[Retracted] Fault-Tolerant Secure Routing of BH<sub><i>n</i></sub>-Based Data Center Networks

Zhang, Xinxin; Xu, Li; Li, Aihua

doi:https://doi.org/10.1155/2021/6723914

Security and Communication Networks

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Abstract Introduction Preliminaries Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Research Article | Open Access

Volume 2021 | Article ID 6723914 | https://doi.org/10.1155/2021/6723914

[Retracted] Fault-Tolerant Secure Routing of BH_n-Based Data Center Networks

Xinxin Zhang,^1,2Li Xu ,^1,2and Aihua Li³

Academic Editor: Xingsi Xue

Received13 Sept 2021

Accepted08 Nov 2021

Published31 Dec 2021

Abstract

As the core infrastructure of cloud computing, a large scale of the data center networks (DCNs), which consist of millions of servers with high capacity, suffer from node failure such that the reliability is deteriorated. Malicious group could inevitably compromise the quality and reliability of data; thus, how to ensure the security routing of data is an urgent practical problem. As models for large-scale DCNs, it is worth mentioning the balanced hypercube, which is well-known for its strong connectivity, regularity, and a smaller diameter. Each of which makes a balanced hypercube a trustworthy model to deal with data traffic and provides a certain degree of fault-tolerance as well. In this paper, we use the balanced hypercube as a model for the data center networks and design a reliable safety level by referring to different safety levels of related subgraph. This subgraph contains the source and destination nodes, and the shortest feasible paths are located so that the reliable transmission is achieved. Then, we get that the length of fault-tolerant safety routing of data center networks based on balanced hypercube is always no greater than the Hamming distance plus two. Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

1. Introduction

With the advent of 5G mobile communication technology, demands for network information and user are increasing. Internet giants at home and abroad continue to expand their own data centers. Many large data centers are being built to provide increasingly popular online services, such as gaming, e-mail, cloud storage, and infrastructure services. For example, Microsoft implied that Bing, Hotmail, and a handful of other smaller services would be backed by a million servers [1]. The popularity of IoT is increasing with artificial intelligence technology, which covers a wide range of applications. As crucial components of IoT, sensor networks include connected sensors and associated devices, which deploy plenty of heterogeneous sensing nodes for capturing environmental data [2–4]. The secure routing is a key focus since the industrial environment is extremely vulnerable and many potential threats (e.g., distributed denial of service and selfish attacks) are introduced due to the connection to the Internet [5]. Load balancing is a high-end solution for large-scale networks to solve high-load access and a large number of concurrent requests. The goal of a more feasible approach for architests is to achieve flexible scaling and high reliability. With the expansion of the data center network scale, distributed equilibrium has more and more advantages. DCNs fall into two main categories: switch-centric and server-centric. Server-centric DCNs have the advantage of reducing the cost and providing a higher degree of programmability. However, servers usually have a much larger processing delay than switches and by using servers for forwarding packets [6]. As the number of servers in data center networks (DCNs) increases, server failures may become the common phenomenon rather than exceptions. Zhou et al. [7] proposed an adaptive authenticated model for big data stream SAVI in SDN-based data center networks. Usual secure data transmission in a DCN is based on the condition of the set of arbitrary faulty servers.

