Queue-Aware Service Orchestration and Adaptive Parallel Traffic Scheduling Optimization in SDNFV-Enabled Cloud Computing


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Owing to software defined network function virtualization (SDNFV), network services can be implemented as service function chains (SFCs) in SDNFV-enabled Cloud Computing. SFCs consist of a series of ordered virtual network functions (VNFs). Due to the dynamic of underlying network state and the unpredictability of network traffic, the traditional SFC orchestrating (SFCO) approaches based on centralized placement and single-path routing lead to low availability of network resources, making it difficult to effectively manage and utilize complex and heterogeneous network resources. To address the above challenges, we propose a queue-aware SFCs orchestrating and adaptive parallel traffic scheduling optimization approach. First, the SFCO problem is modeled as a stochastic optimization problem, and the Lyapunov optimization theory is used to transform and decompose the SFCO problem to decouple the time coupling of optimal decision-making. An automatic decentralized algorithm based on queue model is proposed to orchestrate SFCs using information of local and its immediate one-hop neighbors. Furthermore, an adaptive parallel traffic scheduling optimization algorithm based on deep reinforcement learning is proposed, according to the decision output of the distributed SFC algorithm and current network state, network traffic is allocated to multiple paths for parallel transmission, which improves the availability of network resources and network performance. Experimental results show that, compared with the benchmarks, the average queue depth of the designed approach is reduced by 42.18%similar to 69.97% , the average cost of the designed approach is reduced by 16.1%similar to 55.6% , the average throughput is improved by 2.41%similar to 10.07% , the average link resource utilization rate is improved by about 6.9%similar to 28.3% , the average round-trip delay is shortened by 17.1%similar to 24.1% , and the average packet loss rate is reduced by 39.4%similar to 51.7% .
Cloud computing,Optimization,Costs,Delays,Quality of service,Dynamic scheduling,Couplings,Service function chain,orchestration,multi-path,Lyapunov optimization,deep reinforcement learning
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