Tecniche per il controllo dinamico del consumo di potenza per piattaforme system-on-chip

Providing support for multimedia applications on low-power mobile devices remains a significant research challenge. This is primarily due to two reasons: • Portable mobile devices have modest sizes and weights, and therefore inadequate resources, low CPU processing power, reduced display capabilities, limited memory and battery lifetimes as compared to desktop and laptop systems. • On the other hand, multimedia applications tend to have distinctive QoS and processing requirementswhichmake themextremely resource-demanding. This innate conflict introduces key research challenges in the design of multimedia applications and device-level power optimization. Energy efficiency in this kind of platforms can be achieved only via a synergistic hardware and software approach. In fact, while System-on-Chips are more and more programmable thus providing functional flexibility, hardwareonly power reduction techniques cannot maintain consumption under acceptable bounds. It is well understood both in research and industry that system configuration andmanagement cannot be controlled efficiently only relying on low-level firmware and hardware drivers. In fact, at this level there is lack of information about user application activity and consequently about the impact of power management decision on QoS. Even though operating system support and integration is a requirement for effective performance and energy management, more effective and QoSsensitive power management is possible if power awareness and hardware configuration control strategies are tightly integratedwith domain-specificmiddleware services. The main objective of this PhD research has been the exploration and the integration of amiddleware-centric energymanagement with applications and operating-system. We choose to focus on the CPU-memory and the video subsystems, since they are the most power-hungry components of an embedded system. A second main objective has been the definition and implementation of software facilities (like toolkits, API, and run-time engines) in order to improve programmability and performance efficiency of such platforms. Enhancing energy efficiency and programmability ofmodernMulti-Processor System-on-Chips (MPSoCs) Consumer applications are characterized by tight time-to-market constraints and extreme cost sensitivity. The software that runs on modern embedded systems must be high performance, real time, and even more important low power. Although much progress has been made on these problems, much remains to be done. Multi-processor System-on-Chip (MPSoC) are increasingly popular platforms for high performance embedded applications. This leads to interesting challenges in software development since efficient software development is a major issue for MPSoc designers. An important step in deploying applications on multiprocessors is to allocate and schedule concurrent tasks to the processing and communication resources of the platform. The problem of allocating and scheduling precedenceconstrained tasks on processors in a distributed real-time system is NP-hard. There is a clear need for deployment technology that addresses thesemulti processing issues. This problem can be tackled by means of specific middleware which takes care of allocating and scheduling tasks on the different processing elements and which tries also to optimize the power consumption of the entire multiprocessor platform. This dissertation is an attempt to develop insight into efficient, flexible and optimalmethods for allocating and scheduling concurrent applications tomultiprocessor architectures. It is a well-known problem in literature: this kind of optimization problems are very complex even in much simplified variants, therefore most authors propose simplified models and heuristic approaches to solve it in reasonable time. Model simplification is often achieved by abstracting away platform implementation ”details”. As a result, optimization problems become more tractable, even reaching polynomial time complexity. Unfortunately, this approach creates an abstraction gap between the optimization model and the real HW-SW platform. The main issue with heuristic or, more in general, with incomplete search is that they introduce an optimality gap of unknown size. They provide very limited or no information on the distance between the best computed solution and the optimal one. The goal of this work is to address both abstraction and optimality gaps, formulating accurate models which accounts for a number of ”non-idealities” in real-life hardware platforms, developing novel mapping algorithms that deterministically find optimal solutions, and implementing software infrastructures required by developers to deploy applications for the targetMPSoC platforms. Energy Efficient LCDBacklightAutoregulation on Real-LifeMultimediaAp- plication Processor Despite the ever increasing advances in Liquid Crystal Display’s (LCD) technology, their power consumption is still one of the major limitations to the battery life of mobile appliances such as smart phones, portable media players, gaming and navigation devices. There is a clear trend towards the increase of LCD size to exploit the multimedia capabilities of portable devices that can receive and render high definition video and pictures. Multimedia applications running on these devices require LCD screen sizes of 2.2 to 3.5 inches andmore to display video sequences and pictures with the required quality. LCD power consumption is dependent on the backlight and pixel matrix driving circuits and is typically proportional to the panel area. As a result, the contribution is also likely to be considerable in future mobile appliances. To address this issue, companies are proposing low power technologies suitable for mobile applications supporting low power states and image control techniques. On the research side, several power saving schemes and algorithms can be found in literature. Some of them exploit software-only techniques to change the image content to reduce the power associated with the crystal polarization, some others are aimed at decreasing the backlight level while compensating the luminance reduction by compensating the user perceived quality degradation using pixel-by-pixel image processing algorithms. The major limitation of these techniques is that they rely on the CPU to perform pixel-based manipulations and their impact on CPU utilization and power consumption has not been assessed. This PhDdissertation shows an alternative approach that exploits in a smart and efficient way the hardware image processing unit almost integrated in every current multimedia application processors to implement a hardware assisted image compensation that allows dynamic scaling of the backlight with a negligible impact on QoS. The proposed approach overcomes CPU-intensive techniques by saving system power without requiring either a dedicated display technology or hardware modification. Thesis Overview The remainder of the thesis is organized as follows. The first part is focused on enhancing energy efficiency and programmability of modern Multi-Processor System-on-Chips (MPSoCs). Chapter 2 gives an overview about architectural trends in embedded systems, illustrating the principal features of new technologies and the key challenges still open. Chapter 3 presents a QoS-driven methodology for optimal allocation and frequency selection for MPSoCs. The methodology is based on functional simulation and full system power estimation. Chapter 4 targets allocation and scheduling of pipelined stream-oriented applications on top of distributed memory architectures with messaging support. We tackled the complexity of the problem by means of decomposition and no-good generation, and prove the increased computational efficiency of this approach with respect to traditional ones. Chapter 5 presents a cooperative framework to solve the allocation, scheduling and voltage/frequency selection problem to optimality for energyefficient MPSoCs, while in Chapter 6 applications with conditional task graph are taken into account. Finally Chapter 7 proposes a complete framework, called Cellflow, to help programmers in efficient software implementation on a real architecture, the Cell Broadband Engine processor. The second part is focused on energy efficient software techniques for LCD displays. Chapter 8 gives an overview about portable device display technologies, illustrating the principal features of LCD video systems and the key challenges still open. Chapter 9 shows several energy efficient software techniques present in literature, while Chapter 10 illustrates in details our method for saving significant power in an LCD panel. Finally, conclusions are drawn, reporting the main research contributions that have been discussed throughout this dissertation.

