Capacity Allocation for Clouds with Parallel Processing, Batch Arrivals, and Heterogeneous Service Requirements

Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires multiple types of resources for processing. In this setting, providers decide whether to reserve portions of their capacity to individual job classes or to offer it in a flexible manner. Methodology/results: In collaboration with Huawei Cloud, a worldwide provider of cloud services, we propose a heuristic policy that allocates multiple types of resources to jobs and also satisfies their pre-specified service level agreements (SLAs). We model the system as a multi-class queueing network with parallel processing and multiple types of resources, where arrivals (i.e., virtual machines and containers) follow time-varying patterns and require at least one unit of each resource for processing. While virtual machines leave if they are not served immediately, containers can join a queue. We introduce a diffusion approximation of the offered load of such system and investigate its fidelity as compared to the observed data. Then, we develop a heuristic approach that leverages this approximation to determine capacity levels that satisfy probabilistic SLAs in the system with fully flexible servers. Managerial Implications: Using a data set of cloud computing requests over a representative 8-day period from Huawei Cloud, we show that our heuristic policy results in a 20% capacity reduction and better service quality as compared to a benchmark that reserves resources. In addition, we show that the system utilization induced by our policy is superior to the benchmark, i.e., it implies less idling of resources in most instances. Thus, our approach enables cloud operators to both reduce costs and achieve better performance

Staffing under Taylor's Law: A Unifying Framework for Bridging Square-root and Linear Safety Rules

Staffing rules serve as an essential management tool in service industries to attain target service levels. Traditionally, the square-root safety rule, based on the Poisson arrival assumption, has been commonly used. However, empirical findings suggest that arrival processes often exhibit an ``over-dispersion'' phenomenon, in which the variance of the arrival exceeds the mean. In this paper, we develop a new doubly stochastic Poisson process model to capture a significant dispersion scaling law, known as Taylor's law, showing that the variance is a power function of the mean. We further examine how over-dispersion affects staffing, providing a closed-form staffing formula to ensure a desired service level. Interestingly, the additional staffing level beyond the nominal load is a power function of the nominal load, with the power exponent lying between $1/2$ (the square-root safety rule) and $1$ (the linear safety rule), depending on the degree of over-dispersion. Simulation studies and a large-scale call center case study indicate that our staffing rule outperforms classical alternatives.Comment: 55 page

Services in Manufacturing Industries: Contributions to Quality and Competition

Motivated by the increasingly important role of services in manufacturing industries, this dissertation examines implications of this trend for quality management and competition by firms engaged in the production of joint product-service offerings. Broadly defined, we study the following research questions: How do the service contracts offered by manufacturers affect product quality? How does consumer demand respond to product quality and service attributes when manufacturers compete on services? How are consumer intentions influenced by product quality and service quality perceptions, and how does consumer heterogeneity influence this relationship? We empirically study these questions in the aerospace, automobile and consumer electronics industries, respectively. In the first study, we examine the impact of Performance-Based Contracting on product reliability in an application in the aerospace industry (aircraft engines), and show that the incentive alignment induced by performance-based contracts positively influences product reliability by different mechanisms. In the second essay, we formulate and estimate a structural model to analyze the impact of service competition and product quality in the U.S. automobile industry. We show that the impact of service attributes (warranty length, service quality) on consumer demand critically depends on the firm\u27s product quality. Finally, in the third essay (consumer electronics industry), we examine the joint influence of product quality and service quality perceptions on consumer intentions toward a brand, and show that consumer heterogeneity plays a significant role in defining this relationship. Collectively, our results suggest that the joint consideration of product and service is essential for the development of an effective competitive strategy and for the management of quality by manufacturing firms

Data-Driven Robust Optimization in Healthcare Applications

abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are ap- plied that (1) allow for the decision-maker to express a spectrum of pessimism against model uncertainties while (2) still utilizing incoming data for learning. Two main ap- plications are investigated with respect to these goals, the first being a percentile optimization technique with respect to a multi-class queueing system for application in hospital Emergency Departments. The second studies the use of robust forecasting techniques in improving developing countries’ vaccine supply chains via (1) an inno- vative outside of cold chain policy and (2) a district-managed approach to inventory control. Both of these research application areas utilize data-driven approaches that feature learning and pessimism-controlled robustness.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201