Combinatorial optimisation for sustainable cloud computing

Enabled by both software and hardware advances, cloud computing has emerged as an efficient way to leverage economies of scale for building large computational infrastructures over a global network. While the cost of computation has dropped significantly for end users, the infrastructure supporting cloud computing systems has considerable economic and ecological costs. A key challenge for sustainable cloud computing systems in the near future is to maintain control over these costs. Amid the complexity of cloud computing systems, a cost analysis reveals a complex relationship between the infrastructure supporting actual computation on a physical level and how these physical assets are utilised. The central question tackled in this dissertation is how to best utilise these assets through efficient workload management policies. In recent years, workload consolidation has emerged as an effective approach to increase the efficiency of cloud systems. We propose to address aspects of this challenge by leveraging techniques from the realm of mathematical modeling and combinatorial optimisation. We introduce a novel combinatorial optimisation problem suitable for modeling core consolidation problems arising in workload management in data centres. This problem extends on the well-known bin packing problem. We develop competing models and optimisation techniques to solve this offline packing problem with state-of-the-art solvers. We then cast this newly defined combinatorial optimisation problem in an semi-online setting for which we propose an efficient assignment policy that is able to produce solutions for the semi-online problem in a competitive computational time. Stochastic aspects, which are often faced by cloud providers, are introduced in a richer model. We then show how predictive methods can help decision makers dealing with uncertainty in such dynamic and heterogeneous systems. We explore a similar but relaxed problem falling within the scope of proactive consolidation. This is a relaxed consolidation problem in which one decides which, when and where workload should be migrated to retain minimum energy cost. Finally, we discuss ongoing efforts to model and characterise the combinatorial hardness of bin packing instances, which in turn will be useful to study the various packing problems found in cloud computing environments

Thermal Aware Design Automation of the Electronic Control System for Autonomous Vehicles

The autonomous vehicle (AV) technology, due to its tremendous social and economical benefits, is transforming the entire world in the coming decades. However, significant technical challenges still need to be overcome until AVs can be safely, reliably, and massively deployed. Temperature plays a key role in the safety and reliability of an AV, not only because a vehicle is subjected to extreme operating temperatures but also because the increasing computations demand more powerful IC chips, which can lead to higher operating temperature and large thermal gradient. In particular, as the underpinning technology for AV, artificial intelligence (AI) requires substantially increased computation and memory resources, which have been growing exponentially through recent years and further exacerbated the thermal problems. High operating temperature and large thermal gradient can reduce the performance, degrade the reliability, and even cause an IC to fail catastrophically. We believe that dealing with thermal issues must be coupled closely in the design phase of the AVs’ electronic control system (ECS). To this end, first, we study how to map vehicle applications to ECS with heterogeneous architecture to satisfy peak temperature constraints and optimize latency and system-level reliability. We present a mathematical programming model to bound the peak temperature for the ECS. We also develop an approach based on the genetic algorithm to bound the peak temperature under varying execution time scenarios and optimize the system-level reliability of the ECS. We present several computationally efficient techniques for system-level mean-time-to-failure (MTTF) computation, which show several orders-of-magnitude speed-up over the state-of-the-art method. Second, we focus on studying the thermal impacts of AI techniques. Specifically, we study how the thermal impacts for the memory bit flipping can affect the prediction accuracy of a deep neural network (DNN). We develop a neuron-level analytical sensitivity estimation framework to quantify this impact and study its effectiveness with popular DNN architectures. Third, we study the problem of incorporating thermal impacts into mapping the parameters for DNN neurons to memory banks to improve prediction accuracy. Based on our developed sensitivity metric, we develop a bin-packing-based approach to map DNN neuron parameters to memory banks with different temperature profiles. We also study the problem of identifying the optimal temperature profiles for memory systems that can minimize the thermal impacts. We show that the thermal aware mapping of DNN neuron parameters on memory banks can significantly improve the prediction accuracy at a high-temperature range than the thermal ignorant for state-of-the-art DNNs

