Cost and availability aware resource allocation and virtual function placement for CDNaaS provision

We address the fundamental tradeoff between deployment cost and service availability in the context of on-demand content delivery service provision over a telecom operator's network functions virtualization infrastructure. In particular, given a specific set of preferences and constraints with respect to deployment cost, availability and computing resource capacity, we provide polynomial-time heuristics for the problem of jointly deriving an appropriate assignment of computing resources to a set of virtual instances and the placement of the latter in a subset of the available physical hosts. We capture the conflicting criteria of service availability and deployment cost by proposing a multi-objective optimization problem formulation. Our algorithms are experimentally shown to outperform state-of-the-art solutions in terms of both execution time and optimality, while providing the system operator with the necessary flexibility to balance between conflicting objectives and reflect the relevant preferences of the customer in the produced solutions.This work was supported in part by the French FUI-18 DVD2C project and by the European Union’s Horizon 2020 research and innovation program under the 5G-Transformer project (grant no. 761536)

A systems biology understanding of protein constraints in the metabolism of budding yeasts

Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society

Adaptive Knobs for Resource Efficient Computing

Performance demands of emerging domains such as artificial intelligence, machine learning and vision, Internet-of-things etc., continue to grow. Meeting such requirements on modern multi/many core systems with higher power densities, fixed power and energy budgets, and thermal constraints exacerbates the run-time management challenge. This leaves an open problem on extracting the required performance within the power and energy limits, while also ensuring thermal safety. Existing architectural solutions including asymmetric and heterogeneous cores and custom acceleration improve performance-per-watt in specific design time and static scenarios. However, satisfying applications’ performance requirements under dynamic and unknown workload scenarios subject to varying system dynamics of power, temperature and energy requires intelligent run-time management. Adaptive strategies are necessary for maximizing resource efficiency, considering i) diverse requirements and characteristics of concurrent applications, ii) dynamic workload variation, iii) core-level heterogeneity and iv) power, thermal and energy constraints. This dissertation proposes such adaptive techniques for efficient run-time resource management to maximize performance within fixed budgets under unknown and dynamic workload scenarios. Resource management strategies proposed in this dissertation comprehensively consider application and workload characteristics and variable effect of power actuation on performance for pro-active and appropriate allocation decisions. Specific contributions include i) run-time mapping approach to improve power budgets for higher throughput, ii) thermal aware performance boosting for efficient utilization of power budget and higher performance, iii) approximation as a run-time knob exploiting accuracy performance trade-offs for maximizing performance under power caps at minimal loss of accuracy and iv) co-ordinated approximation for heterogeneous systems through joint actuation of dynamic approximation and power knobs for performance guarantees with minimal power consumption. The approaches presented in this dissertation focus on adapting existing mapping techniques, performance boosting strategies, software and dynamic approximations to meet the performance requirements, simultaneously considering system constraints. The proposed strategies are compared against relevant state-of-the-art run-time management frameworks to qualitatively evaluate their efficacy

A risk mitigation framework for construction / asset management of real estate and infrastructure projects

The increasing demand on residential, office, retail, and services buildings as well as hotels and recreation has been encouraging investors from both private and public sectors to develop new communities and cities to meet the mixed demand in one location. These projects are huge in size, include several diversified functions, and are usually implemented over many years. The real estate projects’ master schedules are usually initiated at an early stage of development. The decision to start investing in infrastructure systems, that can ultimately serve fully occupied community or city, is usually taken during the early development stage. This applies to all services such as water, electricity, sewage, telecom, natural gas, roads, urban landscape and cooling and heating. Following the feasibility phase and its generated implementation schedule, the construction of the infrastructure system starts together with a number of real estate projects of different portfolios (retail, residential, commercial,…etc.). The development of the remaining real estate projects continues parallel to customer occupancy of the completed projects. The occurrence of unforeseen risk events, post completing the construction of infrastructure system, may force decision makers to react by relaxing the implementation of the remaining unconstructed projects within their developed communities. This occurs through postponing the unconstructed project and keeping the original feasibility-based sequence of projects unchanged. Decision makers may also change the sequence of implementing their projects where they may prioritize either certain portfolio or location zone above the other, depending on changes in the market demand conditions. The change may adversely impact the original planned profit in the original feasibility. The profit may be generated from either real estate portfolios and/or their serving Infrastructure system. The negative impact may occur due to possible delayed occupancy of the completed real estate projects which in turn reduces the services demand. This finally results in underutilization of the early implemented Infrastructure system. This research aims at developing a dynamic decision support prototype system to quantify impacts of unforeseen risks on the profitability of real estate projects as well as its infrastructure system in the cases of changing projects’ implementation schedules. It is also aimed to support decision makers with scheduled portfolio mix that maximizes their Expected Gross Profit (EGP) of real estate projects and their infrastructure system. The provided schedules can be either based on location zone or portfolio type to meet certain marketing conditions or even to respect certain relations between neighbor projects’ implementation constraints. In order to achieve the research objectives, a Risk Impact Mitigation (RIM) decision support system is developed. RIM consists mainly of four models, Real Estate Scheduling Optimization Model RESOM, Sustainable Landscape Optimization Model SLOM, District Cooling Optimization Model DCOM and Water Simulation Optimization Model WSOM. Integrated with the three Infrastructure specialized models SLOM, DCOM, WSOM, RESOM provides EGP values for individual Infrastructure systems. The three infrastructure models provide the demand profile that relate to a RESOM generated implementation schedule. RESOM then uses these profiles for calculating the profits using the projects’ capital expenditure and financial expenses. The three models included in this research (SLOM, DCOM and WSOM) relate to the urban landscape, district cooling and water systems respectively. RIM is applied on a large scale real estate development in Egypt. The development was subjected to difficult political and financial circumstances that were not forecasted while preparing original feasibility studies. RIM is validated using a questionnaire process. The questionnaire is distributed to 31 experts of different academic and professional background. RIM’s models provided expected results for different real life cases tested by experts as part of the validation process. The validation process indicated that RIM’s results are consistent, in compliance with expected results and is extremely useful and novel in supporting real estate decision makers in mitigating risk impacts on their profits. The validation process also indicated promising benefits and potential need for developed commercial version for future application within the industry

