- Open Access
Reliable design for virtual network requests with location constraints in edge-of-things computing
© The Author(s). 2018
- Received: 24 January 2018
- Accepted: 9 March 2018
- Published: 16 March 2018
How to efficiently map virtual networks (VNs) onto a shared physical network is a challenging issue in the field of network virtualization in edge-of-things computing. Since an efficient VN mapping approach can reduce network resource consumption, lower latency, and enhance service reliability, it is important for both customers and network service providers. In this paper, we study the problem of mapping multiple VNs with geographic location constraints onto a physical network while considering the survivability and reliability requirements of each VN request in edge-of-things based data centers. We present the model of this problem and propose a Geographic-Guided Survivable Multiple VN Mapping (GG-SMVNM) algorithm to efficiently solve this problem, which simultaneously considers resource sharing and mapping VN links and nodes in edge-of-things computing. Furthermore, we conduct a large amount of simulations to validate and evaluate our proposed approach. The simulation results show that the proposed method is superior to the existing solution.
- Network virtualization
- Edge-of-things computing
The emerging edge computing technologies [1–3], Internet of Things (IoT) [4, 5], and rich cloud services [6, 7] are used to create novel edge-of-things computing. In it, the data processing occurs in part at the network edge or between the cloud-to-end that can best meet customer necessities rather than entirely processing the data in a comparatively fewer number of massive clouds. Operators use the edge-of-things computing paradigm to provide network and computing services in a flexible and resource-efficient way [8–11]. Network virtualization is one of the main technologies and promoters of edge-of-things computing. Network virtualization allows multiple heterogeneous virtual networks (VNs) to share the same physical network in edge-of-things computing [12–16]. Due to the increasing popularity of edge-of-things computing, a great deal of research has been conducted on network virtualization and virtual network mapping technology [17–24].
A network virtualization environment (NVE)  is composed of shared resources (i.e., physical network with resource capacity) and virtual network (VN) requests in edge-of-things computing. A set of VN links and VN nodes makes up a VN request. Every VN node needs a fixed amount of nodal resources (i.e., storage resources, CPU and memory) to execute the edge-of-things computing services and applications, and each VN link that connects two VN nodes needs a great deal of communication bandwidth to exchange the data and information between the connected VN nodes. The progress of virtual network (VN) mapping is quite complicated because of the constraints of virtual links and nodes, despite knowing all VNs in advance. The VN mapping process is composed of two steps: the mapping of VN nodes and the mapping of VN links. Even if the mapping of all the virtual nodes is accomplished, the mapping of virtual links is still complicated. As a result, there are many VN mapping algorithms that can map as many VNs onto the physical network of edge-of-things computing as possible and minimize the VN mapping costs [26–30]. The VN mapping algorithm proposed in  maps the VNs under the guidance of minimizing mapping costs. However, it does not take the nodal survivability into consideration. The author comes up with the multiple VN mapping problems that consider survivability in , which introduces the VN mapping algorithm that considers the circumstance of physical network link failures. Research in  studies the VN mapping problem. It shares the backup resource among different VNs without considering the backup resource sharing of a single VN during the mapping process. The author in  researched the redeployment and migration problem of the dynamic VN. The authors in [29, 30] study the problem of VN mapping while considering the local failures of the physical network in edge-of-things computing.
Although many algorithms for VN mapping in edge-of-things computing have been designed, few of these algorithms take the effect of VN nodes’ geographic constraints into account. Moreover, no contribution has been made on the backup resource sharing among multiple VNs arriving simultaneously when node survivability is taken into consideration. For example, the 1-redundant method and the K-redundant method introduced in  can realize working and backup link resource sharing when mapping backup nodes and links, although they only apply for a single VN. Meanwhile, the working and backup link resource sharing can be achieved among multiple VNs arriving spontaneously when at most one physical node fails at a certain time. Moreover, nodal resource sharing can be realized thanks to the geographic constraint, which is left to further exploration and research.
We study the problem of reliable mapping for VN requests with location constraints in edge-of-things computing.
We propose the model and design an efficient algorithm for the studied problem.
We conduct extensive simulations to evaluate the performance of our proposed algorithms.
