main idea is to find a solution for an automated optimal planning of interventions in the incident management process. Indeed, despite the number of software solutions for incident management process, intervention planning is still a manual task due to the high complexity of its automation. In this paper, we propose two solutions for automated optimal planning of interventions in the incident management. The first is inspired by the vehicle routing problems and the second is inspired by the constraint-based problems. Eventually, we will compare between the results of these two solution.
Parallel constraint programming (CP) solvers typically split the search space in disjoint subspaces, and run solvers independently on these. This may induce significant overhead when solving optimization problems. Par...
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ISBN:
(数字)9783319339542
ISBN:
(纸本)9783319339542;9783319339535
Parallel constraint programming (CP) solvers typically split the search space in disjoint subspaces, and run solvers independently on these. This may induce significant overhead when solving optimization problems. Parallel Boolean Satisfiability (SAT) solvers typically run a portfolio of solvers, all solving the same problem but sharing some limited learnt clause information. In this paper we consider parallelizing a lazy clause generation (LCG) constraint programming solver, which is a constraint programming solver with learning. Since it is both a kind of CP solver and a kind of SAT solver it is not clear which approach to parallelization is likely to be most effective. We give examples of very different kinds of optimization problems we wish to parallelize and show that a hybrid approach to parallelization can provide a robust and high performing parallel LCG solver.
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