Fault-tolerant routing means that efficient transmission of information routing can still be found when there is a certain number of fault nodes in the network systems. It is an important application in different networks and has been studied extensively in the literature. Chiu and Chen [8] proposed an algorithm to facilitate efficient fault-tolerant routing of messages in hypercube multicomputers which routes a message in an attempt to minimize rerouting. Derek et al. [9] discussed the fault-tolerant message routing using local neighborhood information, while Xiao et al. [10] studied it with load balancing support. Duong and Kaneko [11] developed two new fault-tolerant routing algorithms for hypercubes based on approximate directed routable probabilities and conducted a computer experiment to verify the effectiveness of their algorithms. Similarly, Park et al. [12] proposed a fault-tolerant routing algorithm in a dual-cube with the same idea and presented results of a computer experiment showing better performance of their algorithm. The edge fault-tolerant properties of hypercubes and folded hypercubes were proposed by Xu et al. [13], and these conclusions provide a solid theoretical basis for fault-tolerant routing algorithms. As the network environment becomes increasingly complex, more and more researches projects on the fault-tolerant routing in these network environments are conducted. For example, Mohammad [14] put forward an adaptive fault-tolerant routing algorithm for an injured tree-hypercube which requires that each node only knows the condition of its link. Penaranda et al. [15] presented a fault-tolerant routing methodology for the KNS topology that degrades performance gracefully in the presence of faults and tolerates a reasonably large number of faults without disabling any healthy node. Xiang et al. [16] used limited global safety information to study fault-tolerant routing in 2D tori or meshes, and then they [17] studied fault-tolerant efficient routing in dragonfly networks which have been widely used in the current high-performance computers or high-end servers. Robert et al. [18] investigated a fault-tolerant routing strategy for -ary -direct -indirect topologies based on intermediate nodes.

At present, research on the data center networks is mostly based on DCell which is proposed by Guo et al. [19] in 2008. Two years later, Markus et al. [20] proposed a generalized DCell structure for load-balanced data center networks. Marc et al. [21] developed the connectivity of data center networks modeled by DCell. Wang et al. [22] proposed an efficient algorithm for finding a disjoint paths in the node-to-set routing of DCell networks. Subsequently, they studied the Hamiltonian properties of DCell networks and extend it to all generalized DCell Hamiltonian-connected [23], the restricted -connectivity [24], and vertex-disjoint paths in DCell networks successively [25]. Li et al. [6] studied the diagnosability and -good-neighbor conditional diagnosability of the data center network DCell. Gu et al. [26] discussed the pessimistic diagnosability of data center networks, and Lv et al. [27] studied the reliability evaluation of DCell networks.

Nevertheless, compared with the DCell, the balanced hypercube proposed by Wu and Huang can handle a reasonable amount of message traffic and also provide certain degree of fault tolerance since the balanced hypercube is a variation of the standard hypercube with desirable properties of strong connectivity, regularity, and small node multidiameter and it is a special type of load-balanced graph designed to tolerate processor failure [28]. The balanced hypercube originates from the hypercube problem that relates to the construction of networks of parallel processors. The diameter of a graph is an important measure of communication delay. With the deployment of high connectivity data center network, the network provides multiple available transmission paths and a large amount of available bandwidth for data transmission. Normally, the shorter the diameter is, the lower the communication delays. The balanced hypercube meets the topological requirements of the data center network. In [28], Wu proposed the balanced hypercube for fault-tolerant applications. Cheng [29] studied the fault-tolerant cycle embedding in a balanced hypercube. Zhang et al. [30] studied the feedback numbers of a balanced hypercube. All of these provide a theoretical basis for our research. We study the -diagnosability of balanced hypercube under the PMC model [31]. The data center network based on the balanced hypercube model is very suitable for increasingly complex network structure.

The main motivation of this study is to propose an efficient fault-tolerant security routing of -based data center network, and our ideas come from [6, 32]. Our contributions to this paper are as follows.

We design the safety level to characterize the reliability of a balanced hypercube.

According to the different safety levels of the spanning subgraph, we determine the shortest feasible paths, which are located such that reliable transmission is achieved.

Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

We organize our paper as follows. In Section 2, we outline terminologies and notations used throughout this paper and then propose the definition and propositions of data center networks based on the balanced hypercube model. In Section 3, we introduce the definition of local safety level. In Section 4, we give and prove the minimum feasible path with local safety information. In Section 5, we propose fault-tolerant routing with local safety levels. Section 6 is the numerical value analysis. Section 6 is a summary of this paper.

2. Preliminaries

In this section, we first give some important terminologies and notations throughout the paper, and then we introduce the definition and propositions of data center networks based on the balanced hypercube models.