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The short-term scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500–1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient. We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

Aerial Coverage Path Planning applied to Mapping

In the last decade we have seen how small and light weight aerial platforms - aka, Mini Unmanned Aerial Vehicles (MUAV) - shipped with heterogeneous sensors have become a 'most wanted' Remote Sensing (RS) tool. Most of the off-the-shelf aerial systems found in the market provide way-point navigation. However, they do not rely on a tool that compute the aerial trajectories considering all the aspects that allow optimizing the aerial missions. One of the most demanded RS applications of MUAV is image surveying. The images acquired are typically used to build a high-resolution image, i.e., a mosaic of the workspace surface. Although, it may be applied to any other application where a sensor-based map must be computed. This thesis provides a study of this application and a set of solutions and methods to address this kind of aerial mission by using a fleet of MUAVs. In particular, a set of algorithms are proposed for map-based sampling, and aerial coverage path planning (ACPP). Regarding to map-based sampling, the approaches proposed consider workspaces with different shapes and surface characteristics. The workspace is sampled considering the sensor characteristics and a set of mission requirements. The algorithm applies different computational geometry approaches, providing a unique way to deal with workspaces with different shape and surface characteristics in order to be surveyed by one or more MUAVs. This feature introduces a previous optimization step before path planning. After that, the ACPP problem is theorized and a set of ACPP algorithms to compute the MUAVs trajectories are proposed. The problem addressed herein is the problem to coverage a wide area by using MUAVs with limited autonomy. Therefore, the mission must be accomplished in the shortest amount of time. The aerial survey is usually subject to a set of workspace restrictions, such as the take-off and landing positions as well as a safety distance between elements of the fleet. Moreover, it has to avoid forbidden zones to y. Three different algorithms have been studied to address this problem. The approaches studied are based on graph searching, heuristic and meta-heuristic approaches, e.g., mimic, evolutionary. Finally, an extended survey of field experiments applying the previous methods, as well as the materials and methods adopted in outdoor missions is presented. The reported outcomes demonstrate that the findings attained from this thesis improve ACPP mission for mapping purpose in an efficient and safe manner.

