Presto: Towards efficient online virtual network embedding in virtualized cloud data centers
Introduction
As highly multiplexed shared environments, cloud data centers are equipped with a large number of physical servers and virtual machines (VMs) hosted in servers to simultaneously offer multiple tenants with on-demand use of computing resources in a pay-as-you-go manner [1], [2], [3], [4], [5]. How to efficiently share the physical network resources among multiple tenants that have diversified network topologies with different network characteristics is a key concern. With respect to this issue, network virtualization has emerged as an efficient technology for resource sharing, where multiple heterogeneous network architectures are allowed to coexist on a shared substrate [6], [7], [8]. Upon on the virtualized shared data centers, the infrastructure providers then make best effort to utilize the substrate resources to serve the users that request customized services with required resources (such as CPU capacities, network bandwidth, etc.) running over different user self-defined network topologies, which are also known as virtual networks (VNs). Each virtual network consists of a set of virtual nodes interconnected through a set of virtual links with required capacities. The allocation of substrate resources to the virtual networks is called virtual network embedding3 (VNE). Each virtual node is mapped onto a substrate node, while each virtual link is mapped onto a substrate path connecting the corresponding substrate nodes under a series of pre-defined constraints. Fig. 1 illustrates an example of virtual network embedding, where two embedded virtual networks share the same substrate network.
The main objective of solving VNE problem is to make efficient use of substrate resources through dynamic and effective VN mapping algorithms. Although embedding diversified virtual networks of different users onto the underlying physical network can maximize the benefits gained from existing hardware of the infrastructure, VNE has been presented as a very challenging resource allocation problem [9], [10], [11] that has been addressed in many research studies [12], [13], [14], [15], [16], [17]. In fact, the VNE problem is NP-hard [13], [15], [16], [18], even in the offline case. Even when all the virtual nodes are embedded, to embed the virtual links is still NP-hard [15], [19], [20]. Naturally, the online case of VNE problem would be more intractable.
In response to this issue, in this paper we propose an efficient online heuristic VNE algorithm, named Presto, based on an Artificial Intelligence resource abstraction model called Blocking Island. Presto decomposes the VNE problem into two separate phases: virtual node mapping and virtual link mapping. In each phase, with the help of BI model, Presto ranks and embeds the virtual nodes or virtual links in a most advantageous order aiming to maximize the acceptance ratio. All the VN requests are also sorted according to some specific metrics, which targets at maximizing the economic revenues and minimizing the embedding cost. In addition, the proposed sliding window based batch processing approach enables Presto with the ability of lookahead and forward checking when processing the dynamically arriving VN requests. Moreover, with the benefit of BI paradigm, the search space is significantly decreased, and accordingly, the computation efficiency is greatly improved.
The primary contributions of this paper can be summarized as below:
- 1.
Two heuristic variable ordering algorithms HVNO and HVLO are designed.
- 2.
A node mapping algorithm HNM and a link mapping algorithm HLM are put forward.
- 3.
To the best of our knowledge, we are the first to apply BI paradigm to solve the VNE problem.
- 4.
Extensive simulations are conducted to evaluate the performance of Presto.
The rest of the paper is organized as follows. First we briefly review the related research literature in Section 2. Then Section 3 demonstrates the VN mapping model and problem formulation. In Section 4, the formulated multiple objective linear programming problem is presented. Afterwards, Section 5 introduces the BI paradigm. The Presto framework is designed in Section 6 followed by evaluations in Section 7. Section 8 concludes this paper.
Section snippets
Related work and motivation
A considerable number of research and investigations have been conducted on the virtual network embedding problem in recent years. In order to deal with the computationally intractable VNE problem, most of the proposals resort to heuristic algorithms aiming to find some feasible solutions other than optimal solutions. Generally, the existing works can be classified into two categories: offline algorithms [14], [21], [22] and online algorithms [12], [13], [16], [18], [23].
The offline algorithms
Substrate network model
The underlying substrate network can be modelled as a weighted undirected graph where Ns is the set of substrate nodes of the network and Es is the set of substrate links between nodes of Ns. Each substrate node ns ∈ Ns has an associated capacity weight value C(ns) to denote the available CPU capacity of the physical node ns. Each substrate link es(i, j) ∈ Es between two substrate nodes i and j is associated with a bandwidth capacity B(es), which denotes the amount of available
Multiple objectives linear programming optimization model
In this section, the virtual network embedding problem is formulated as a Multiple Objectives Linear Programming (MOLP) optimization problem. The goal is to find some feasible solutions which aim to optimize the two given objectives while satisfying a series of constraints including capacity constraints, flow constraints, domain constraints and some binary constraints.
Blocking island paradigm
As a resource abstraction model derived from Artificial Intelligence, Blocking Island (BI) was firstly proposed by Christian Frei, et al. in [35] to represent the availability of network link bandwidth. BI is defined as: A β-Blocking Island (β-BI) for a node x is the set of all nodes of the network that can be reached from x using links with at least β available resources, including x. In addition to link bandwidth abstraction, in this paper we extend the BI model to abstract the available CPU
Presto: BI-based online heuristic virtual network embedding framework
As aforementioned, the virtual network embedding (VNE) is an NP-hard problem and the traditional approaches suffer from a high complexity due to the huge searching space consisting of exponential number of nodes and routes. In response to this issue, with the benefit of BI model, we propose an efficient BI based heuristic framework, named Presto, to solve the online virtual network embedding problem with much lower and more manageable complexity.