Utilisation d'algorithmes d'approximation en Programmation Par Contraintes

National audienceDans cet article, nous présenterons les travaux prélimi-naires menés sur l'utilisation d'algorithmes d'approxima-tion en Programmation Par Contraintes afin d'améliorer le calcul de bornes lors de la résolution de problèmes d'optimisation sous contraintes. L'objectif de nos travaux est d'étudier plus particulièrement quels algorithmes d'ap-proximation présentent suffisamment de flexibilité pour être utilisés en Programmation Par Contraintes, et comment les utiliser au sein d'un propagateur qui mettra à jour les bornes de la variable-objectif à chaque noeud de l'espace de recherche. Enfin l'idée sera d'appliquer cette approche à plusieurs familles de problèmes d'optimisation afin d'en extraire une généralisation

Processing of Preceramic Polymers for Direct-Ink Writing

Preceramic polymers are organosilicon polymers that, when pyrolyzed to above 1000°C, convert from a polymer to an amorphous ceramic. These polymers have been used for fiber spinning, polymer infiltration, and casting of materials but have recently gained interest for use as the feedstock material for additive manufacturing techniques. This work explores preceramic polymers being used for direct-ink writing (an additive manufacturing method) and many of the issues that occur with the polymers during curing and pyrolysis. The first chapter of this dissertation provides a review of preceramic polymers, while the second and third chapters focus on the development of inks made of preceramic polymers. The second chapter uses a polysilazane polymer mixed with up to 43.3 volume percent hexagonal boron nitride as the rheological modifier to enable printing. The pyrolyzed parts are tested with 3-point flexure and microhardness indentation to observe failure behavior. The third chapter uses a polycarbosilane polymer with zirconium diboride and silicon carbide fibers as constituents for printable inks. These polycarbosilane-based inks exhibit much more porosity and crack development during curing and pyrolysis than the inks in the second chapter. Defects are characterized with micro-computed tomography and scanning electron microscopy. From the measured defects, new suggestions for decreasing porosity and crack development are discussed. Building from the observations in the third chapter, the fourth chapter focuses on how the size of printed material influences the development of defects and overall strength. Two new inks, similar to those in chapter three, are used with the addition that one of the formulations utilizes fumed alumina as an added viscosity modifier. The final study investigates printed rods of varying diameters (0.45 to 1.7 millimeter) to observe the effects of off-gassing during curing on the development of porosity. Failure strength is measured with 3-point flexure and Weibull statistics are used to understand how specimen size and ink formulation affect final specimen strength. Overall, this dissertation shows that preceramic polymers are a viable option as a feedstock material for direct ink-writing and begins to quantify the degree to which part size and filler selection affect overall porosity development after curing and pyrolysis

Characterisation of discontinuous carbon fibre preforms for automotive applications

The high cost of raw materials, high labour costs and lengthy cycle times have limited the use of conventional ply-based composites in the automotive industry. This thesis seeks to identify the potential of using low cost discontinuous fibre composites (DFCs) for structural applications. Properties of DFCs are governed by the degree of homogeneity of the reinforcement and discontinuities at the fibre ends, which cause stress concentrations; thereby limiting the mechanical performance of the material. This work focuses on material characterisation of laminates moulded from discontinuous carbon fibre preforms manufactured by a robotic spray process. Through the culmination of this work, a suitable design methodology for automotive applications has been identified. Design procedures for aerospace have also been considered. An analytical model has been developed to determine the tensile stiffness and strength of a discontinuous carbon fibre preform composite. The model can be used within automotive and aerospace design methodologies to define material properties, but a number of other factors must be considered. Areal mass of the preform has been identified as the governing factor in achieving target compaction levels. Poor homogeneity in thin parts prevents the ability to achieve high volume fractions, which determines mechanical performance. It has been demonstrated that the matrix has a greater influence on the properties of DFCs when compared to continuous fibre composites. Toughened resins were particularly effective in improving tensile strength of DFCs that exhibited poor homogeneity. Damage tolerance of DFCs has been evaluated through open-hole and compression after impact testing. Higher property retention was observed compared to continuous fibre equivalents. Greater damage tolerance of DFCs could lead to increased weight-saving in structural applications. However, current safety factors based on conventional laminates may be too conservative and could lead to over-engineering thus limiting the potential of the material

Efficient Monte Carlo Based Methods for Variability Aware Analysis and Optimization of Digital Circuits.