2.1. Terminologies and Notations

In DCNs, the topology of which is often represented by a graph , where each node denotes a processor the edge denotes a link between nodes and . If and , we call a subgraph of . The number of nodes in a set is defined as , and the number of edges in the edge set of is defined as . If and are subsets of , we define the set of edges between and as and denote the neighbor set of as , where indicates removing node from for . represents the number of hops routing from to , and is the number of hops of the minimum feasible path from to .

2.2. The Balanced Hypercube

In the following, a special type of load-balanced graph called balanced hypercube (), where the positive integer is called the dimension of the balanced hypercube. The DCN-based , which interconnect servers, is defined in a recursive style. That is, an -dimensional can be recursively constructed from four -dimensional ones. It is load balanced because for every node in , there exists another node matching it and these two nodes have the same adjacent nodes. Due to this structure, can effectively alleviate the problem of congestion and deadlock in DCNs.

Definition 1. (see [28]). An -dimensional balanced hypercube consists of nodes , where and . Every node connects the following nodes
, where, whereFor convenience, we assume that all the arithmetic operations on the indexes of nodes in are four modulated. Figure 1 shows the structures of two balanced hypercubes and , where consists of four . In , the first element of node is named inner indices, while the other elements are outer indices. Any node in has two types of adjacent nodes: inner and outer. Every node in has two inner adjacent nodes and outer adjacent nodes.
The -dimensional balanced hypercube is very similar to a -dimensional hypercube in the sense of topological properties. These two structures share similar features in terms of symmetry and connectivity, which will be discussed in the following section. Note that a -dimensional hypercube has a diameter of , has a smaller diameter than , when is odd. Thus, the balanced hypercube is more suitable for routing. Based on Definition 1, it is clear that can be derived from four s by adding a new dimension as the th outer index of every node in .

Proposition 1 (see [28, 33]). The has some important properties as follows:(1) is a load balanced graph, and nodes in can be partitioned into a set of matching pairs, and .(2) has nodes, each of which has adjacent nodes.(3) is -connected, for any pair of nodes in , there exist disjoint paths between them.(4)Let and be two distinct partite sets of which is a bipartite graph. Assume and are two different nodes in , , and are two different nodes in . Then there exist two node-disjoint paths and , and = for , where and represent paths from to and from to , respectively.

3. Local Safety Level

Two processors are connected by a bidirectional link if and only if the binary representations of the two processors differ in exactly one bit or two bits according to Definition 1. The Hamming distance between two nodes and , denoted by , is the number of hops between and . In the , there are many , among them are the -dimension subcubes. The between source and destination is the smallest spanning balanced hypercube that contains and .

Lee and Hayes [34] proposed the concept of a safe node for the first time. Routing is to avoid unsafe nodes which could lead to communication difficulties. Chiu and Wu [35] proposed a fault-free node is defined as an unsafe node if it has either two or more faulty nearest neighbors, a fault-free node that is not unsafe is called a safe node. And, in [31], we can diagnose the faulty nodes in the diagnostic process based on [35], and we redefine them as follows:(1)A faulty node is level-0 safe(2)An unsafe node, that has no safe neighbor is level-1 safe(3)An unsafe node, that has at least one safe neighbor is level-2 safe(4)A fault-free node that is not unsafe is called a level-3 safe that is a safe node

A cube is fully unsafe if it contains no safety node. Our work in this paper is presented based on the above safe node concept. We introduce the concept of to facilitate fault-tolerant routing. Now, we give an example to illustrate as follows.

The 3-dimensional balanced hypercube as presented in Figure 2. Obviously, we diagnose the solid nodes are faulty called level-0 safe, then we can judge that nodes and , are level-1 safe, and nodes , , , , , , , , , , , , , , , , , , and are level-2 safe, and the other nodes are level-3 safe. The safety status indicates the routing capability of each node, and the safety state of each node affects the safety status of the whole system and then determines the routing efficiency of the whole network. A subgraph , is fully unsafe if it has no safe node; obviously, there are no fully unsafe subgraph in Figure 2.

