The P-ART framework for placement of virtual network services in a multi-cloud environment
The P-ART framework for placement of virtual network services in a multi-cloud environment
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DOI：
10.1016/j.comcom.2019.03.003
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发表时间：
2019-05
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影响因子：
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通讯作者：
Lav Gupta;R. Jain;A. Erbad;D. Bhamare
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文献类型：
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作者：
Lav Gupta;R. Jain;A. Erbad;D. Bhamare
运营商的网络业务具有分布式、动态性、投资密集性等特点。将它们部署为虚拟网络服务 (VNS) 带来了低成本敏捷部署的前景，从而缩短了新服务的上市时间。如果这些虚拟服务在多个云上动态托管，则可以在优化性能和成本方面实现更大的灵活性。另一方面，当在多个云上进行编排时，运营商服务的严格性能规范变得难以满足，因此需要新颖且创新的放置策略。在选择适当的云组合进行放置时，重要的是要预见并可视化虚拟网络服务实际激活时将存在的环境。这有多种用途 - 可以选择云来优化成本，可以将所选的性能参数保持在定义的限制内，并且可以提高放置速度。在本文中，我们提出了 P-ART（预测自适应实时）框架，该框架依靠预测演绎功能来实现这些目标。由于预测如此之多，我们在框架中加入了一种新颖的概念——漂移补偿技术，通过考虑长期流量变化，使预测更接近现实。同时，预测模型的近乎实时更新可以处理突然的短期变化。然后，新的随机放置启发式方法将使用这些预测，该启发式方法使用成本最低的延迟约束策略来执行快速云选择。使用排队理论模型的数据集以及在 CloudLab 上的实施进行的实证分析证明了 P-ART 框架的有效性。该放置系统运行速度快，可在亚分钟的时间内放置数千个函数，接受率较高，适合动态放置。我们预计该框架将成为使用网络功能虚拟化 (NFV) 在多云系统上部署运营商级 VNS 成为现实的重要一步。
Carriers’ network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the time a virtual network service is actually activated. This serves multiple purposes — clouds can be selected to optimize the cost, the chosen performance parameters can be kept within the defined limits, and the speed of placement can be increased. In this paper, we propose the P-ART (Predictive-Adaptive Real Time) framework that relies on predictive-deductive features to achieve these objectives. With so much riding on predictions, we include in our framework a novel concept-drift compensation technique to make the predictions closer to reality by taking care of long-term traffic variations. At the same time, near real-time update of the prediction models takes care of sudden short-term variations. These predictions are then used by a new randomized placement heuristic that carries out a fast cloud selection using a least-cost latency-constrained policy. An empirical analysis carried out using datasets from a queuing-theoretic model and also through implementation on CloudLab, proves the effectiveness of the P-ART framework. The placement system works fast, placing thousands of functions in a sub-minute time frame with a high acceptance ratio, making it suitable for dynamic placement. We expect the framework to be an important step in making the deployment of carrier-grade VNS on multi-cloud systems, using network function virtualization (NFV), a reality.