Abstract
In a cloud computing environment, users using the pay-as-you-go billing model can relinquish their services at any point in time and pay accordingly. From the perspective of the Cloud Service Providers (CSPs), this is not beneficial as they may lose the opportunity to earn from the relinquished resources. Therefore, this paper tackles the resource assignment problem while considering users relinquishment and its impact on the net profit of CSPs. As a solution, we first compare different ways to predict user behavior (i.e. how likely a user will leave the system before its scheduled end time) and deduce a better prediction technique based on linear regression. Then, based on the RACE (Relinquishment-Aware Cloud Economics) model proposed in [1], we develop a relinquishment-aware resource optimization model to estimate the amount of resources to assign on the basis of predicted user behavior. Simulations performed with CloudSim show that cloud service providers can gain more by estimating the amount of resources using better prediction techniques rather than blindly assigning resources to users. They also show that the proposed prediction-based resource assignment scheme typically generates more profit for a lower or similar utilization.
Highlights
Cloud computing is known as the most widespread paradigm of distributed and parallel computing
Since users can relinquish their service at any point in time, it is essential to have ways in which cloud providers can predict the behavior of users
Different ways were proposed in the literature but they were mainly using some sort of average based on the historical behavior of users
Summary
Cloud computing is known as the most widespread paradigm of distributed and parallel computing. It has been observed that many users tend to purchase 10 times the amount of resources needed for the operation of their jobs, resulting in low server resource utilization [4] In this case, since CSPs reserve the resources for the requested duration by the users, they may lose opportunities to earn profit as users will only pay for the duration for which the service was used [1]. The history could be used to predict the user behavior and allocate the resources based on this information when the user returns for the service This way, CSPs have the potential to maximize their server utilization and, at the same time, minimize their loss. We: Propose a new technique based on linear regression to predict user behavior
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