Next, we list the useful definitions and properties of local safety are proposed by Xiang [32, 33] in order to prove that there are feasible paths under different safety conditions:

Definition 2. (see [32, 33]). The safety level of the is defined as follows:(1)A node in a hypercube is locally unsafe corresponding to a subcube if it has at least two faulty neighbors or at least three locally unsafe or faulty neighbors inside . Otherwise, it is locally safe corresponding to . A subcube is fully unsafe if it contains no locally safe node; otherwise, it is a safe subcube.(2)Locally unsafe nodes corresponding to a subcube is classified as follows: A locally unsafe node is called locally level-2 safe if it has at least one locally safe neighbor (level-3 safe) inside ; otherwise, it is a locally level-1 safe node.(3)A node in a hypercube is locally unsafe inside an if it has at least 2 faulty neighbors or at least 3 locally unsafe or faulty neighbors inside the ; otherwise, it is locally safe in the . The is unsafe if it contains no locally safe node (level-3 safe); otherwise, it is a safe . Locally unsafe nodes inside an are classified as a locally unsafe node is level-2 safe if it has at least one locally safe neighbor (level-3 safe) in the ; otherwise, it is a level-2 safe node.(4)An -dimensional subcube is defined as a maximal safe subcube if it is safe and any -dimensional subcube that contains it is fully unsafe.

Proposition 2 (see [32, 33]). The following properties can be obtained from the above definition:(1)A -dimensional subcube is safe if and only if it contains a fault-free -dimensional subcube, and all nodes of which have at most one faulty neighbor (level-0 safe).(2)If a node is locally unsafe in a -dimensional spanning subcube , it is still locally unsafe in an -dimensional spanning subcube if contains .(3)Suppose the subcube is an -dimensional safe subcube in a faulty -cube . A locally level-1 safe node in always has a locally level-2 safe neighbor onside even though the faulty -cube is fully unsafe.We can use the following scheme to obtain local safety information for all fault-free nodes concurrently if the is fully unsafe. For each node check local safety of node in -dimensional subgraphs . If has at least two faulty neighbors inside a subgraph, then the node is locally unsafe in the subgraph. The node stores local safety information of itself if the local safety information of the subgraph has been obtained and the subgraph is safe. The node also keeps local safety information of its neighbors inside the subgraph.

4. Minimum Feasible Path with a Local Safety Level

The safety status of the spanning balanced hypercube has an important effect on routing between and and a path is called a feasible path if it travels through no faulty nodes. A feasible path is called minimum if it has the shortest length among all feasible paths between the source and destination nodes. In this section, we will prove some theorems about minimum feasible path with a local safety level of the balanced hypercubes, and these theorems will be used in the routing algorithm in Section 5.

Theorem 1. There always exists a minimum feasible path from to if either or is locally safe in even though the faulty is fully unsafe.

Proof. Without loss of generality, we only consider the case when source is locally safe. The case when the destination is locally safe is similar. We use the inductive hypothesis. When , messages from can be routed to directly, so . when , according to Definition 1 (1) there exists at least one safe neighbor in because source is locally safe corresponding to . Messages in can be transmitted to destination via along a minimum feasible path and . Assume the theorem holds when and . We would like to prove the theorem also holds when . Assume the source , we know that has neighbors (among them there are two extra neighbors) in distinct minimum feasible paths between and and there exists more than one locally safe neighbor of . In addition, is locally safe in according to Proposition 1 (1). Whether (see in Figure 3(a)) or (see in Figure 3(b)), indicating . Thus, when and , and there exists one minimum feasible path.

(a)

(b)

Theorem 2. Suppose the spanning balanced hypercube is safe, where both of and are locally unsafe in . There always exists a minimum feasible path between and even if the faulty is fully unsafe.

Proof. There are two cases based on the different safety levels of unsafe nodes according to Definition 1 (3).