Models and Methodologies to Address Emerging Needs in Network and Supply Chain Optimization

In this dissertation, we model three different security scenarios and propose solution methodologies to address each problem. Chapter 2 presents a large-scale optimization approach for solving a dynamic bi-level network interdiction problem (NIP) in which interdiction activities must be scheduled in order to minimize the cumulative maximum flow over a finite time horizon. A logic-based decomposition (LBD) approach is proposed that utilizes constraint programming to exploit the scheduling nature of this dynamic NIP. Chapter 3 considers a set of centers to which content (e.g., data or smuggled items), are assigned to ensure availability. An interdictor (e.g., border security officials) attempts to determine which centers (e.g., border\u27s checkpoints) to interdict in order to minimize the content availability. We present our efforts to model the problem as an Integer Programming formulation and show that the problem is NP-hard. We propose modeling improvements, which, in conjunction with a genetic algorithm is used to obtain quality solutions to the problem quickly. A comparison of the approaches is presented along with future research direction for the problem. Finally, Chapter 4 pursues a quantitative risk assessment of the complete poultry supply chain in China. This work is supported by collaborators in biological engineering, poultry science and numerous companies and universities throughout China. This effort considers contamination concerns from Salmonella for chicken broilers studied at the production steps in the supply chain as well as offering one of the first attempts to include the transportation, distribution, retail and consumption elements that complete the supply chain. Our quantitative risk assessment model makes use of preliminary data collected from a Chinese poultry company since Fall 2016

Molecular dynamics simulations of complex systems including HIV-1 protease

Advances in supercomputer architectures have resulted in a situation where many scienti�fic codes are used on systems whose performance characteristics di�ffer considerably from the platform they were developed and optimised for. This is particularly apparent in the realm of Grid computing, where new technologies such as MPIg allow researchers to connect geographically disparate resources together into virtual parallel machines. Finding ways to exploit these new resources efficiently is necessary both to extract the maximum bene�fit from them, and to provide the enticing possibility of enabling new science. In this thesis, an existing general purpose molecular dynamics code (LAMMPS) is extended to allow it to perform more efficiently in a geographically distributed Grid environment showing considerable performance gains as a result. The technique of replica exchange molecular dynamics is discussed along with its applicability to the Grid model and its bene�fits with respect to increasing sampling of configurational space. The dynamics of two sub-structures of the HIV-1 protease (known as the flaps) are investigated using replica exchange molecular dynamics in LAMMPS showing considerable movement that would have been difficult to investigate by traditional methods. To complement this, a study was carried out investigating the use of computational tools to calculate binding affinity between HIV-1 protease mutants and the drug lopinavir in comparison with results derived experimentally by other research groups. The results demonstrate some promise for computational methods in helping to determine the most eff�ective course of treatment for patients in the future

Collaborative Edge Computing in Mobile Internet of Things

The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportunities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision systems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool instead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abundance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative computing, collaborative security and collaborative privacy intelligence. The first framework, Opportunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spatial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third frame- work, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user’s privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration

Optimization of vehicle routing and scheduling with travel time variability - application in winter road maintenance

This study developed a mathematical model for optimizing vehicle routing and scheduling, which can be used to collect travel time information, and also to perform winter road maintenance operations (e.g., salting, plowing). The objective of this research was to minimize the total vehicle travel time to complete a given set of service tasks, subject to resource constraints (e.g., truck capacity, fleet size) and operational constraints (e.g., service time windows, service time limit). The nature of the problem is to design vehicle routes and schedules to perform the required service on predetermined road segments, which can be interpreted as an arc routing problem (ARP). By using a network transformation technique, an ARP can be transformed into a well-studied node routing problem (NRP). A set-partitioning (SP) approach was introduced to formulate the problem into an integer programming problem (I PP). To solve this problem, firstly, a number of feasible routes were generated, subject to resources and operational constraints. A genetic algorithm based heuristic was developed to improve the efficiency of generating feasible routes. Secondly, the corresponding travel time of each route was computed. Finally, the feasible routes were entered into the linear programming solver (CPL EX) to obtain final optimized results. The impact of travel time variability on vehicle routing and scheduling for transportation planning was also considered in this study. Usually in the concern of vehicle and pedestrian\u27s safety, federal, state governments and local agencies are more leaning towards using a conservative approach with constant travel time for the planning of winter roadway maintenance than an aggressive approach, which means that they would rather have a redundancy of plow trucks than a shortage. The proposed model and solution algorithm were validated with an empirical case study of 41 snow sections in the northwest area of New Jersey. Comprehensive analysis based on a deterministic travel time setting and a time-dependent travel time setting were both performed. The results show that a model that includes time dependent travel time produces better results than travel time being underestimated and being overestimated in transportation planning. In addition, a scenario-based analysis suggests that the current NJDOT operation based on given snow sector design, service routes and fleet size can be improved by the proposed model that considers time dependent travel time and the geometry of the road network to optimize vehicle routing and scheduling. In general, the benefit of better routing and scheduling design for snow plowing could be reflected in smaller minimum required fleet size and shorter total vehicle travel time. The depot location and number of service routes also have an impact on the final optimized results. This suggests that managers should consider the depot location, vehicle fleet sizing and the routing design problem simultaneously at the planning stage to minimize the total cost for snow plowing operations