Aerial Coverage Path Planning applied to Mapping

In the last decade we have seen how small and light weight aerial platforms - aka, Mini Unmanned Aerial Vehicles (MUAV) - shipped with heterogeneous sensors have become a 'most wanted' Remote Sensing (RS) tool. Most of the off-the-shelf aerial systems found in the market provide way-point navigation. However, they do not rely on a tool that compute the aerial trajectories considering all the aspects that allow optimizing the aerial missions. One of the most demanded RS applications of MUAV is image surveying. The images acquired are typically used to build a high-resolution image, i.e., a mosaic of the workspace surface. Although, it may be applied to any other application where a sensor-based map must be computed. This thesis provides a study of this application and a set of solutions and methods to address this kind of aerial mission by using a fleet of MUAVs. In particular, a set of algorithms are proposed for map-based sampling, and aerial coverage path planning (ACPP). Regarding to map-based sampling, the approaches proposed consider workspaces with different shapes and surface characteristics. The workspace is sampled considering the sensor characteristics and a set of mission requirements. The algorithm applies different computational geometry approaches, providing a unique way to deal with workspaces with different shape and surface characteristics in order to be surveyed by one or more MUAVs. This feature introduces a previous optimization step before path planning. After that, the ACPP problem is theorized and a set of ACPP algorithms to compute the MUAVs trajectories are proposed. The problem addressed herein is the problem to coverage a wide area by using MUAVs with limited autonomy. Therefore, the mission must be accomplished in the shortest amount of time. The aerial survey is usually subject to a set of workspace restrictions, such as the take-off and landing positions as well as a safety distance between elements of the fleet. Moreover, it has to avoid forbidden zones to y. Three different algorithms have been studied to address this problem. The approaches studied are based on graph searching, heuristic and meta-heuristic approaches, e.g., mimic, evolutionary. Finally, an extended survey of field experiments applying the previous methods, as well as the materials and methods adopted in outdoor missions is presented. The reported outcomes demonstrate that the findings attained from this thesis improve ACPP mission for mapping purpose in an efficient and safe manner.

Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Traditional heuristic approaches to the Examination Timetabling Problem normally utilize a stochastic method during Optimization for the selection of the next examination to be considered for timetabling within the neighbourhood search process. This paper presents a technique whereby the stochastic method has been augmented with information from a weighted list gathered during the initial adaptive construction phase, with the purpose of intelligently directing examination selection. In addition, a Reinforcement Learning technique has been adapted to identify the most effective portions of the weighted list in terms of facilitating the greatest potential for overall solution improvement. The technique is tested against the 2007 International Timetabling Competition datasets with solutions generated within a time frame specified by the competition organizers. The results generated are better than those of the competition winner in seven of the twelve examinations, while being competitive for the remaining five examinations. This paper also shows experimentally how using reinforcement learning has improved upon our previous technique.

