OCPA: An Algorithm for Fast and Effective Virtual Machine Placement and Assignment in Large Scale Cloud Environments

Zhuang, Zhenyun; Guo, Chun

doi:10.1109/cloudcom-asia.2013.81

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…The authors in [20] have made a discussion and categorized the VM placement schemes into resource-aware, power-aware, cost-aware and network-aware. Network-aware approaches, such as [21], [22], [23], [24], [25], try to reduce traffic related issues or avoid congestion and in general, VMs make use of network either to communicate with each other or to access the required data from storage components. However, none of these approaches take into consideration the anti-collocation requirement of VMs.…”

Section: Related Workmentioning

confidence: 99%

Algorithms for network-aware application component placement for cloud resource allocation

Barshan

Moens

Latré³

et al. 2017

J. Commun. Netw.

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Due to the soaring popularity of cloud-based services over the last years, the size and the complexity of cloud environments has been growing quickly. In the context of cloud systems, mapping a number of application components to a set of physical servers and assigning cloud resources to those components is challenging. Traditional resource allocation systems which rely on a centralized management paradigm suffer from scalability issues, making them inappropriate for large-scale cloud environments. Therefore, there is a need for providing new management solutions that scale well to large size cloud systems. In this article, we present optimal and heuristic solutions for network-aware placement of multi-component applications with differing component characteristics. The optimal ILP-based solution minimizes the application rejection rate and the cost of mapping while respecting application component requirements and physical network limitations. As the execution time of the optimal model scales exponentially, we also offer scalable heuristic solutions for centralized and hierarchical application placement, which are thoroughly explained and evaluated and compared to the optimal solution. Our evaluations show that while the proposed centralized heuristic is near-optimal, the hierarchical approach is much faster and offers higher scalability compared to a centralized approach, e.g. mapping 2.7 million application components onto 512k servers. Moreover, the percentage of servers used and fully placed applications remain close to that of the centralized and optimal solutions.

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