Lower bounds and reduction procedures for the bin packing problem
Discrete Applied Mathematics - Combinatorial Optimization
Future Generation Computer Systems
Network-aware migration control and scheduling of differentiated virtual machine workloads
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Communications of the ACM
Maximizing revenue in Grid markets using an economically enhanced resource manager
Concurrency and Computation: Practice & Experience - Economic Models and Algorithms for Grid Systems
Journal of Grid Computing
The tight bound of first fit decreasing bin-packing algorithm is FFD(I) ≤ 11/9OPT(I) + 6/9
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
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The on-demand capability of cloud computing allows consumers to purchase only the computing resources they require, as and when they need it. However, without a view of future demand, cloud providers' faces challenges in optimising the use of their infrastructure. In this paper, we propose a pricing method for cloud computing which allows providers to schedule virtual machines more efficiently through the use of provision point contracts (PPCs), commonly used for deal-of-the day websites such as Groupon. We show that the model can achieve a reduction of around 2% on the mean number of servers utilised. This may seem a modest percentage, but it can equate to freeing up thousands of physical servers in a single industrial-scale cloud computing data-centre. Additionally, our pricing model prevents discounts being offered where no increase in server efficiency is likely to be achieved. This suggests that the model can be implemented with little risk of it negatively affecting the efficiency of server provisioning. Finally, our results indicate that the cloud-service users who engage with the PPC method can achieve savings of over 20%.