Evaluation
In order to evaluate the performance of Presto, we implemented a Presto prototype by extending our DCNSim simulator [38] with some accompanying Python scripts. We firstly give an overview on the simulation environment. Then we demonstrate the evaluation results of Presto with respect to some performance metrics including acceptance ratio, gained revenue, embedding cost and computation efficiency.
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
Ting Wang received the Ph.D. degree in Computer Science and Engineering from Hong Kong University of Science and Technology, Hong Kong SAR China, in 2015, the Master Eng. degree from Warsaw University of Technology, Poland, in 2011, and the Bachelor Sci. degree from University of Science and Technology Beijing, China, in 2008. From 02.2012 to 08.2012 he visited as a research assistant in the Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently a research
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EE-CTA: Energy efficient, concurrent and topology-aware virtual network embedding as a multi-objective optimization problem
2019, Computer Standards and InterfacesCitation Excerpt :We implemented four existing algorithms to compare their performance with the proposed EE-CTA. The first algorithm is Presto (2016) which uses preemptive strategy with making a Blocking Island tree for the substrate network [17]. The second compared algorithm is ACO (2016) which divides each virtual network to ring or tree topology and then uses two strategies to embed divided subsections [2].
Green virtual network embedding with supervised self-organizing map
2019, NeurocomputingCitation Excerpt :According to the studies, virtual network embedding problem has two phases of node mapping and link mapping [18]. In the node mapping phase, each virtual node is mapped on a physical/substrate node with sufficient empty capacity [19]. In the link mapping phase, each virtual link is mapped on one or multiple disjoint paths with sufficient bandwidth.
Towards full virtualization of SDN infrastructure
2018, Computer NetworksCitation Excerpt :Network virtualization draws more and more attention in recent years. In public cloud, tenants outsource their infrastructure to the cloud data center, and each tenant has the specific requirement for his virtual network in the cloud [2]. In the wide-area network, people are thinking on network slicing [28], so as to support multiple logical (virtualized) network infrastructure, such as phone network and IoT network, in the future 5G core network [3,4].
Incorporating energy and load balance into virtual network embedding process
2018, Computer CommunicationsCitation Excerpt :In the subsequent section, we will introduce these two kinds of virtual network embedding algorithms. In recent years, energy-aware virtual network embedding algorithms [1–5] have received considerable attention because the power consumption of an average data center is the same as 25,000 households [6] and it is becoming the major energy cost of network operators which almostly account for 42% of the total operating costs. In addition, the security vulnerabilities caused by network virtualization technologies have been attracted great attention from researchers.
Ting Wang received the Ph.D. degree in Computer Science and Engineering from Hong Kong University of Science and Technology, Hong Kong SAR China, in 2015, the Master Eng. degree from Warsaw University of Technology, Poland, in 2011, and the Bachelor Sci. degree from University of Science and Technology Beijing, China, in 2008. From 02.2012 to 08.2012 he visited as a research assistant in the Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently a research scientist in Alcatel Lucent Bell Labs, Shanghai, China. His research interests include data center networks, cloud computing, green computing, network function virtualization, software defined network and next generation networks.
Mounir Hamdi received the B.S. degree in Electrical Engineering – Computer Engineering minor (with distinction) from the University of Louisiana in 1985, and the MS and the PhD degrees in Electrical Engineering from the University of Pitts-burgh in 1987 and 1991, respectively. He is a Chair Professor at the Hong Kong University of Science and Technology, and was the head of department of computer science and engineering. Now he is the Dean of the College of Science, Engineering and Technology at the Hamad Bin Khalifa University, Qatar. He is an IEEE Fellow for contributions to design and analysis of high-speed packet switching. He is/was on the Editorial Board of various prestigious journals and magazines including IEEE Transactions on Communications, IEEE Communication Magazine, Computer Networks, Wireless Communications and Mobile Computing, and Parallel Computing as well as a guest editor of IEEE Communications Magazine, guest editor-in-chief of two special issues of IEEE Journal on Selected Areas of Communications, and a guest editor of Optical Networks Magazine. He has chaired more than 20 international conferences and workshops including The IEEE International High Performance Switching and Routing Conference, the IEEE GLOBECOM/ICC Optical networking workshop, the IEEE ICC High-speed Access Workshop, and the IEEE IPPS HiNets Workshop, and has been on the program committees of more than 200 international conferences and workshops. He was the Chair of IEEE Communications Society Technical Committee on Transmissions, Access and Optical Systems, and Vice-Chair of the Optical Networking Technical Committee, as well as member of the ComSoc technical activities council. He received the best paper award at the IEEE International Conference on Communications in 2009 and the IEEE International Conference on Information and Networking in 1998. He also supervised the best PhD paper award among all universities in Hong Kong.