Optimal Posted Prices for Online Cloud Resource Allocation

We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool

Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Frontiers In Operations Research For Overcoming Barriers To Vehicle Electrification

Electric vehicles (EVs) hold many promises including diversification of the transportation energy feedstock and reduction of greenhouse gas and other emissions. However, achieving large-scale adoption of EVs presents a number of challenges resulting from a current lack of supporting infrastructure and difficulties in overcoming technological barriers. This dissertation addresses some of these challenges by contributing to the advancement of theories in the areas of network optimization and mechanism design. To increase the electric driving range of plug-in hybrid electric vehicles (PHEVs), we propose a powertrain energy management control system that exploits energy efficiency dif- ferences of the electric machine and the internal combustion engine during route planning. We introduce the Energy-Efficient Routing problem (EERP) for PHEVs, and formulate this problem as a new class of the shortest path problem. We prove that the EERP is NP-complete. We then propose two exact algorithms that find optimal solutions by exploiting the transitive structure inherent in the network. To tackle the intractability of the problem, we proposed a Fully Polynomial Time Approximation Scheme (FPTAS). From a theoretic perspective, the proposed two-phase approaches improve the state-of-the-art to optimally solving shortest path problems on general constrained multi-graph networks. These novel approaches are scalable and offer broad potential in many network optimization problems. In the context of vehicle routing, this is the first study to take into account energy efficiency difference of different operating modes of PHEVs during route planning, which is a high level powertrain energy management procedure. Another challenge for EV adoption is the inefficiency of current charging systems. In addition, high electricity consumption rates of EVs during charging make the load manage- ment of micro grids a challenge. We proposed an offline optimal mechanism for scheduling and pricing of electric vehicle charging considering incentives of both EV owners and utility companies. In the offline setting, information about future supply and demand is known to the scheduler. By considering uncertainty about future demand, we then designed a family of online mechanisms for real-time scheduling of EV charging. A fundamental problem with significant economic implications is how to price the charging units at different times under dynamic demand. We propose novel bidding based mechanisms for online scheduling and pricing of electric vehicle charging. The proposed preemption-aware charging mechanisms consider incentives of both EV drivers and grid operators. We also prove incentive-compatibility of the mechanisms, that is, truthful reporting is a dominant strategy for self-interested EV drivers. The proposed mechanisms demonstrate the benefits of electric grid load management, revenue maximization, and quick response, key attributes when providing online charging services

Large-Scale Distributed Coalition Formation

The CyberCraft project is an effort to construct a large scale Distributed Multi-Agent System (DMAS) to provide autonomous Cyberspace defense and mission assurance for the DoD. It employs a small but flexible agent structure that is dynamically reconfigurable to accommodate new tasks and policies. This document describes research into developing protocols and algorithms to ensure continued mission execution in a system of one million or more agents, focusing on protocols for coalition formation and Command and Control. It begins by building large-scale routing algorithms for a Hierarchical Peer to Peer structured overlay network, called Resource-Clustered Chord (RC-Chord). RC-Chord introduces the ability to efficiently locate agents by resources that agents possess. Combined with a task model defined for CyberCraft, this technology feeds into an algorithm that constructs task coalitions in a large-scale DMAS. Experiments reveal the flexibility and effectiveness of these concepts for achieving maximum work throughput in a simulated CyberCraft environment

Local energy trading for microgrids:Modeling human behavior, uncertainty and grid constraints

Electricity is one of the major drivers of today’s society and its rapid development, however, it also significantly contributes to the ongoing global warming. To reduce its impact, the energy transition aims to change the electricity production by switching from fossil fuels to more renewable and sustainable energy sources. In adition, the energy transition also addresses required changes in the heating and mobility sector.These changes have a significant impact on the electricity system, and an intelligent (active) management of the electricity production and consumption is required. Throughout the last years, many approaches have been proposed to manage production and consumption. At the core of this thesis, we focus on three different aspects of such approaches.1: We consider and analyze the impact of human behavior on the outcome of a local electricity market. Due to the direct participation of households, the question arises how human preferences and behavior affect the outcome of the market. Therefore, we translate a behavioral model from social science into a multi-objective optimization problem, which uses the personal preferences and motives of households and creates tailor-made bidcurves. We analyze the results on an household and the market level to derive implications for future maket design.2: The shift in electricity production leads to increased uncertainties in the future energy system. To deal with such uncertainties, we focus on a joint energy management of a neighborhood. We apply ideas and techniques from robust optimization to deal with the uncertainty, and mainly focus on an approach combining static robust optimization with a rolling horizon framework. Hereby, we generalize the rolling horizon by allowing more flexible starting time slots and compare and analyze two such generalized rolling horizon versions.3: The increased peaks due to the additional production and generation pose a serious burden to the current electricity grid. To ensure a safe operation, we focus on grid constraints in the context of a real-time control approach, which implements day-ahead and intraday market solutions. We use the planned solutions to guide the real-time decisions, and identify an interesting connection between day-ahead operations and their real-time realization