Negotiated economic grid brokering for quality of service

We demonstrate a Grid broker's job submission system and its selection process for finding the provider that is most likely to be able to complete work on time and on budget. We compare several traditional site selection mechanisms with an economic and Quality of Service (QoS) oriented approach. We show how a greater profit and QoS can be achieved if jobs are accepted by the most appropriate provider. We particularly focus upon the benefits of a negotiation process for QoS that enables our selection process to occur

Two Auction-Based Resource Allocation Environments: Design and Experience

Many computer systems have reached the point where the goal of resource allocation is no longer to maximize utilization; instead, when demand exceeds supply and not all needs can be met, one needs a policy to guide resource allocation decisions. One natural policy is to seek efficient usage, which allocates resources to the set of users who have the highest utility for the use of the resources. Researchers have frequently proposed market-based mechanisms to provide such a goal-oriented way to allocate resources among competing interests while maximizing overall utility of the users.Engineering and Applied Science

Learning based opportunistic admission control algorithm for mapreduce as a service, in

ABSTRACT Admission Control has been proven essential to avoid overloading of resources and for meeting user service demands in utility driven grid computing. Recent emergence of Cloud based services and the popularity of MapReduce paradigm in Cloud Computing environments make the problem of admission control intriguing. We propose a model that allows one to offer MapReduce jobs in the form of on-demand services. We present a learning based opportunistic algorithm that admits MapReduce jobs only if they are unlikely to cross the overload threshold set by the service provider. The algorithm meets deadlines negotiated by users in more than 80% of cases. We employ an automatically supervised Naive Bayes Classifier to label incoming jobs as admissible and non-admissible. From the list of jobs classified as admissible, we then pick a job that is expected to maximize service provider utility. An external supervision rule automatically evaluates decisions made by the algorithm in retrospect, and trains the classifier. We evaluate our algorithm by simulating a MapReduce cluster hosted in the Cloud that offers a set of MapReduce jobs as services to its users. Our results show that admission control is useful in minimizing losses due to overloading of resources, and by choosing jobs that maximize revenue of the service provider

Improving service level agreements for a job scheduler by visualizing simulations

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 49-51).Currently, job owners at Google do not have a good way to generate suitable Service Level Agreements (SLAs), which means that they cannot accurately communicate their intentions to the job scheduler. This means that the owner's job might not finish on time or at all. The solution described in this thesis helps users visualize design changes to SLAs and use simulation to explore the behavior resulting from the SLAs. I have designed and begun development of a visualization and simulation framework that allows users to see how the job scheduler's behavior might vary under different SLA parameters. This thesis describes the steps made towards designing and implementing a system that both helps users visualize SLAs and their reward functions, and allows users to create an SLA and gain an idea of the behavior of a job scheduler with the SLA as input.by Dina M. Betser.M.Eng

Market acceptance of cloud computing: An analysis of market structure, price models and service requirements

Diese Arbeit betrachtet menschliches Verhalten auf elektronischen Märkten. In diesem Zusammenhang wurden zwei Experimente durchgeführt, die das Auction Fever in Holländischen Auktionen sowie das menschliche Risikoverhalten im sehr hohen Wahrscheinlichkeitsbereich näher untersuchen. Im Experiment zum menschlichen Risikoverhalten wurde speziell die Bewertung hoher Gewinnwahrscheinlichkeiten durch die Teilnehmer untersucht. Hinsichtlich des Auction Fever wurde ein Referenzexperiment beschrieben, kritisiert und auf Basis dieser Kritik ein Folgeexperiment durchgeführt. Hierbei wurde beobachtet, dass Menschen ein vorher gesetztes Limit nicht konstant unterbieten sondern auch teilweise überbieten. Es wurden außerdem Einflussfaktoren für diesen emotionalen Effekt erarbeitet. --Verhandlungstheorie,Experiment