Markets are Dead, Long Live Markets

Researchers have long proposed using economic approaches to resource allocation in computer systems. However, few of these proposals became operational, let alone commercial. Questions persist about the economic approach regarding its assumptions, value, applicability, and relevance to system design. The goal of this paper is to answer these questions. We find that market-based resource allocation is useful, and more importantly, that mechanism design and system design should be integrated to produce systems that are both economically and computationally efficient.Comment: Fix rotation of figure

Online Bidding Behaviour And Loss Aversion In Cloud Computing Markets: An Experiment

The last few years have witnessed a rapid growth in commoditization and consumption of IT services particularly due to the growing acceptance of cloud computing services. This in turn has led to newer forms of pricing the cloud services such as dynamic pricing. Infact, spot pricing, a dynamic pricing scheme has become mainstream. Cloud consumers using these schemes need to place their bids inorder to procure computing instances. Most of extant research on cloud dynamic pricing focuses on resource allocation problems and bidding strategies. We identify the need to look at behavioural biases of bidders to bring in a holistic perspective to cloud dynamic pricing discussions. In this paper, we conduct an experiment to elicit the impact of a behavioural bias namely, loss aversion, on a cloud consumer’s bidding behaviour. We discuss the social implications of our result to cloud consumers and the economic implications for cloud providers

Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids

Peer ReviewedPostprint (published version

XChange: A market-based approach to scalable dynamic multi-resource allocation in multicore architectures

Efficiently allocating shared on-chip resources across cores is critical to optimize execution in chip multiprocessors (CMPs). Techniques proposed in the literature often rely on global, centralized mechanisms that seek to maximize system through-put. Global optimization may hurt scalability: as more cores are integrated on a die, the search space grows exponentially, making it harder to achieve optimal or even acceptable oper-ating points at run-time without incurring significant over-heads. In this paper, we propose XChange, a novel CMP resource allocation mechanism that delivers scalable high through-put and fairness. Through XChange, the CMP functions as a market, where each shared resource is assigned a price which changes over time, and each core seeks to maximize its own utility, by bidding for these shared resources. Because each core works largely independently, the resource alloca-tion becomes a scalable, mostly distributed decision-making process. In addition, by distributing the resources propor-tionally to the bids, the system avoids unfairness, treating each core in an unbiased manner. Our evaluation shows that, using detailed simulations of a 64-core CMP configuration running a variety of multipro-grammed workloads, the proposed XChange mechanism im-proves system throughput (weighted speedup) by about 21% on average, and fairness (harmonic speedup) by about 24% on average, compared with equal-share on-chip cache and power distribution. On both metrics, that is at least about twice as much improvement over equal-share as a state-of-the-art centralized allocation scheme. Furthermore, our re-sults show that XChange is significantly more scalable than the state-of-the-art centralized allocation scheme we com-pare against. 1

Strategic and operational services for workload management in the cloud

In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior. In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework

Financial Market Models for the Grid

The existing network of computing devices around the world created by the Internet gives the possibility of establishing a global market for computing power, where anybody connected to this network can acquire computing power or sell his own spare computing resources in exchange for real money. This potential global market for computing power, which does not exist yet, is what we study in this thesis. Specifically, we study the market with both analytic and simulated models. This thesis predicts how a future global market for Grid computing will behave. We give arguments that such a large market, together with its potential indefinite growth, would not be able to scale if it were organized with a central server, and therefore we study a peer-to-peer market model in our simulations. We create a high-level model with the most relevant characteristics of the market, where buyers and sellers trade a single commodity. In our simulations, the parameters of the volume of contracts, proportion of satisfied agents and number of messages in the network achieve stable values in the long run. We also derive analytically the conditions that make the price get stable over time; we then implement these conditions in the simulation as local mechanisms of the market participants, which make the whole system achieve a stable price evolution. We are also confident that, as soon as the Grid market emerges, a parallel market of derivatives will be created as well. This market of derivatives will be important due to the non-storability nature of computing power. We develop a futures market for computing power based on Markov chains, where we initially model the behaviour of each participant with a particular Markov chain, and then we derive a global transition probability matrix that models the market as a whole. Furthermore, we analyse the performance of a futures trader operating in such a market, and we obtain an optimal trading strategy with the use of Markov Decision Processes. We finally develop a stochastic differential equation model that captures the essence of the spot price evolution of computing power observed in our market simulations. This model is based on a previously one proposed for the electricity market, and consists of the use of a Markov regime-switching mechanism in order to model the existence of spikes in the spot price. We then estimate the parameters in the model with the output data of our simulation program; the estimation is carried out both by maximum likelihood and the generalised method of moments

