A scalable network-aware framework for cloud monitoring orchestration

Abstract Monitoring components are implanted in clouds to evaluate performance, detect the failures, and assess component interactions by message analysis. In this paper, we propose Monitoring as a Service (MaaS) installed across its software defined network in the cloud. All switches in datacenter only forward traffic. Specific SDN application on top of the network controller has been implemented in order to orchestrate multiple network tenants monitoring needs. Applications declare the required observation for traffics and their specific monitoring needs to the Maas. Therefore, a set of virtual monitoring functions (vMF) are prescribed to be placed in datacenter for flows. An optimal placement algorithm places vMF with respect to the network and computing utilization maximization objectives. Network objective refers to minimize traffic delay for flows needed to be monitored and computing objective expresses balancing nodes computing resources. The optimal placement of virtual network functions is known to be an NP-hard problem. Compared to the existing work, we discovered different problem which how a vMF can be split into smaller pieces to decrease the total placement cost for a set of flows required, based on four patterns: free, parser-collocation, job-collocation, and full-collocation. Moreover, we proposed three heuristics to make our placement algorithm scalable for a large network, called, chain partitioning, topology partitioning, and zoning. We show the feasibility of our approach in a large dataset consists of 540k nodes and 11.5M edges, with about 40 requests (flows) at the same time. Furthermore, the proposed solution saves at least 20 percent of total monitoring cost, in average, compared to the latest related work.