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Michela Milano

Università di Bologna, DEIS, Faculty Member

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Papers by Michela Milano

MPOpt-Cell

Proceedings of the 8th ACM International Conference on Computing Frontiers - CF '11, 2011

We present MPOpt-Cell, an architecture-aware framework for high-productivity development and effi... more We present MPOpt-Cell, an architecture-aware framework for high-productivity development and efficient execution of stream applications on the CELL BE Processor. It enables developers to quickly build Synchronous Data Flow (SDF) applications using a simple and intuitive programming interface based on a set of compiler directives that capture the key abstractions of SDF. The compiler backend and system runtime efficiently manage

Precedence Constraint Posting for the RCPSP with Uncertain, Bounded Durations

PlanSIG2010, Dec 1, 2010

The Resource Constrained Project Scheduling Problem is an important problem in project management... more The Resource Constrained Project Scheduling Problem is an important problem in project management, manufacturing and resource optimization. We focus on a variant of RCPSP with time lags and uncertain activity durations. We adopt a Precedence Constraint Posting approach and add precedence constraints to the original project graph so that all resource conflicts are solved and a consistent assignment of start times can be computed for whatever combination of activity durations. We propose a novel method for computing ...

What-If Analysis Through Simulation-Optimization Hybrids

ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann, 2012

This paper proposes to improve traditional what-if analysis for policy making by a novel integrat... more This paper proposes to improve traditional what-if analysis for policy making by a novel integration of different components. When a simulator is available, a human expert, e.g., a policy maker, might understand the impact of her choices by running a simulator on a set of scenarios of interest. In many cases, when the number of scenarios is exponential in the number of choices, identifying the scenarios of interest might be particularly challenging. We claim that abandoning this generate and test approach could greatly enhance the decision process and the quality of political actions undertaken. In this paper we propose and experiment with one approach for combining simulation with a combinatorial optimization and decision making component. In addition, we propose two alternative approaches that can reasonably combine decision making with simulation in a coherent way and avoid the generate and test behaviour.

Automated DNA curvature profile reconstruction in atomic force microscope images

AI* IA Notizie, Dec 1, 2002

An automated algorithm is presented to determine the DNA molecule intrinsic curvature profiles an... more An automated algorithm is presented to determine the DNA molecule intrinsic curvature profiles and the molecular spatial orientations in Atomic Force Microscope images. The curvature is composed by static and dynamic contributions. The first one is the intrinsic curvature, vectorial function of the DNA nucletide sequence, while the second one is due to thermal noise. This algorithm allows to reconstruct the intrinsic curvature profile excluding the thermal contribution and detects fragment orientation on AFM image with a percentage ...

An Analysis of Regularized Approaches for Constrained Machine Learning

Trustworthy AI - Integrating Learning, Optimization and Reasoning

Embedding Decision Trees and Random Forests in Constraint Programming

Lecture Notes in Computer Science, 2015

In past papers, we have introduced Empirical Model Learning (EML) as a method to enable Combinato... more In past papers, we have introduced Empirical Model Learning (EML) as a method to enable Combinatorial Optimization on real world systems that are impervious to classical modeling approaches. The core idea in EML consists in embedding a Machine Learning model in a traditional combinatorial model. So far, the method has been demonstrated by using Neural Networks and Constraint Programming (CP). In this paper we add one more technique to the EML arsenal, by devising methods to embed Decision Trees (DTs) in CP. In particular, we propose three approaches: 1) a simple encoding based on meta-constraints; 2) a method using attribute discretization and a global table constraint; 3) an approach based on converting a DT into a Multi-valued Decision Diagram, which is then fed to an mdd constraint. We finally show how to embed in CP a Random Forest, a powerful type of ensemble classifier based on DTs. The proposed methods are compared in an experimental evaluation, highlighting their strengths and their weaknesses.