Case 1. Either or is level-2 safe. It is sufficient to only consider the source is level-2 safe corresponding to . The situation is the same when the destination is level-2 unsafe inside . Assume source is a level-2 safe node; it has a locally safe neighbor inside according to Definition 1 (3). Node is in one of the minimum feasible paths from to according to the premise, which should still be a locally safe node inside according to Proposition 1 (2). The spanning balanced hypercube is safe according to Definition 1 (1) because it contains at least one locally safe node . Now, this case of conversion to Theorem 1, and it is easy to find a minimum path from to which length , and .

Case 2. When both of source and destination are locally level-1 safe. The Hamming distance , when both and are locally level-1 safe in their safe spanning balanced hypercube . The locally level-1 safe source has at least one locally level-2 safe neighbor inside according to Proposition 1 (3) because is a safe subgraph. All neighbors of inside which is in one of the minimum paths from to . Certainly, is in one of the minimum feasible paths from to . The node has at least one locally level-3 safe neighbor corresponding to according to Definition 1 (1). Among the neighbors of , one is and the other is distinct. Neighbors are distinct all in minimum paths from to . Certainly, the node is in one of the minimum paths of length from to . It is clear that is also a locally safe node in the spanning balanced hypercube according to Proposition 1 (2) because contains . Now, in the case of conversion to Theorem 1, it is easy to find a minimum path from to .
In summary, there always exists a minimum feasible path whose length is from a source to a destination if the spanning balanced hypercube is safe even though the faulty is fully unsafe.

Theorem 3. There always exists a minimum feasible path from a source to a destination if the spanning balanced hypercube is unsafe even though the faulty is fully unsafe.

Proof. When the source and destination are fault-free in an unsafe spanning balanced hypercube , we only consider the source . Since, all the neighbors in the path from to are not level-3 safe in the , we need to only find a safe neighbor outside of the subcube (see in Figure 4) and then find a in the subgraph. Consequently, the length of the feasible path from to is .

Theorem 4. There exists a minimum feasible path from source to destination if there exists a fault-free node along with minimum feasible path from to such that both and are safe.

Proof. Let be the set of dimensions that source and destination differ and \{\} be a partition of . That is to say, and . A spanning balanced hypercube can be generated corresponding to from source and another spanning balanced hypercube can be established corresponding to . There exists a minimum feasible path from source to node according to Theorem 1 because the spanning balanced hypercube is safe. Similarly, there exists a minimum feasible path from to destination because is also a safe subgraph. Therefore, there exists a minimum feasible path from source to destination .

5. Fault-Tolerant Routing with Local Information

In this section, we use the above theorems about minimum feasible path to give a corresponding fault-tolerant routing algorithm in balanced hypercube DCNs.

A fault-tolerant routing algorithm is presented based on local safety information. The main idea of this routing scheme is as follows.

Assume information about maximal safe subgraphs of the balanced hypercube is available at each node. When the spanning subgraph of a source and a destination is found to be contained in a maximal safe subgraph, the following algorithm can always pass the message from the source to the destination .

However, when a maximal safe subcube that contains the spanning balanced hypercube is not available, the message is sent to a fault-free neighbor , taking the preference to the one in a minimum feasible path from the source to the destination . We first check whether the spanning balanced hypercube is contained in a maximal safe subgraph or not. If the spanning balanced hypercube is contained in a maximal safe subgraph, a feasible path has been found. The procedure - passes the message from to when it is certain that the message can be routed successfully inside the maximal safe subgraph that contains the spanning subgraph . Then, according to the current node and the destination , we propose the following cases the message delivery: If , send the message through a different dimension. If , send the message to a fault-free neighbor along with one of the minimum feasible paths from to if possible. Otherwise, if a fault-free neighbor in a minimum feasible path from the source to the destination is not available, deroute the message to a proper neighbor. If , according to the safety level of the node passes the message as follows:(1)When the current node is locally level-1 safe in the subgraph , send the message to one of its locally level-2 safe neighbors in a minimum feasible path from to if possible. Otherwise, send the message to a locally level-1 safe neighbor of in a minimum path from to if possible. When both of the above conditions are not met, according to Theorem 3, send the message to a local level-2 safe neighbor in a nonminimum path from to , which is always available.(2)When is locally level-2 safe in the subgraph , send the message to a locally safe neighbor in a minimum feasible path from to if possible. Otherwise, send the message to a locally level-2 safe neighbor of in a minimum feasible path from to if possible. When both conditions are not met, according to Theorem 3, send the message to a locally safe neighbor of in a nonminimum path from to .(3)When is local safe in the subgraph , send the message to a local safe neighbor of in a minimum feasible path from to if ; this path is in a minimum feasible path from to , and the length of the path is .