The remainder of the paper is arranged as follows. Section 5 outlines the problem statements and is followed by the heuristic algorithms in Section 3. Section 4 gives the detailed simulation results. Finally, Section 5 gives the conclusion of this paper.
2.1 Virtual network request
In this work, we research the issue of mapping multiple VN requests that guarantees the nodes’ survivability while at most only one node of physical network (PN) fails at any time in edge-of-things computing and while considering the geographic location constraints of VN nodes.
2.2 Physical network in edge-of-things computing
The physical network of edge-of-things computing is composed of multiple data centers that are dispersed across multiple geographical locations interconnected by a network. Like the VN request, a physical network model can be represented as G S = (N S , L S ), where N S represents the set of physical nodes of the physical network (every node of which provides a physical resource such as CPU and memory capacity with corresponding geographic coordinates) and L S represents the set of physical links of the physical network (with every physical link providing physical bandwidth resources to satisfy the communication demand between physical nodes). Furthermore, when a physical node fails, the corresponding physical links also fail. Hence, the virtual nodes mapped onto the failed physical node need to be migrated to another physical node that is not failed, and the corresponding virtual links also need to migrate. In this paper, there is at most only one physical node that fails at all times in the physical network of edge-of-things computing.
2.3 Reliable virtual network provisioning
A substrate edge-of-things computing physical network PN is represented as G S = (N S , L S ), and several virtual network requests arrive simultaneously.
2.3.2 Mapping constraints
They include the geographical position constraints of virtual nodes, the resource demands of virtual links, and the resource demands of virtual nodes.
There is at most one physical node failure at any time, and if there is a virtual node mapped onto it, the virtual node and adjacent links need to be migrated and recovered.
Under the precondition above with the mapping constraints, the problem is how to design and realize a mapping algorithm that can map several VNs arriving simultaneously onto the physical network and guarantee the survivability of virtual nodes. It must realize the resource sharing in every VN and also the resource sharing among VNs to save the physical resources of edge-of-things computing, with the aim of minimizing the mapping costs and getting a generally comparatively good multiple VN mapping result.
2.4 Problem formulation
2.4.1 Residual resources
To achieve the survivable VN mapping in a reasonable time, we design an effective heuristic to solve the problem that we researched in the paper. The Geographic-Guided Survivable Multiple VN Mapping (GG-SMVNM) algorithm is detailed in this section.
3.1 Step 1: successively map all the simultaneously arriving VNs onto the physical network of edge-of-things computing
When mapping the VN requests, we aim to map the virtual nodes of different VNs to the same substrate node to make the newly generated VN more concise. The mapping results satisfying the geographic location constraints of VN0, VN1, and VN2 are shown in Fig. 1. Figure 1 only depicts the mapping of virtual nodes, but the corresponding mappings of virtual links are not shown here. We map the virtual node v1 from VN0 and node v0 from VN1 to the same substrate node s4. Therefore, the capacity demand of the new virtual node generated based on s4 is the total amount of the resource demands of v1 from VN0 and v0 from VN1 when the new virtual network is generated from the physical mapping topology. Similarly, the resource demand of the new virtual node generated by physical node s2 is the total demand of the resource demands of v2 from VN0 and v2 from VN1. The corresponding links also satisfy this demand if two or more virtual links are mapped onto the same physical link. Then, the resource requirement of the new virtual link generated by the physical link is the sum of the resource requirements of these virtual links. If there are several mapping results of the virtual nodes belonging to different VNs due to the geographic constraint, then we need to choose the best one with the minimum mapping costs. For example, in Fig. 1, v1 from VN0 and v0 from VN1 can both be mapped onto the physical node s4, while node v1 of VN0 and v2 of VN1 can both be mapped onto s4 as well. Then, we chose the mapping solution with minimum mapping costs.
3.2 Step 2: when all the virtual nodes are mapped, generate a new virtual network according to the mapping results
3.3 Step 3: enhance the newly generated virtual network
3.4 Step 4: map the extra backup links and nodes
We apply the strategy similar with  to map backup links and nodes in order to decrease the costs of backup nodal mapping. Moreover, resource sharing of working links and backup links during the backup link and node mapping process leads to the resource sharing of links and nodes among original VNs, which will reduce the mapping costs as much as possible.
