1,446 research outputs found
Pricing and Bidding Strategies for Cloud Computing Spot Instances
We consider a cloud service based on spot instances and explore bidding and pricing strategies aimed at optimizing users\u27 utility and provider\u27s revenue, respectively. Our focus is on jobs that are heterogeneous in both valuation and sensitivity to execution delay. Of particular interest is the impact of correlation in these two dimensions. We characterize optimal bidding and pricing strategies under some simplifying assumptions, and more importantly highlight the impact of correlation in determining the benefits of a spot service over an on-demand service. We also provide a preliminary assessment of the results\u27 robustness under more general assumptions
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
Reliable Provisioning of Spot Instances for Compute-intensive Applications
Cloud computing providers are now offering their unused resources for leasing
in the spot market, which has been considered the first step towards a
full-fledged market economy for computational resources. Spot instances are
virtual machines (VMs) available at lower prices than their standard on-demand
counterparts. These VMs will run for as long as the current price is lower than
the maximum bid price users are willing to pay per hour. Spot instances have
been increasingly used for executing compute-intensive applications. In spite
of an apparent economical advantage, due to an intermittent nature of biddable
resources, application execution times may be prolonged or they may not finish
at all. This paper proposes a resource allocation strategy that addresses the
problem of running compute-intensive jobs on a pool of intermittent virtual
machines, while also aiming to run applications in a fast and economical way.
To mitigate potential unavailability periods, a multifaceted fault-aware
resource provisioning policy is proposed. Our solution employs price and
runtime estimation mechanisms, as well as three fault tolerance techniques,
namely checkpointing, task duplication and migration. We evaluate our
strategies using trace-driven simulations, which take as input real price
variation traces, as well as an application trace from the Parallel Workload
Archive. Our results demonstrate the effectiveness of executing applications on
spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure
A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances
Cloud providers sell their idle capacity on markets through an auction-like
mechanism to increase their return on investment. The instances sold in this
way are called spot instances. In spite that spot instances are usually 90%
cheaper than on-demand instances, they can be terminated by provider when their
bidding prices are lower than market prices. Thus, they are largely used to
provision fault-tolerant applications only. In this paper, we explore how to
utilize spot instances to provision web applications, which are usually
considered availability-critical. The idea is to take advantage of differences
in price among various types of spot instances to reach both high availability
and significant cost saving. We first propose a fault-tolerant model for web
applications provisioned by spot instances. Based on that, we devise novel
auto-scaling polices for hourly billed cloud markets. We implemented the
proposed model and policies both on a simulation testbed for repeatable
validation and Amazon EC2. The experiments on the simulation testbed and the
real platform against the benchmarks show that the proposed approach can
greatly reduce resource cost and still achieve satisfactory Quality of Service
(QoS) in terms of response time and availability
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