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

Using Clouds to Scale Grid Resources: An Economic Model

Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users’ demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users’ tasks by getting too many resources. Our simulator is based on the well-known GridSim software for grid simulation, which we expand to simulate infrastructure clouds. The results show how the proposed system can successfully adapt the amount of allocated resources to the demand, while at the same time ensuring that resources are fairly shared among users

An economic market for the brokering of time and budget guarantees

Grids offer best effort services to users. Service level agreements offer the opportunity to provide guarantees upon services offered, in such a way that it captures the users’ requirements, while also considering concerns of the service providers. This is achieved via a process of converging requirements and service cost values from both sides towards an agreement. This paper presents the intelligent scheduling for quality of service market-oriented mechanism for brokering guarantees upon completion time and cost for jobs submitted to a batch-oriented compute service. Web Services agreement (negotiation) is used along with the planning of schedules in determining pricing, ensuring that jobs become prioritised depending on their budget constraints. An evaluation is performed to demonstrate how market mechanisms can be used to achieve this, whilst also showing the effects that scheduling algorithms can have upon the market in terms of rescheduling. The evaluation is completed with a comparison of the broker’s capabilities in relation to the literature

The ISQoS Grid Broker for Temporal and Budget Guarantees

We introduce our Grid broker that uses SLAs in job submission with the aim of ensuring jobs are computed on time and on budget. We demonstrate our broker's ability to perform negotiation and to select preferentially higher priority jobs, in a tender market and discuss the architecture that makes this possible. We additionally show the effects of rescheduling and how careful consideration is required in order to avoid price instability. We therefore make recommendations upon how to maintain this stability, given rescheduling

A grid broker pricing mechanism for temporal and budget guarantees

We introduce a pricing mechanism for Grid computing, with the aim of showing how a broker can accept the most appropriate jobs to be computed on time and on budget. We analyse the mechanism’s performance via discrete event simulation, and illustrate its viability, the benefits of a new admission policy and to how slack relates to machine heterogeneity

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

On the Design of Mutually Aware Optimal Pricing and Load Balancing Strategies for Grid Computing Systems

Abstract-Managing resources and cleverly pricing them on computing systems is a challenging task. Resource sharing demands careful load balancing and often strives to achieve a win-win situation between resource providers and users. Toward this goal, we consider a joint treatment of load balancing and pricing. We do not assume static pricing to determine load balancing, or vice versa. Instead, we study the relationship between the price that a computing node is charged and the load and revenue that it receives. We find that there exists an optimal price which maximizes the revenue. We then consider a multiuser environment and explore how the load from a user can be balanced on processors with existing loads. Finally, we derive an optimal price that maximizes the revenue in the multi-user environment. We evaluate the performance of the proposed algorithms through simulations

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