Pseudo code of the GG-SMVNM algorithm
Input: A physical network G S = (N S , L S ); the arrived VN requests VN0, VN1…VNX.
Output: A survivable mapping solution considering the location constraints of virtual nodes.
Step 1: Map the VNs that are arriving simultaneously.
The geographic locations constraints of the virtual node must be considered while mapping the VNs, and we should map the virtual nodes that belong to different VNs to the same substrate node.
Step 2: Update the resources of the physical network.
Compute the remaining recourses of physical nodes and links and calculate the costs of mapping multiple VNs.
Step 3: Generate a new virtual network based on the mapping results.
3.1) Traverse the nodes of the physical network and record the amount of resources allocated to virtual nodes. Add the virtual nodes to the set of virtual nodes from the new VN, and the capacity demand of these virtual nodes are the amount of recourses on corresponding physical nodes. Take the geographic locations of the corresponding physical nodes as the location constraints of the newly generated virtual nodes.
3.2) Traverse the routing table of the physical network. If the IDs of the corresponding substrate nodes of the virtual nodes in the set mentioned in 3.1 match the routing table, then there is a virtual link between these two virtual nodes.
Step 4: Map the backup nodes and links of the enhanced newly generated VN.
Traverse every backup node that needs to be mapped in descending order of resource demands and map the backup nodes and corresponding links onto the physical network.
4.1) Traverse the physical network nodes and compute the mapping costs of backup nodes and corresponding links.
4.2) Select a physical node with the minimum costs. Map the backup node onto it and map the corresponding backup links.
4.3) Update the resources and record the remaining capacities of the physical nodes and links.
Step 5: Compute the mapping cost of backup nodes and links.
Pseudo code of GG-SVNM algorithm
Input: A physical network G S = (N S , L S ), a virtual network request G V = (N V , L V ).
Output: A survivable mapping solution considering the location constraints of virtual nodes.
Step 1: Calculate CMS i for each physical node.
Step 2: Map the original virtual network.
The location constraints must be satisfied while mapping the virtual nodes.
Step 3: Update the resources of the physical network.
Calculate the remaining capacities of the physical nodes and links after mapping the original virtual network. Compute the total mapping costs of all virtual links and nodes from the original virtual network.
Step 4: Map the backup links and nodes.
4.1) Traverse all physical nodes. Then compute the mapping costs of the backup virtual nodes and relevant backup links.
a) Compute the mapping costs of backup links corresponding to backup nodes.
b) Compute the mapping costs of backup nodes.
c) Calculate the reduced costs brought on by the other virtual nodes in CMS i .
4.2) Select the physical node with minimum mapping costs. Then map the backup node onto it and map the corresponding backup links.
4.3) Update the resources. Calculate the available resources for the links and nodes on the physical network.
Step 5: Calculate the mapping costs of backup nodes and backup links.
In this, CMS i means the set of virtual nodes which physical node s i can host.
We first introduce the environment of our simulation in the section, and then we give the results. Last, we analyze the results of simulation.
4.1 Simulation environment
4.1.1 Physical network
4.1.2 Virtual network configuration
In the case of multiple VNs mapping, the number of VNs simultaneously arriving is random in real applications. In our simulations, without the loss of generality, the number of arriving VNs is randomly distributed from 1 to 4, and each VN randomly consists of 3 or 4 nodes. The resource demand of each node is a variable that is randomly generated between 20 and 30, and its geographic coordinate is also generated randomly with the longitude and latitude whose values fall into a specific range. The possibility that there exists a virtual link between two virtual nodes is 50%. The bandwidth demand of the virtual link is randomly generated between 50 and 80.
Parameter g represents the ratio of the unit node resource overhead to the unit bandwidth overhead. Different values of g can compare the effects that different ratios of the unit node resource costs to unit bandwidth costs on VN mapping costs. We set the parameter g to 5 in the simulations.
In our simulations, we presume that zero or one substrate node fails at any time and several VNs arrive simultaneously. For evaluating our proposed algorithm, we compare the mapping performances of our GG-SMVNM and GG-SVNM algorithms with the EVPF approach proposed in  and consider the geographic location constraints of virtual nodes. Furthermore, we vary the number of simultaneously arriving VNs (the number of nodes on each VN is 3 or 4) on the premise that the physical resources are abundant and compare the total mapping costs, backup node mapping costs, and backup link mapping costs of these two algorithms.
