Online Virtual Machine Evacuation for Disaster Resilience in Inter-Data Center Networks
Sousa, A. F.
; Mendieta, S.
; Musumeci, F.
; Tornatore, M.
IEEE Transactions on Network and Service Management Vol. 18, Nº 2, pp. 1990 - 2001, June, 2021.
ISSN (print): 1932-4537
Journal Impact Factor: (in )
Digital Object Identifier: 10.1109/TNSM.2021.3056766
With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.