An Indirect Genetic Algorithm for Set Covering Problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

'An Indirect Genetic Algorithm for Set Covering Problems'

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

Unsupervised parameter estimation for one-class support vector machines

Although the hyper-plane based One-Class Support Vector Machine (OCSVM) and the hyper-spherical based Support Vector Data Description (SVDD) algorithms have been shown to be very effective in detecting outliers, their performance on noisy and unlabeled training data has not been widely studied. Moreover, only a few heuristic approaches have been proposed to set the different parameters of these methods in an unsupervised manner. In this paper, we propose two unsupervised methods for estimating the optimal parameter settings to train OCSVM and SVDD models, based on analysing the structure of the data. We show that our heuristic is substantially faster than existing parameter estimation approaches while its accuracy is comparable with supervised parameter learning methods, such as grid-search with crossvalidation on labeled data. In addition, our proposed approaches can be used to prepare a labeled data set for a OCSVM or a SVDD from unlabeled data.

A hybrid constructive heuristic and simulated annealing for railway crew scheduling

Railway crew scheduling problem is the process of allocating train services to the crew duties based on the published train timetable while satisfying operational and contractual requirements. The problem is restricted by many constraints and it belongs to the class of NP-hard. In this paper, we develop a mathematical model for railway crew scheduling with the aim of minimising the number of crew duties by reducing idle transition times. Duties are generated by arranging scheduled trips over a set of duties and sequentially ordering the set of trips within each of duties. The optimisation model includes the time period of relief opportunities within which a train crew can be relieved at any relief point. Existing models and algorithms usually only consider relieving a crew at the beginning of the interval of relief opportunities which may be impractical. This model involves a large number of decision variables and constraints, and therefore a hybrid constructive heuristic with the simulated annealing search algorithm is applied to yield an optimal or near-optimal schedule. The performance of the proposed algorithms is evaluated by applying computational experiments on randomly generated test instances. The results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time for large-sized problems.

Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

Nested heuristic methods for the location-routeing problem

The concept of 'nested methods' is adopted to solve the location-routeing problem. Unlike the sequential and iterative approaches, in this method we treat the routeing element as a sub-problem within the larger problem of location. Efficient techniques that take into account the above concept and which use a neighbourhood structure inspired from computational geometry are presented. A simple version of tabu search is also embedded into our methods to improve the solutions further. Computational testing is carried out on five sets of problems of 400 customers with five levels of depot fixed costs, and the results obtained are encouraging.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

The essence of a tacit intersection : a heuristic inquiry into nature-based leisure and personal transformation

There are a considerable number of programs and agencies that count on the existence of a unique relationship between nature and human development. In addition, there are significant bodies of literature dedicated to understanding developmentally focused nature-based experiences. This research project was designed to flirther the understanding of this phenomenon. Consequently, the purpose of this research endeavour was to discover the essence ofthe intersection ofpersonal transformation and nature-based leisure, culminating in a rich and detailed account of this otherwise tacit phenomenon. As such, this research built on the assumption of this beneficial intersection of nature and personal transformation and contributes to the understanding ofhow this context is supporting or generating of selfactualization and positive development. Heuristic methods were employed because heuristics is concerned with the quality and essence of an experience, not causal relationships (Moustakas, 1990). Heuristic inquiry begins with the primary researcher and her personal experience and knowledge of the phenomenon. This study also involved four other coresearchers who had also experienced this phenomenon intensely. Co-researchers were found through purposeful and snowball sampling. Rich narrative descriptions of their experiences were gathered through in-depth, semi-structured interviews, and artifact elicitation was employed as a means to get at co-researchers' tacit knowledge. Each coresearcher was interviewed twice (the first interview focused on personal transformation, the second on nature) for approximately four and a half hours in total. Transcripts were read repeatedly to discern patterns that emerged from the study of the narratives and were coded accordingly. Individual narratives were consolidated to create a composite narrative of the experience. Finally, a creative synthesis was developed to represent the essence of this tacit experience. In conclusion the essence of the intersection of nature-based leisure and personal transformation was found to lie in the convergence of the lived experience of authenticity. The physical environment of nature was perceived and experienced to be a space and context of authenticity, leisure experiences were experienced as an engagement of authenticity, and individuals themselves encountered a true or authentic self that emanated from within. The implications of these findings are many, offering suggestions, considerations and implications from reconsidered approaches to environmental education to support for selfdirected human development.