Efficient Heuristics for Profit Optimization of Virtual Cloud Brokers


Efficient Heuristics for Profit Optimization of Virtual Cloud Brokers


This text introduces a replacement quite broker for cloud computing, whose business depends on outsourcing virtual machines (VMs) to its customers. More specifically, the broker owns a number of reserved instances of different VMs from several cloud suppliers and offers them to its customers in an on-demand basis, at cheaper prices than those of the cloud suppliers. The essence of the business resides in the massive distinction in value between on-demand and reserved VMs. We tend to define the Virtual Machine Designing Downside, an optimization problem to maximise the profit of the broker. We also propose a range of economical good heuristics (seven 2-section list scheduling heuristics and a reordering local search) to allocate a collection of VM requests from customers into the obtainable pre-booked ones, that maximize the broker earnings. We perform experimental evaluation to analyze the profit and quality of service metrics for the ensuing designing, including a group of 400 downside instances that account for realistic workloads and eventualities using real knowledge from cloud providers.

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