Swarm-based traffic lights policy selection

Proceedings of the fourth ACM international symposium on Development and analysis of intelligent vehicular networks and applications, 2014

ABSTRACT Improving the efficiency of urban vehicular mobility, also via the optimized management ... more ABSTRACT Improving the efficiency of urban vehicular mobility, also via the optimized management of the dynamic behavior of traffic lights with limited infrastructure investments and limited operational costs, is widely recognized as a crucial goal for smart cities, capable of relevant economic impacts in terms of travel time/cost reduction and better sustainability. Within this context, in the framework of the ongoing EU FP7 COLOMBO project, we are investigating, developing, and thoroughly evaluating innovative locality-based vehicular cooperation protocols for the determination of traffic characteristics in proximity of intersections, with no need for communication towards global data collection centers. One of the specific and original goals in COLOMBO is to achieve reasonable and sufficiently accurate traffic estimations with limited penetration rates of actively participating vehicles equipped with differentiated V2X capabilities (full-fledged V2X-enabled cars but also vehicles with only onboard smartphones). In this paper, we specifically focus on our recent research work of implementation and evaluation of our protocols on top of the iTETRIS simulation platform, a state-of-the-art integrated platform resulted from the synergic interworking of the ns-3 network and the SUMO vehicular mobility simulators. In particular, here we originally describe how to effectively and efficiently implement V2X protocols on iTETRIS, as well as lessons learned from our practical experience of deployment, evaluation, and protocol/iTETRIS fine-tuning. The reported simulation results (obtained through realistic simulations based on real traffic traces and the real road topology of the city of Bologna) show the feasibility of the proposed approach also with very limited penetration rates.

Towards a unified Evaluation of Traffic Light Algorithms

Within the COLOMBO project, modern traffic surveillance and traffic light control algorithms base... more Within the COLOMBO project, modern traffic surveillance and traffic light control algorithms based on data obtained from vehicular communications are developed. An evaluation of existing descriptions of traffic light evaluation show that both, common measurement definitions as well as standardised simulation scenarios are missing. We present a definition of measurement methodology based on earlier projects, namely iTETRIS and FESTA. Additionally, the scenarios under development are presented, including basic, synthetic ones, as well as some that represent real-world networks.

COLOMBO: Investigating the Potential of V2X for Traffic Management Purposes assuming low penetration Rates

Precedence Constraint Posting for Cyclic Scheduling Problems

Lecture Notes in Computer Science, 2011

Resource constrained cyclic scheduling problems consist in planning the execution over limited re... more Resource constrained cyclic scheduling problems consist in planning the execution over limited resources of a set of activities, to be indefinitely repeated. In such a context, the iteration period (i.e. the difference between the completion time of consecutive iterations) naturally replaces the makespan as a quality measure; exploiting inter-iteration overlapping is the primary method to obtain high quality schedules. Classical approaches for cyclic scheduling rely on the fact that, by fixing the iteration period, the problem admits an integer linear model. The optimal solution is then usually obtained iteratively, via linear or binary search on the possible iteration period values. In this paper we follow an alternative approach and provide a port of the key Precedence Constraint Posting ideas in a cyclic scheduling context; the value of the iteration period is not a-priori fixed, but results from conflict resolution decisions. A heuristic search method based on Iterative Flattening is used as a practical demonstrator; this was tested over instances from an industrial problem obtaining encouraging results.

Global Cyclic Cumulative Constraint

Lecture Notes in Computer Science, 2012

This paper proposes a global cumulative constraint for cyclic scheduling problems. In cyclic sche... more This paper proposes a global cumulative constraint for cyclic scheduling problems. In cyclic scheduling a project graph is periodically re-executed on a set of limited capacity resources. The objective is to find an assignment of start times to activities such that the feasible repetition period λ is minimized. Cyclic scheduling is an effective method to maximally exploit available resources by partially overlapping schedule repetitions. In our previous work [4], we have proposed a modular precedence constraint along with its filtering algorithm. The approach was based on the hypothesis that the end times of all activities should be assigned within the period: this allows the use of traditional resource constraints, but may introduce resource inefficiency. The adverse effects are particularly relevant for long activity durations and high resource availability. By relaxing this restriction, the problem becomes much more complicated and specific resource constrained filtering algorithms should be devised. Here, we introduce a global cumulative constraint based on modular arithmetic, that does not require the end times to be within the period. We show the advantages obtained for specific scenarios in terms of solution quality with respect to our previous approach, that was already superior with respect to state of the art techniques.

