Survivable and Bandwidth-Guaranteed Embedding of Virtual Clusters in Cloud Data Centers (Extended Version)

TL;DR

This paper addresses the challenge of embedding virtual clusters in cloud data centers with survivability and guaranteed bandwidth, proposing algorithms for resource-efficient primary and backup embedding to enhance service reliability.

Contribution

It introduces a formal problem definition for survivable virtual cluster embedding and proposes both optimal and heuristic algorithms for joint primary and backup embedding.

Findings

Heuristic algorithm achieves near-optimal resource efficiency.

Proposed algorithms significantly improve survivability and bandwidth guarantees.

Extensive simulations validate the effectiveness of the algorithms.

Abstract

Cloud computing has emerged as a powerful and elastic platform for internet service hosting, yet it also draws concerns of the unpredictable performance of cloud-based services due to network congestion. To offer predictable performance, the virtual cluster abstraction of cloud services has been proposed, which enables allocation and performance isolation regarding both computing resources and network bandwidth in a simplified virtual network model. One issue arisen in virtual cluster allocation is the survivability of tenant services against physical failures. Existing works have studied virtual cluster backup provisioning with fixed primary embeddings, but have not considered the impact of primary embeddings on backup resource consumption. To address this issue, in this paper we study how to embed virtual clusters survivably in the cloud data center, by jointly optimizing primary and backup embeddings of the virtual clusters. We formally define the survivable virtual cluster embedding problem. We then propose a novel algorithm, which computes the most resource-efficient embedding given a tenant request. Since the optimal algorithm has high time complexity, we further propose a faster heuristic algorithm, which is several orders faster than the optimal solution, yet able to achieve similar performance. Besides theoretical analysis, we evaluate our algorithms via extensive simulations.