A virtual data center (VDC) comprises multiple virtual machines (VMs) with communication dependencies represented as virtual links (VLs). These virtual components, i.e., VMs and VLs, often experience fluctuating demands across different resource types. In this article, we focus on addressing the issue of dynamic resource expansion that leads to the relocation of solution components (SCs), where a SC comprises a VM and its attached VLs, with either the VM and/or at least one of the VLs facing resource expansion. This is challenging because of the complexity involved in frequently relocating multiple dependent virtual components across the substrate network. This article presents a model calledReMatchthat aims at building an efficient remapping plan with reduced remapping cost and improved resource utilization for service providers (SPs) in polynomial time. The overall relocation problem is formulated as a one-to-many matching game with heterogeneous VM demands. Owing to the inapplicability of the classical deferred acceptance algorithm (DAA) and revised DA (RDA), we propose a modified version of the RDA (MRDA) to obtain a weakly stable assignment. Thorough simulation and analysis show thatReMatchoutperforms the baseline algorithms considering multiple evaluation metrics.

A virtual data center (VDC) mostly encapsulates multiple virtual machines (VMs) with communication dependencies. These VDC requests are dynamic in nature and often experience fluctuating demands across different resources. In this paper, we propose a dynamic resource reconfiguration strategy called VMatch that generates an efficient relocation/remapping plan for already assigned virtual links (VLs) facing bandwidth expansion. The overall problem is formulated as a one-to-one matching game that aims to minimize the relocation cost from the users perspective and at the same time improves resource utilization from a service providers (SPs) perspective. By using the concept of preferences in the matching game, different stakeholders, i.e., end-users and SPs express their priorities. Thorough simulation analysis of the proposed approach shows that the model on an average can reduce the remapping cost by 19% and improve server utilization by 21% in comparison with the baselines.