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

A framework for exchange-based trading of cloud computing commodities

Cloud computing is a paradigm for using IT services with characteristics such as flexible and scalable service usage, on-demand availability, and pay-as-you-go billing. Respective services are called cloud services and their nature usually motivates a differentiation in three layers: Infrastructure as a Service (IaaS) for cloud services offering functionality of hardware resources in a virtualised way, Platform as a Service (PaaS) for services acting as execution platforms, and Software as a Service (SaaS) representing applications provided in a cloud computing way. Any of these services is offered with the illusion of unlimited scalability. The infinity gained by this illusion implies the need for some kind of regulation mechanism to manage sup- ply and demand. Today’s static pricing mechanisms are limited in their capabilities to adapt to dynamic characteristics of cloud environments such as changing workloads. The solution is a dy- namic pricing approch compareable to today’s exchanges. This requires comparability of cloud services and the need of standardised access to avoid vendor lock-in. To achieve comparability, a classification for cloud services is introcuced, where classes of cloud services representing tradable goods are expressed by the minimum requirements for a certain class. The main result of this work is a framework for exchange-based trading of cloud com- puting commodities, which is composed of four core components derived from existing ex- change market places. An exchange component takes care of accepting orders from buyers and sellers and determines the price for the goods. A clearing component is responsible for the fi- nancial closing of a trade. The settlement component takes care of the delivery of the cloud service. A rating component monitors cloud services and logs service level agreement breaches to calculate provider ratings, especially for reliability, which is an important factor in cloud computing. The framework establishes a new basis for using cloud services and more advanced business models. Additionally, an overview of selected economic aspects including ideas for derivative financial instruments like futures, options, insurances, and more complex ones is pro- vided. A first version of the framework is currently being implemented and in use at Deutsche Bo ̈rse Cloud Exchange AG.Cloud Computing repra ̈sentiert eine neue Art von IT-Diensten mit bestimmten Eigenschaften wie Flexibilita ̈t, Skalierbarkeit, sta ̈ndige Verfu ̈gbarkeit und nutzungsbezogene (pay-as-you-go) Abrechnung. IT-Dienste, die diese Eigenschaften besitzen, werden als Cloud Dienste bezeichnet und lassen sich in drei Ebenen einteilen: Infrastructure as a Service (IaaS), womit virtuelle Hardware Ressourcen zur Verfu ̈gung gestellt werden, Platform as a Service (PaaS), das eine Ausfu ̈hrungsumgebung darstellt und Software as a Service (SaaS), welches das Anbieten von Applikationen als Cloud Dienst bezeichnet. Cloud Dienste werden mit der Illusion unendlicher Skalierbarkeit angeboten. Diese Unendlichkeit erfordert einen Mechanismus, der in der Lage ist, Angebot und Nachfrage zu regulieren. Derzeit eingesetzte Preisbildungsmechanismen sind in ihren Mo ̈glichkeiten beschra ̈nkt sich auf die Dynamik in Cloud Umgebungen, wie schnell wechselnde Bedarfe an Ressourcen, einzustellen. Eine mo ̈gliche Lo ̈sung stellt ein dynamischer Preisbildungsmechanismus dar, der auf dem Modell heutiger Bo ̈rsen beruht. Dieser erfordert die Standardisierung und Vergleichbarkeit von Cloud Diensten und eine standardisierte Art des Zugriffs. Um die Vergleichbarkeit von Cloud Diensten zu erreichen, werden diese in Klassen eingeteilt, die jeweils ein am Bo ̈rsenplatz handelbares Gut darstellen. Das Ergebnis dieser Arbeit ist ein Rahmenwerk zum bo ̈rsenbasierten Handel von Cloud Computing Commodities, welches aus vier Kernkomponenten besteht, die von existieren- den Bo ̈rsen und Rohstoffhandeslpla ̈tzen abgeleitet werden ko ̈nnen. Die Bo ̈rsenkomponente nimmt Kauf- und Verkaufsorders entgegen und bestimmt die aktuellen Preise der handelbaren Cloud Rohstoffe. Die Clearing Komponente stellt die finanzielle Abwicklung eines Gescha ̈ftes sicher, das Settlement ist fu ̈r die tatsa ̈chliche Lieferung zusta ̈ndig und die Rating Komponente u ̈berwacht die Cloud Dienste im Hinblick auf die Nichteinhaltung von Service Level Agree- ments und vor allem deren Zuverla ̈ssigkeit, die einen wichtigen Faktor im Cloud Computing darstellt. Das Rahmenwerk begru ̈ndet eine neue Basis fu ̈r die Cloudnutzung und ermo ̈glicht fort- geschrittenere Gescha ̈ftsmodelle. In der Arbeit wird weiters ein U ̈berblick u ̈ber o ̈konomis- che Aspekte wie Ideen zu derivaten Finanzinstrumenten auf Cloud Computing Commodities gegeben. Dieses Rahmenwerk wird derzeit an der Deutsche Bo ̈rse Cloud Exchange AG imple- mentiert und bereits in einer ersten Version eingesetzt