- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
As an efficient solution to diversify the future Internet for resource sharing in data centers, the network virtualization enables seamless integration of network experiments, services and architectures with different features by allowing multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. Embedding multiple virtual networks onto a shared substrate by allocating substrate resources to virtual nodes and virtual links of VN requests under a collection of constrains is known to be an NP-hard problem even for the offline VN embedding. To deal with this issue, this paper formulates the VN embedding problem as a new multiple objective linear programming optimization program, and solves it in a preemptive strategy by decomposing the problem into node mapping and link mapping phases. Furthermore, based on an Artificial Intelligence resource abstraction model, named Blocking Island (BI), we propose an efficient online heuristic VN embedding algorithm called Presto. Presto operates with quite low computation complexity and greatly reduces the search space, which far outperforms other candidates. The goal of Presto is to maximize the economic revenue of infrastructure providers while minimizing the embedding cost. The extensive simulation results further prove the feasibility and good performance of Presto in revenue, VN request acceptance ratio, computation efficiency and resource utilization.
8. Conclusion and future work
This paper aims to achieve an efficient online virtual network embedding algorithm in virtualized cloud data centers. In order to deal with the computationally intractable VNE problem, which is known as NP-hard, we formulated it as an MOLP problem with multiple practical objectives and designed an efficient VNE framework Presto consisting of a series of heuristic algorithms such as DHRO, HVNO, HVLO, HNM and HLM. With the benefit of Blocking Island paradigm derived from an AI model, Presto achieves a good performance in various aspects including revenue, embedding cost, acceptance ratio and computation efficiency. To the best of our knowledge, we are the first to apply BI model to deal with VNE problem. The extensive simulation results have witnessed the effectiveness of Presto adopting BI model. After all, there are still some open issues. For example, what the performance of Presto will be if path splitting and migration is allowed? How will the window size affect the acceptance ratio, overall revenue and embedding cost? How’s Presto’s performance if coordinated node and link mapping approach is applied? These issues are left for our future work. Besides, in future work more sophisticated recently proposed VNE algorithms will be implemented to conduct more comprehensive analysis and comparisons with our algorithm.