We used Microsoft Visual Studio 2008 and C++ programming language to implement the compared algorithms.
4.2 Performance metrics
The total VN mapping cost: the total expenses of using physical network resources to provide all VN requests. It can be calculated as follows:
The backup node mapping cost: the total expenses of using physical node resources to host the backup virtual nodes. It can be calculated as follows:
The backup link mapping cost: the total expenses of using physical link resources to host the backup links. It can be defined as follows:
4.3 Simulation results and analysis
In this paper, we propose a survivable VN mapping algorithm (GG-SMVNM) that considers the geographic location constraints of virtual nodes for efficiently mapping multiple VNs in edge-of-things computing. We introduce the resource sharing strategy in VNs and also across multiple VNs to save the physical resources of edge-of-things computing and reduce the mapping costs. Furthermore, the geographic location constraints are considered in both original virtual nodes mapping and backup node mapping, which makes significant sense in real edge-of-things computing applications. We conduct extensive simulations on different networks to evaluate our proposed algorithms. The simulation results show that our proposed algorithm has better performance than existing approaches.
In this research, we mainly focus on the problem of provisioning VN requests with location constraints in an autonomous domain network in edge-of-things computing. However, in practical applications, there are some VNs need to be deployed onto multiple autonomous domain networks. Therefore, in our future research, we are going to study and solve the problem of reliable VN mapping in multiple domains while considering the quality of service (QoS) requirements.
This research was partially supported by the Fundamental Research Funds of China West Normal University (No.17D075).
Availability of data and materials
SZ is in charge of the major theoretical analysis, algorithm design, and numerical simulations. AKS is in charge of part of the theoretical analysis and algorithm design. Both authors read and approved the final manuscript.
Both authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- W Shi, J Cao, Q Zhang, et al., Edge computing: vision and challenges. IEEE Internet Things J 3(5), 637–646 (2016)View ArticleGoogle Scholar
- TX Tran, A Hajisami, P Pandey, et al., Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun. Mag. 55(4), 54–61 (2017)View ArticleGoogle Scholar
- S Sardellitti, G Scutari, S Barbarossa, Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Trans Signal Inf Proc Over Netw 1(2), 89–103 (2015)MathSciNetView ArticleGoogle Scholar
- G Sun, V Chang, M Ramachandran, et al., Efficient location privacy algorithm for internet of things (IoT) services and applications. J. Netw. Comput. Appl. 89, 3–13 (2017)View ArticleGoogle Scholar
- X Sun, N Ansari, EdgeIoT: mobile edge computing for the Internet of Things. IEEE Commun. Mag. 54(12), 22–29 (2016)View ArticleGoogle Scholar
- J Li, X Huang, J Li, et al., Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Syst 25(8), 2201–2210 (2014)View ArticleGoogle Scholar
- X Chen, X Huang, J Li, et al., New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE Trans Inf Forensics Secur 10(1), 69–78 (2015)View ArticleGoogle Scholar
- J Li, J Li, X Chen, et al., Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans. Comput. 64(2), 425–437 (2015)MathSciNetView ArticleMATHGoogle Scholar
- G Sun, D Liao, D Zhao, et al., Live migration for multiple correlated virtual Machines in Cloud-based Data Centers. IEEE Trans. Serv. Comput., 1–14 (2016)Google Scholar
- J Li, J Li, D Xie, et al., Secure auditing and deduplicating data in cloud. IEEE Trans. Comput. 65(8), 2386–2396 (2016)MathSciNetView ArticleMATHGoogle Scholar
