8 research outputs found
Ensuring Trust in One Time Exchanges: Solving the QoS Problem
We describe a pricing structure for the provision of IT services that ensures
trust without requiring repeated interactions between service providers and
users. It does so by offering a pricing structure that elicits truthful
reporting of quality of service (QoS) by providers while making them
profitable. This mechanism also induces truth-telling on the part of users
reserving the service
Pricing the Cloud: An Adaptive Brokerage for Cloud Computing
Abstract—Using a multi-agent social simulation model to predict the behavior of cloud computing markets, Rogers & Cliff (R&C) demonstrated the existence of a profitable cloud brokerage capable of benefitting cloud providers and cloud consumers alike. Functionally similar to financial market brokers, the cloud broker matches provider supply with consumer demand. This is achieved through options, a type of derivatives contract that enables consumers to purchase the option, but not the obligation, of later purchasing the underlying asset—a cloud computing virtual machine instance—for an agreed fixed price. This model benefits all parties: experiencing more predictable demand, cloud providers can better optimize their workflow to minimize costs; cloud users access cheaper rates offered by brokers; and cloud brokers generate profit from charging fees. Here, we replicate and extend the simulation model of R&C using CReST—an opensource, discrete event, cloud data center simulation modeling platform developed at the University of Bristol. Sensitivity analysis reveals fragility in R&C’s model. We address this by introducing a novel method of autonomous adaptive thresholding (AAT) that enables brokers to adapt to market conditions without requiring a priori domain knowledge. Simulation results demonstrate AAT’s robustness, outperforming the fixed brokerage model of R&C under a variety of market conditions. We believe this could have practical significance in the real-world market for cloud computing. Keywords—CReST; simulation; cloud computing; brokerage I
Cloud Market Maker: An automated dynamic pricing marketplace for cloud users
© 2015 Elsevier B.V. Abstract Cloud providers commonly incur heavy upfront set up costs which remain almost constant whether they serve a single or many customers. In order to generate a return on this investment, a suitable pricing strategy is required by providers. Established industries such as the airlines employ dynamic pricing to maximize their revenues. In order to increase their resource utilization rates, cloud providers could also use dynamic pricing for their services. At present however most providers use static schemes for pricing their resources. This work presents a new dynamic pricing mechanism for cloud providers. Furthermore, at present no platform exists that provides a dynamic unified view of the different cloud offerings in real-time. Due to a rapidly changing landscape and a limited knowledge of the cloud marketplace, consumers can often end up choosing a cloud provider that is more expensive or does not give them what they really need. This is because some providers spend significantly on advertising their services online. In order to assist cloud customers in the selection of a suitable resource and cloud providers in implementing dynamic pricing, this work describes an automated dynamic pricing marketplace and a decision support system for cloud users. We present a multi-agent multi-auction based system through which such services are delivered. An evaluation has been carried out to determine how effectively the Cloud Market Maker selects the resource, dynamically adjusts the price for the cloud users and the suitability of dynamic pricing for the cloud environment
Cost-effective resource management for distributed computing
Current distributed computing and resource management infrastructures (e.g., Cluster and Grid) suffer
from a wide variety of problems related to resource management, which include scalability bottleneck,
resource allocation delay, limited quality-of-service (QoS) support, and lack of cost-aware and service
level agreement (SLA) mechanisms.
This thesis addresses these issues by presenting a cost-effective resource management solution
which introduces the possibility of managing geographically distributed resources in resource units that
are under the control of a Virtual Authority (VA). A VA is a collection of resources controlled, but not
necessarily owned, by a group of users or an authority representing a group of users. It leverages the
fact that different resources in disparate locations will have varying usage levels. By creating smaller
divisions of resources called VAs, users would be given the opportunity to choose between a variety of
cost models, and each VA could rent resources from resource providers when necessary, or could potentially
rent out its own resources when underloaded. The resource management is simplified since the
user and owner of a resource recognize only the VA because all permissions and charges are associated
directly with the VA. The VA is controlled by a ’rental’ policy which is supported by a pool of resources
that the system may rent from external resource providers. As far as scheduling is concerned, the VA is
independent from competitors and can instead concentrate on managing its own resources. As a result,
the VA offers scalable resource management with minimal infrastructure and operating costs.
We demonstrate the feasibility of the VA through both a practical implementation of the prototype
system and an illustration of its quantitative advantages through the use of extensive simulations. First,
the VA concept is demonstrated through a practical implementation of the prototype system. Further, we
perform a cost-benefit analysis of current distributed resource infrastructures to demonstrate the potential
cost benefit of such a VA system. We then propose a costing model for evaluating the cost effectiveness
of the VA approach by using an economic approach that captures revenues generated from applications
and expenses incurred from renting resources. Based on our costing methodology, we present rental
policies that can potentially offer effective mechanisms for running distributed and parallel applications
without a heavy upfront investment and without the cost of maintaining idle resources. By using real
workload trace data, we test the effectiveness of our proposed rental approaches.
Finally, we propose an extension to the VA framework that promotes long-term negotiations and
rentals based on service level agreements or long-term contracts. Based on the extended framework,
we present new SLA-aware policies and evaluate them using real workload traces to demonstrate their effectiveness in improving rental decisions