Adaptive and Resilient Revenue Maximizing Dynamic Resource Allocation and Pricing for Cloud-Enabled IoT Systems

TL;DR

This paper introduces an adaptive, threshold-based framework for cloud resource allocation and pricing that maximizes revenue while dynamically responding to real-time changes in resource availability and task arrivals.

Contribution

It presents a novel adaptive and resilient mechanism for dynamic VM allocation and pricing to optimize revenue in cloud-enabled IoT systems.

Findings

The proposed method effectively maximizes revenue under varying resource and demand conditions.

It demonstrates resilience to real-time fluctuations in VM availability and task complexity.

The framework outperforms traditional static allocation strategies.

Abstract

Cloud computing is becoming an essential component of modern computer and communication systems. The available resources at the cloud such as computing nodes, storage, databases, etc. are often packaged in the form of virtual machines (VMs) to be used by remotely located client applications for computational tasks. However, the cloud has a limited number of VMs available, which have to be efficiently utilized to generate higher productivity and subsequently generate maximum revenue. Client applications generate requests with computational tasks at random times with random complexity to be processed by the cloud. The cloud service provider (CSP) has to decide whether to allocate a VM to a task at hand or to wait for a higher complexity task in the future. We propose a threshold-based mechanism to optimally decide the allocation and pricing of VMs to sequentially arriving requests in order to maximize the revenue of the CSP over a finite time horizon. Moreover, we develop an adaptive and resilient framework based that can counter the effect of realtime changes in the number of available VMs at the cloud server, the frequency and nature of arriving tasks on the revenue of the CSP.