Neuron Constraints to Model Complex Real-World Problems

Lecture Notes in Computer Science, 2011

The benefits of combinatorial optimization techniques for the solution of real-world industrial p... more The benefits of combinatorial optimization techniques for the solution of real-world industrial problems are an acknowledged evidence; yet, the application of those approaches to many practical domains still encounters active resistance by practitioners, in large part due to the difficulty to come up with accurate declarative representations. We propose a simple and effective technique to bring hard-todescribe systems within the reach of Constraint Optimization methods; the goal is achieved by embedding into a combinatorial model a softcomputing paradigm, namely Neural Networks, properly trained before their insertion. The approach is flexible and easy to implement on top of available Constraint Solvers. To provide evidence for the viability of the proposed method, we tackle a thermal aware task allocation problem for a multi-core computing platform.

Constraint Based Scheduling to Deal with Uncertain Durations and Self-Timed Execution

Lecture Notes in Computer Science, 2010

In this paper we propose off-line and on-line extensions to the Resource Constrained Project Sche... more In this paper we propose off-line and on-line extensions to the Resource Constrained Project Scheduling Problem. The off-line extension is a variant of RCPSP with time lags and uncertain, bounded activity durations. In this context we improve over our previous work presented in [12] by proposing an incremental flow computation for finding minimal conflict sets and a set of filtering rules for cumulative constraint propagation. The on-line extension is based instead on considering an on-line semantics such as the Self-Timed Execution and take it into account in the scheduling process. Adding the on-line aspect to the problem makes the CSP framework no longer suitable. We have extended the CSP framework to take into account general search decisions and auxiliary unbound variables. An extensive set of experimental results show an improvement of up to two orders of magnitude over our previous approach.

Disregarding Duration Uncertainty in Partial Order Schedules? Yes, We Can!

Lecture Notes in Computer Science, 2014

In the context of Scheduling under uncertainty, Partial Order Schedules (POS) provide a convenien... more In the context of Scheduling under uncertainty, Partial Order Schedules (POS) provide a convenient way to build flexible solutions. A POS is obtained from a Project Graph by adding precedence constraints so that no resource conflict can arise, for any possible assignment of the activity durations. In this paper, we use a simulation approach to evaluate the expected makespan of a number of POSs, obtained by solving scheduling benchmarks via multiple approaches. Our evaluation leads us to the discovery of a striking correlation between the expected makespan and the makespan obtained by simply fixing all durations to their average. The strength of the correlation is such that it is possible to disregard completely the uncertainty during the schedule construction and yet obtain a very accurate estimation of the expected makespan. We provide a thorough empirical and theoretical analysis of this result, showing the existence of solid ground for finding a similarly strong relation on a broad class of scheduling problems of practical importance.

Maximum-throughput mapping of SDFGs on multi-core SoC platforms

Journal of Parallel and Distributed Computing, 2013

h i g h l i g h t s • We face Max-Throughput Mapping and Scheduling of streaming applications (SD... more h i g h l i g h t s • We face Max-Throughput Mapping and Scheduling of streaming applications (SDF) on MPSoC platforms. • We develop a Constraint-based solver relying on an incremental algorithm to narrow the search space. • The method is complete, but we devise heuristics to quickly guide search to high quality solutions. • We perform an extensive evaluation to assess the method effectiveness and scalability. • Adding incrementality speeds-up tree-search pruning by orders of magnitude.

