A Stable Network-Aware VM Placement for Cloud Systems

Abstract

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult.We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NPHard; hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

“…More recently, more attention has been devoted to the problem of online (or adaptive) placement, whereby a sequence of VDC or VM requests is processed in an iterative fashion [26,27]. Our work falls into the second category.…”

“…The solution supports a broad range of management objectives with a single algorithm that performs adaptive placement. The solution is fully decentralized and highly Most works in the literature, for instance [19][20][21][22][23][24][25][26][27], address only a single management objective. The result in [28] supports multiple management objectives, but each objective with a different algorithm.…”

Exploiting CPU-Load and Data Correlations in Multi-Objective VM Placement for Geo-Distributed Data Centers

“…Differently, in practice, there are often cyclic communication scenarios, where two VMs regularly exchange information in both directions. As a result, the authors in [6] proposed two heuristic algorithms to address bidirectional data communication under time-varying traffic demands. The first one, 2PCCRS, can be applied only if the network topology is a tree.…”

“…We would like to thank Maurizio Rossi and Davide Brunelli for the discussions about the design of the renewable energy controller. [6]. For instance, scale-out applications (e.g.…”

SOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data Centers

“…Algorithms that minimize communication costs and latency were also proposed for resource allocation in distributed clouds [2]. Biran et al [5] formulated the network-aware VM placement problem as a Min Cut Ratio-aware VM Placement problem that considers not only local physical resources but also network resources, and introduced heuristics to solve it. In addition to these, there are also studies focusing on the SLA aspect [6,8,22], the contention between VMs [11,13], and the temperature in the data center [18].…”