Network Function Virtualization (NFV) carries the potential for on-demand deployment of network algorithms in virtual machines (VMs). In large clouds, however, VM resource allocation incurs delays that hinder the dynamic scaling of such NFV deployment. Parallel resource management is a promising direction for boosting performance, but it may significantly increase the communication overhead and the decline ratio of deployment attempts. Our work analyzes the performance of various placement algorithms and provides empirical evidence that state-of-the-art parallel resource management dramatically increases the decline ratio of deterministic algorithms but hardly affects randomized algorithms. We, therefore, introduce APSR -- an efficient parallel random resource management algorithm that requires information only from a small number of hosts and dynamically adjusts the degree of parallelism to provide provable decline ratio guarantees. We formally analyze APSR, evaluate it on real workloads, and integrate it into the popular OpenStack cloud management platform. Our evaluation shows that APSR matches the throughput provided by other parallel schedulers, while achieving up to 13x lower decline ratio and a reduction of over 85% in communication overheads.

翻译：网络功能虚拟化(NFV)具有在虚拟机器中按需部署网络算法的潜力。然而,在大云层中,VM资源分配造成延误,阻碍了这种NFV部署的动态规模。平行资源管理是提高绩效的一个有希望的方向,但可能会大大增加通信间接费用和部署尝试的下降比例。我们的工作分析了各种安置算法的绩效,并提供了经验证据,证明最先进的平行资源管理大大增加了确定性算法的下降比率,但几乎不会影响随机算法。因此,我们引入了APSR -- -- 高效的平行随机资源管理算法,只需要少数东道主提供信息,并动态调整平行管理的程度,以提供可变的下降比率保证。我们正式分析APSR,评估实际工作量,并将其纳入流行的 OpenStack 云管理平台。我们的评价表明,APSR与其他平行调度器提供的吞吐量相匹配,同时达到13x的下降率,通信管理费减少85%以上。