Robust Scheduling of Task Graphs under Execution Time Uncertainty

IEEE Transactions on Computers, 2013

Effective multicore computing requires to make efficient usage of the computational resources on ... more Effective multicore computing requires to make efficient usage of the computational resources on a chip. Offline mapping and scheduling can be applied to improve the performance, but classical approaches require considerable a-priori knowledge of the target application. In a practical setting, precise information is often unavailable; one can then resort to approximate time and resource usage figures, but this usually requires to make conservative assumptions. The issue is further stressed if real-time guarantees must be provided. We tackle predictable and efficient non-preemptive scheduling of multi-task applications in the presence of duration uncertainty. Hard real-time guarantees are provided with limited idle time insertion, by exploiting a hybrid off-line/on-line technique known as Precedence Constraint Posting (PCP). Our approach does not require probability distributions to be specified, relying instead on simple and cheaper-toobtain information (bounds, average values). The method has been tested on synthetic applications/platforms and compared with an off-line optimized Fixed Priority Scheduling (FPS) approach and a pure on-line FIFO scheduler; the results are very promising, as the PCP schedules exhibit good stability and improved average execution time (14% on average, up to 30% versus FPS and up to 40% versus the FIFO scheduler).

Optimal methods for resource allocation and scheduling: a cross-disciplinary survey

Constraints, 2012

Classical scheduling formulations typically assume static resource requirements and focus on deci... more Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a far-from-trivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and OR techniques, often reporting impressive speed-ups compared to pure CP and OR methods. Motivated by the success of hybrid algorithms on resource assignment and scheduling, we provide a cross-disciplinary survey on such problems, including CP, OR and hybrid approaches. The main effort is to identify key modeling and solution techniques: they may then be applied in the construction of new hybrid algorithms, or they may provide ideas for novel filtering methods (possibly based on decomposition, or on alternative representations of the domain store). In detail, we take a constraint-based perspective and, according to the equation CP = model + propagation + search, we give an overview of state-of-art models, propagation/bounding techniques and search strategies.

CROSS cyclic resource-constrained scheduling solver

Artificial Intelligence, 2014

Cyclic scheduling problems consist in ordering a set of activities executed indefinitely over tim... more Cyclic scheduling problems consist in ordering a set of activities executed indefinitely over time in a periodic fashion, subject to precedence and resource constraints. This class of problems has many applications in manufacturing, embedded systems and compiler design, production and chemical systems. This paper proposes a Constraint Programming approach for cyclic scheduling problems, based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. We discuss two possible formulations. The first one (referred to as CROSS) models a pure cyclic scheduling problem and makes use of both our novel constraints. The second formulation (referred to as CROSS *) introduces a restrictive assumption to enable the use of classical resources constraints, but may incur a loss of solution quality. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. Our approach has been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances. The method proved to effective in finding high quality solutions in a very short amount of time.

A min-flow algorithm for Minimal Critical Set detection in Resource Constrained Project Scheduling

Artificial Intelligence, 2012

We propose a min-flow algorithm for detecting Minimal Critical Sets (MCS) in Resource Constrained... more We propose a min-flow algorithm for detecting Minimal Critical Sets (MCS) in Resource Constrained Project Scheduling Problems (RCPSP). The MCS detection is a fundamental step in the Precedence Constraint Posting method (PCP), one of the most successful approaches for the RCPSP. The proposed approach is considerably simpler compared to existing flow based MCS detection procedures and has better scalability compared to enumeration-and envelope-based ones, while still providing good quality Critical Sets. The method is suitable for problem variants with generalized precedence relations or uncertain/variable durations.

Mapping periodic applications on unary and cumulative resources

The 16th International Conference on Principles and Practice of Constraint Programming (CP’10), Sep 6, 2010

Abstract. Synchronous Data-Flow Graphs (SDFGs) are a very powerful modeling abstraction for high-... more Abstract. Synchronous Data-Flow Graphs (SDFGs) are a very powerful modeling abstraction for high-end multi-core embedded system applications. They intrinsically represent concurrency and compactly express periodicity. Their optimal platform allocation and scheduling subject to cumulative resource and real-time constraints is strongly NP-hard. State of the art approaches mainly focus on incomplete algorithms. We have developed a complete constraint-based framework based on graph modifications as search decisions ...