We give an example to illustrate that, as shown in Figure 5. It is a fault with several faulty nodes. According to the structure of balanced hypercube, the source node and destination are both level-2 safe and locally safety in , and is contained in a maximal safety subgraph according to Theorem 4. So, the message can be sent by minimum feasible paths from to . They are path 1: ; path 2: ; path 3: ; path 4: ; path 5: ; and path 6: . The length of all paths are no greater than 6. We know that the Hamming distance of the balanced hypercube is 4, so the length of fault tolerant routing is balanced no more than . In summary, the hops of fault tolerant routing from to is always no more than by local information.

By the above analysis, we propose Algorithm 1 for fault-tolerant routing with local safety level (see Algorithm 1).

	Input: send the message from the source node to destination node in a fully unsafe .
	Output: a fault-tolerant routing call --.
(1)	for source and destination are locally safe in do
(2)	if the is contained in a maximal safe subgraph ; then
(3)	The message is sent by the minimum feasible path from to ;
(4)	else
(5)	Select a neighbor along with a minimum feasible path from to where is contained in a maximal safe subgraph , pass the message to , and pass the message;
(6)	end if
(7)	end for
(8)	return-;

Xiang [32] proposed that the length of fault-tolerant routing is no more than in a faulty hypercube, where represents the Hamming distance from the source node to the destination node . In this paper, we use the local safety level to propose and prove that, in a faulty balanced hypercube, if there is a neighbor node of which satisfies the conditions in Section 5, and it appears on the shortest feasible path, we send messages in a minimum feasible path whose length is . On the contrary, if neither condition is satisfied, we choose an appropriate nonminimum path to send the messages form to and the length of this fault-tolerant routing is at most by calculating. Thus, we can know that the length of all paths for sending a message is no more than . Specifically, when , the length of fault-tolerant routing is equal to whether it is in hypercube networks or balanced hypercube networks, however, when , the length of fault-tolerant routing in balanced hypercube networks is two hops less than that in hypercube networks. It shows that the message routing in an injured balanced hypercube network is more efficient than that in an injured hypercube. This indicates the balanced hypercube is more suitable for increasingly complex network fault-tolerant routing.

In order to make it more intuitive, we give a comparison diagram with the conclusion of [32] (see Figure 6). The contrast diagram expresses that, when , the length of fault tolerant routing is equal to whether it is in hypercube networks or in balanced hypercube networks; however, when , we can see that the longest length of fault tolerant routing in balanced hypercube networks is two hops less than that in hypercube networks; it means the message routing in a hurt balanced hypercube network is more efficiency than that in an injured hypercube. This indicates that the balanced hypercube is more suitable for increasingly complex network fault-tolerant routing.

6. Conclusion

When considering a model for a large-scale data center network, it is worth mentioning that as a deformation of a hypercube; the balanced hypercube is a better fit for its strong connectivity, regularity, and a smaller diameter. We propose the balanced hypercube as a model for the DCN and design an efficient fault-tolerant security routing of -based DCNs, in which the shortest feasible paths are located such that the reliable routing is achieved. Then, we claim that the length of fault-tolerant security routing of data center networks based on balanced hypercube is always no more than ; in other words, our fault-tolerant secure routing scheme is more effective in the same reliable network environment of DCNs.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to thank the National Natural Science Foundation of China (nos. U1905211, 62 171 132, and 62 102 088).

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Copyright © 2021 Xinxin Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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