On-demand or Spot? Selling the cloud to risk-averse customers

In Amazon EC2, cloud resources are sold through a combination of an on-demand market, in which customers buy resources at a fixed price, and a spot market, in which customers bid for an uncertain supply of excess resources. Standard market environments suggest that an optimal design uses just one type of market. We show the prevalence of a dual market system can be explained by heterogeneous risk attitudes of customers. In our stylized model, we consider unit demand risk-averse bidders. We show the model admits a unique equilibrium, with higher revenue and higher welfare than using only spot markets. Furthermore, as risk aversion increases, the usage of the on-demand market increases. We conclude that risk attitudes are an important factor in cloud resource allocation and should be incorporated into models of cloud markets.Comment: Appeared at WINE 201

Simple Pricing Schemes for the Cloud

The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.Comment: To appear in the 13th Conference on Web and Internet Economics (WINE), 2017. A preliminary version was presented at the 12th Workshop on the Economics of Networks, Systems and Computation (NetEcon), 201

Threshold Policies with Tight Guarantees for Online Selection with Convex Costs

This paper provides threshold policies with tight guarantees for online selection with convex cost (OSCC). In OSCC, a seller wants to sell some asset to a sequence of buyers with the goal of maximizing her profit. The seller can produce additional units of the asset, but at non-decreasing marginal costs. At each time, a buyer arrives and offers a price. The seller must make an immediate and irrevocable decision in terms of whether to accept the offer and produce/sell one unit of the asset to this buyer. The goal is to develop an online algorithm that selects a subset of buyers to maximize the seller's profit, namely, the total selling revenue minus the total production cost. Our main result is the development of a class of simple threshold policies that are logistically simple and easy to implement, but have provable optimality guarantees among all deterministic algorithms. We also derive a lower bound on competitive ratios of randomized algorithms and prove that the competitive ratio of our threshold policy asymptotically converges to this lower bound when the total production output is sufficiently large. Our results generalize and unify various online search, pricing, and auction problems, and provide a new perspective on the impact of non-decreasing marginal costs on real-world online resource allocation problems

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Pricing the Cloud: An Auction Approach

Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Dynamic Pricing Strategy for Maximizing Cloud Revenue

The unexpected growth, flexibility and dynamism of information technology (IT) over the last decade has radically altered the civilization lifestyle and this boom continues as yet. Many nations have been competing to be forefront of this technological revolution, quite embracing the opportunities created by the advancements in this field in order to boost economy growth and to increase the accomplishments of everyday’s life. Cloud computing is one of the most promising achievement of these advancements. However, it faces many challenges and barriers like any new industry. Managing and maximizing such a very complex system business revenue is of paramount importance. The wealth of the cloud protfolio comes from the proceeds of three main services: Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS). The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has a significant portion of business values. Therefore many enterprises show frantic effort to capture the largest portion through the introducing of many different pricing models to satisfy not merely customers’ demands but essentially providers’ requirements. Indeed, one of the most challenging requirements is finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been presented as an elegant solution to overcome these situations aiming to gain more profits. However, previous studies on recent spot pricing schemes reveal an artificial pricing policy that does not comply with the dynamic nature of these phenomena. In this thesis, we investigate dynamic pricing of stagnant resources so as to maximize cloud revenue. To achieve this task, we reveal the necessities and objectives that underlie the importance of adopting cloud providers to dynamic price model, analyze adopted dynamic pricing strategy for real cloud enterprises and create dynamic pricing model which could be a strategic pricing model for IaaS cloud providers to increase the marginal profit and also to overcome technical barriers simultaneously. First, we formulate the maximum expected reward under discrete finite-horizon Markovian decisions and characterize model properties under optimum controlling conditions. The initial approach manages one class but multiple fares of virtual machines. For this purpose, the proposed approach leverages Markov decision processes, a number of properties under optimum controlling conditions that characterize a model’s behaviour, and approximate stochastic dynamic programming using linear programming to create a practical model. Second, our seminal work directs us to explore the most sensitive factors that drive price dynamism and to mitigate the high dimensionality of such a large-scale problem through conducting column generation. More specifically we employ a decomposition approach. Third, we observe that most previous work tackled one class of virtual machines merely. Therefore, we extend our study to cover multiple classes of virtual machines. Intuitively, dynamic price of multiple classes model is much more efficient from one side but practically is more challenging from another side. Consequently, our approach of dynamic pricing can scale up or down the price efficiently and effectively according to stagnant resources and load threshold aims to maximize the IaaS cloud revenue

Alternative Knowledges and the Future of Community Psychology: Provocations from an American Indian Healing Tradition

In the early years of this globalized century, alternative health knowledges and wellness traditions circulate faster and farther than ever before. To the degree that community psychologists seek collaboration with cultural minority and other marginalized populations in support of their collective wellbeing, such knowledges and traditions are likely to warrant attention, engagement, and support. My purpose in this article is to trace an epistemological quandary that community psychologists are ideally poised to consider at the interface of hegemonic and subjugated knowing with respect to advances in community wellbeing. To this end, I describe an American Indian knowledge tradition, its association with specific indigenous healing practices, its differentiation from therapeutic knowledge within disciplinary psychology, and the broader challenge posed by alternative health knowledges for community psychologists.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135430/1/ajcp12046.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135430/2/ajcp12046_am.pd

Market-Based Scheduling in Distributed Computing Systems

In verteilten Rechensystemen (bspw. im Cluster und Grid Computing) kann eine Knappheit der zur Verfügung stehenden Ressourcen auftreten. Hier haben Marktmechanismen das Potenzial, Ressourcenbedarf und -angebot durch geeignete Anreizmechanismen zu koordinieren und somit die ökonomische Effizienz des Gesamtsystems zu steigern. Diese Arbeit beschäftigt sich anhand vier spezifischer Anwendungsszenarien mit der Frage, wie Marktmechanismen für verteilte Rechensysteme ausgestaltet sein sollten

Privacy in resource allocation problems

Collaborative decision-making processes help parties optimize their operations, remain competitive in their markets, and improve their performances with environmental issues. However, those parties also want to keep their data private to meet their obligations regarding various regulations and not to disclose their strategic information to the competitors. In this thesis, we study collaborative capacity allocation among multiple parties and present that (near) optimal allocations can be realized while considering the parties' privacy concerns.We first attempt to solve the multi-party resource sharing problem by constructing a single model that is available to all parties. We propose an equivalent data-private model that meets the parties' data privacy requirements while ensuring optimal solutions for each party. We show that when the proposed model is solved, each party can only get its own optimal decisions and cannot observe others' solutions. We support our findings with a simulation study.The third and fourth chapters of this thesis focus on the problem from a different perspective in which we use a reformulation that can be used to distribute the problem among the involved parties. This decomposition lets us eliminate almost all the information-sharing requirements. In Chapter 3, together with the reformulated model, we benefit from a secure multi-party computation protocol that allows parties to disguise their shared information while attaining optimal allocation decisions. We conduct a simulation study on a planning problem and show our proposed algorithm in practice. We use the decomposition approach in Chapter 4 with a different privacy notion. We employ differential privacy as our privacy definition and design a differentially private algorithm for solving the multi-party resource sharing problem. Differential privacy brings in formal data privacy guarantees at the cost of deviating slightly from optimality. We provide bounds on this deviation and discuss the consequences of these theoretical results. We show the proposed algorithm on a planning problem and present insights about its efficiency.<br/