Abstract

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.

Highlights

  • Several recent weather-based disasters around the world had very negative impacts on cloud networks, causing Data Center (DC) shutdown, consequent data-loss and intolerable downtime of cloud services

  • Given a physical wide area network topology, a DC affected by a disaster, a set of candidate safe destination DCs (i.e., DCs not affected by the disaster), a set of Virtual Machines (VM) running at the affected DC and an alert time, we decide i) the VMs to migrate4, ii) the routing path and migration bandwidth for each VM migration, iii) the scheduling and migration ending time5 of each VM migration with the objective of, in decreasing order of priority, 1) maximizing number of migrated VMs6, 2) minimizing sum of service downtime of all VMs and 3) minimizing overall network resource occupation (RO)

  • 4A VM might not be migrated from the affected DC. 5Note that the ending time of a VM migration is determined when deciding the migration starting time and assigning the migration bandwidth, which determines the migration duration. 6We consider that all VMs have the same priority and we focus on the objective of maximizing the total number of VMs migrated

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Summary

INTRODUCTION

Several recent weather-based disasters around the world had very negative impacts on cloud networks, causing Data Center (DC) shutdown, consequent data-loss and intolerable downtime of cloud services. Cloud networks are composed of a number of geographically distributed DCs interconnected by a communication network They play an indispensable role in delivering latencysensitive and bandwidth-hungry services to end users. To maximize the number of VMs migrated within the deadline, a problem of routing and bandwidth assignment must be solved jointly with the problem of scheduling the VM migration (i.e., deciding the starting and ending time of a VM’s migration) We refer to this problem as “Alert-based Online VM Migration for Disaster-Resilience” (Alert-VMmig). Since the execution time is consuming part of the alert time, we develop an ‘alert-aware VM evacuation’ tool which intelligently selects the most suitable approach, among the various heuristics proposed, based on the deadline, number and characteristics of VMs and capacity of the links.

BACKGROUND
Problem Statement
ILP Formulation
Online RO-min Algorithm
Online B-min
ALERT-AWARE VM EVACUATION TOOL
NUMERICAL RESULTS AND DISCUSSION
Validation of Heuristic Approaches
Objective
Larger Problem Instances
Analysis on Execution Time
Results
CONCLUSION

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