- P Li, J Li, Z Huang, et al., Privacy-preserving outsourced classification in cloud computing. Clust. Comput., 1–10 (2017)Google Scholar
- J Li, Y Zhang, X Chen, et al., Secure attribute-based data sharing for resource-limited users in cloud computing. Comput Secur 72, 1–12 (2018)View ArticleGoogle Scholar
- G Sun, D Liao, D Zhao, et al., Towards provisioning hybrid virtual networks in federated cloud data centers. Futur. Gener. Comput. Syst. (2017) Available online 18 OctoberGoogle Scholar
- Y Zhang, X Chen, J Li, et al., Ensuring attribute privacy protection and fast decryption for outsourced data security in mobile cloud computing. Inf. Sci. 379, 42–61 (2017)View ArticleGoogle Scholar
- G Sun, V Anand, D Liao, et al., Power-efficient provisioning for online virtual network requests in cloud-based data centers. IEEE Syst. J. 9(2), 427–441 (2015)View ArticleGoogle Scholar
- P Li, J Li, Z Huang, et al., Multi-key privacy-preserving deep learning in cloud computing. Futur. Gener. Comput. Syst. 74, 76–85 (2017)View ArticleGoogle Scholar
- S Su, Z Zhang, A Liu, et al., Energy-aware virtual network embedding. IEEE/ACM Trans. Networking 22(5), 1607–1620 (2014)View ArticleGoogle Scholar
- R Mijumbi, JL Gorricho, J Serrat, et al., A neuro-fuzzy approach to self-management of virtual network resources. Expert Syst. Appl. 42(3), 1376–1390 (2015)View ArticleGoogle Scholar
- J Li, X Chen, X Huang, et al., Secure distributed deduplication systems with improved reliability. IEEE Trans. Comput. 64(12), 3569–3579 (2015)MathSciNetView ArticleMATHGoogle Scholar
- G Sun, V Chang, G Yang, et al., The cost-efficient deployment of replica servers in virtual content distribution networks for data fusion. Inf. Sci. 432, 495-515 (2017) Available online 10 AugustGoogle Scholar
- J Li, Y Li, X Chen, et al., A hybrid cloud approach for secure authorized deduplication. IEEE Trans Parallel Distrib Syst 26(5), 1206–1216 (2015)View ArticleGoogle Scholar
- G Sun, D Liao, V Anand, et al., A new technique for efficient live migration of multiple virtual machines. Futur. Gener. Comput. Syst. 55, 74–86 (2016)View ArticleGoogle Scholar
- H Yu, T Wen, H Di, et al., Cost efficient virtual network mapping across multiple domains with joint intra-domain and inter-domain mapping. Opt. Switch. Netw. 14, 233–240 (2014)View ArticleGoogle Scholar
- J Li, Z Liu, X Chen, et al., L-EncDB: a lightweight framework for privacy-preserving data queries in cloud computing. Knowl.-Based Syst. 79, 18–26 (2015)View ArticleGoogle Scholar
- G Sun, H Yu, V Anand, et al., A cost efficient framework and algorithm for embedding dynamic virtual network requests. Futur. Gener. Comput. Syst. 29(5), 1265–1277 (2013)View ArticleGoogle Scholar
- NMK Chowdhury, MR Rahman, R Boutaba, Virtual network embedding with coordinated node and link mapping. IEEE Infocom, 783–791 (2009)Google Scholar
- WL Yeow, C Westphal, UC Kozat, Designing and embedding reliable virtual infrastructures. ACM Sigcomm Comput Commun Rev 41(2), 57–64 (2011)View ArticleGoogle Scholar
- Z Cai, F Liu, N Xiao, et al., Virtual network embedding for evolving networks. IEEE Globecom, 1–5 (2010)Google Scholar
- H Yu, C Qiao, V Anand, et al., Survivable virtual infrastructure mapping in a federated computing and networking system under single regional failures. IEEE Globecom, 1–6 (2010)Google Scholar
- G Sun, H Yu, L Li, et al., The framework and algorithms for the survivable mapping of virtual network onto a substrate network. IETE Tech. Rev. 28(5), 381–391 (2011)View ArticleGoogle Scholar
- H Yu, V Anand, C Qiao, et al., Cost efficient design of survivable virtual infrastructure to recover from facility node failures. IEEE ICC, 1–6 (2011)Google Scholar
- G Sun, D Liao, S Bu, et al., The efficient framework and algorithm for provisioning evolving VDC in federated data centers. Futur. Gener. Comput. Syst. 73, 79–89 (2017)View ArticleGoogle Scholar