Process variability is of increasing concern in modern nanometer-scale CMOS. The suitability of Monte Carlo based algorithms for efficient analysis and optimization of digital circuits under variability is explored in this work. Random sampling based Monte Carlo techniques incur high cost of computation, due to the large sample size required to achieve target accuracy. This motivates the need for intelligent sample selection techniques to reduce the number of samples. As these techniques depend on information about the system under analysis, there is a need to tailor the techniques to fit the specific application context. We propose efficient smart sampling based techniques for timing and leakage power consumption analysis of digital circuits. For the case of timing analysis, we show that the proposed method requires 23.8X fewer samples on average to achieve comparable accuracy as a random sampling approach, for benchmark circuits studied. It is further illustrated that the parallelism available in such techniques can be exploited using parallel machines, especially Graphics Processing Units. Here, we show that SH-QMC implemented on a Multi GPU is twice as fast as a single STA on a CPU for benchmark circuits considered. Next we study the possibility of using such information from statistical analysis to optimize digital circuits under variability, for example to achieve minimum area on silicon though gate sizing while meeting a timing constraint. Though several techniques to optimize circuits have been proposed in literature, it is not clear how much gains are obtained in these approaches specifically through utilization of statistical information. Therefore, an effective lower bound computation technique is proposed to enable efficient comparison of statistical design optimization techniques. It is shown that even techniques which use only limited statistical information can achieve results to within 10% of the proposed lower bound. We conclude that future optimization research should shift focus from use of more statistical information to achieving more efficiency and parallelism to obtain speed ups.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78936/1/tvvin_1.pd

Characterisation of discontinuous carbon fibre preforms for automotive applications

The high cost of raw materials, high labour costs and lengthy cycle times have limited the use of conventional ply-based composites in the automotive industry. This thesis seeks to identify the potential of using low cost discontinuous fibre composites (DFCs) for structural applications. Properties of DFCs are governed by the degree of homogeneity of the reinforcement and discontinuities at the fibre ends, which cause stress concentrations; thereby limiting the mechanical performance of the material. This work focuses on material characterisation of laminates moulded from discontinuous carbon fibre preforms manufactured by a robotic spray process. Through the culmination of this work, a suitable design methodology for automotive applications has been identified. Design procedures for aerospace have also been considered. An analytical model has been developed to determine the tensile stiffness and strength of a discontinuous carbon fibre preform composite. The model can be used within automotive and aerospace design methodologies to define material properties, but a number of other factors must be considered. Areal mass of the preform has been identified as the governing factor in achieving target compaction levels. Poor homogeneity in thin parts prevents the ability to achieve high volume fractions, which determines mechanical performance. It has been demonstrated that the matrix has a greater influence on the properties of DFCs when compared to continuous fibre composites. Toughened resins were particularly effective in improving tensile strength of DFCs that exhibited poor homogeneity. Damage tolerance of DFCs has been evaluated through open-hole and compression after impact testing. Higher property retention was observed compared to continuous fibre equivalents. Greater damage tolerance of DFCs could lead to increased weight-saving in structural applications. However, current safety factors based on conventional laminates may be too conservative and could lead to over-engineering thus limiting the potential of the material

Particle-scale numerical study on screening processes

The present study aimed to increase the understanding of the industrial screening process by using the discrete element method simulation (DEM) and machine learning modelling. Thus, the study focused on understanding the fundamentals of the complicated screening processes by investigating the process model with different controlling factors through particle-scale analysis. The particle-scale analysis was also linked to several macroscopic models and screening processes such as percolation of particles under vibration, the local passing of particles from the screen, choking of screening, non-spherical shaped particles contact detection and packing and machine learning modelling. The computational and theoretical analyses as well as machine leaning helped to clarify the use of particle-scale analysis and screening processes in several areas. The outcomes of this thesis include: (i) the percolation of particles under vibration and the machine learning modelling of percolation velocity to predict the size ratio threshold; (ii) a better understanding of screening process based on local passing of inclined and multi-deck screen and physics informed machine learning modelling to predict the particles passing; (iii) a logical model to predict the choking judgement of screen while combining the numerical results and machine learning and (iv) a novel contact force model for non-spherical particles by Fourier transformation and packing. The research in this thesis is useful for the fundamental understanding of the effect of particles’ contact force, operational conditions, particle properties, percolation and sieving on the screening process. Moreover, the novel process models based on artificial intelligence modelling, DEM simulation, and physics laws can help the design, control and optimisation of screening processes