Non-Inclusive 1-Good-Neighbor Diagnosability of Augmented Cubes ()
1. Introduction
The exponential growth of data volume and the increasing demand for high-performance computing have driven the rapid development of large-scale parallel processing systems. The interconnection network, as the communication backbone of multiprocessor systems, directly determines the efficiency and reliability of the entire system. Its topology, which describes the connection pattern between processors, plays a pivotal role in influencing system performance such as communication latency and fault tolerance [1] [2]. In practical operations, processor faults are inevitable due to hardware aging, environmental interference, or other factors. Therefore, accurately identifying faulty processors (i.e., fault diagnosis) is essential to ensure the continuous and stable operation of the system [3].
To address the fault diagnosis problem, two classic diagnosis models have been proposed: the PMC model [1] and the MM* model [2]. The PMC model diagnoses faults through mutual testing between adjacent processors, while the MM* model relies on comparison-based testing. For these models, two key propositions for distinguishing fault sets have been widely recognized [3] [4]:
Proposition 1. [3] For any two distinct sets
,
and
are distinguishable under the PMC model if and only if there exists a vertex
and there exists a vertex
such that
(See Figure 1(a)).
Proposition 2. [4] For any two distinct sets
,
and
are distinguishable under the MM* model if and only if at least one of the following conditions is satisfied (See Figure 1(b)).
Figure 1. Illustration of Propositions 1.1 and 1.2.
(1) There exist three vertices
and
such that
.
(2) There exist three vertices
and
such that
.
(3) There exist three vertices
and
such that
.
In recent years, extensive research has been conducted on the diagnosability of various interconnection networks, including regular networks, matching composition networks, and lexicographic product networks [5]-[9]. As a generalized form of conditional diagnosability, the
-good-neighbor conditional diagnosability requires that each non-faulty vertex retains at least
neighbors within the non-faulty set, which more closely reflects the actual operating conditions of the system [10]. This metric has been applied to analyze the diagnosability of networks such as k-ary n-cubes, star graphs, and locally exchanged twisted cubes [11]-[13].
However, existing studies primarily focus on inclusive fault sets (i.e., one fault set is a subset of the other), while the probability of non-inclusive fault sets (i.e., neither
nor
) cannot be ignored in practical scenarios [14]. To fill this gap, the concept of non-inclusive diagnosability has been proposed, and relevant research has been carried out for networks such as alternating group graphs, hypercubes, and
-star networks [15]-[18]. The non-inclusive
-good-neighbor diagnosability, which combines the advantages of non-inclusive fault sets and
-good-neighbor constraints, provides a more comprehensive measure of the system’s diagnosis capability [19]. For instance, in large-scale data centers where processors are geographically distributed across multiple clusters, failures often occur independently due to localized issues such as power outages, cooling system malfunctions, or hardware aging. These failures typically affect separate clusters rather than concentrating around a single vertex’s neighborhood, resulting in fault sets that do not include all neighbors of any vertex. Similarly, in distributed computing networks spanning multiple regions, communication delays or regional network disruptions can lead to simultaneous but isolated failures, forming non-inclusive fault patterns. The non-inclusive
-good-neighbor diagnosability has been studied in interconnection networks, hypercubes [20] [14].
The augmented cube
is a promising interconnection network topology with superior properties such as high connectivity, symmetry, and fault tolerance [21]. Previous studies have investigated the diagnosability of
under different models, and derived results such as its conditional diagnosability and
-good-neighbor conditional diagnosability [22]-[25]. However, the non-inclusive 1-good-neighbor diagnosability of
remains unaddressed. This study aims to fill this research gap by systematically analyzing the structural characteristics of
and deriving the exact values of its non-inclusive 1-good-neighbor diagnosability under the PMC and MM* models.
2. Preliminaries
For a simple undirected graph
, let
and
denote the vertex set and edge set of
, respectively. For a vertex
,
represents the neighborhood of
(i.e., the set of vertices adjacent to
), and
denotes the degree of
. A graph
is called
-regular if
for all
. A vertex subset
is referred to as a vertex cut if
is disconnected.
The symmetric difference of two sets
and
is defined as
, which consists of elements that belong to exactly one of the two sets. Two sets
and
are defined as non-inclusive if and only if neither
is a subset of
nor
is contained in
, mathematically expressed as
and
.
Definition 1. [19] A system
is non-inclusive
-good-neighbor
-diagnosable if and only if
and
are distinguishable for any two distinct non-inclusive
-good-neighbor faulty subsets
and
of
with
and
. The non-inclusive
-good-neighbor diagnosability of
, denoted by
, is the maximum value of the integer
such that
is non-inclusive
-good-neighbor
-diagnosable.
Figure 2.
and
.
Definition 2. [26]
is a complete graph
with the vertex set
. For
,
is obtained by taking two copies of the augmented cube
, denoted by
and
, and adding
edges between the two as follows:
Let
and
. A vertex
of
is adjacent to
of
if and only if either
(1)
for
; or
(2)
for
, where
.
According to Definition 1 of augmented cubes, we write this recursive construction of
symbolically as
, where
and
. We call the edges between
and
crossed edges. Clearly, every vertex of
is incident with two crossed edges. For an
-bit binary string
, we use
(respectively,
) to denote the binary string
(respectively,
) which differs with
in the
th bit position (respectively, from the first to the
th bit positions). It is clear that
. We use
rather than
[26].
'The augmented cubes AQ2 and AQ3 are shown in Figure 2.
An alternative definition of
is given in the following.
Definition 3. [26] The augmented cube
of dimension
has
vertices. Each vertex is labeled by a unique
-bit binary string as its address. Two vertices
and
are joined iff either (1) There exists an integer
,
, such that
; in this case, the edge is called a hypercube edge of dimension
, denoted by
, or (2) There exists an integer
,
, such that
; in this case, the edge is called a complement edge of dimension
, denoted by
.
Clearly,
is
-regular. From the definition of
, we have the useful properties as follows.
Lemma 3. [27] Let a cycle of four nodes
in
, where
,
, and
.
for
.
Lemma 4. [26] Any two vertices in
have at most four common neighbors for
.
Lemma 5 [28] Let
be a vertex set of
(
).
1) If
, then
,
2) If
, then
,
3) If
, then
,
4) If
, then
.
Lemma 6. [27] Let
be a vertices set of
,
for
,
is either connected, or it consists of a large component, and all the small components contain at most 2 vertices.
Lemma 7. [28] Let
be a vertices set of
,
for
,
is either connected, or it consists of a large component, and all the small components contain at most 3 vertices.
Lemma 8. [28] Let
be a vertices set of
,
for
,
is either connected, or it consists of a large component, and all the small components contain at most 4 vertices.
Lemma 9. [29] Let
be a path of length two in
between
and
for
. Then
and
. Furthermore, if
, we have
.
Lemma 10. [30] A graph
has a perfect matching if and only if
for all
, where
is the number of connected components of
with odd order.
3. Non-Inclusive 1-Good-Neighbor Diagnosability of
3.1. Result under the PMC Model
In this section, we determine the non-inclusive
-good-neighbor diagnosability of
under the PMC model for
.
Lemma 11. Let
be an
-dimensional augmented cube with
. Then
.
Proof. Consider a 4-cycle
in
, where
,
, and
(the notation
denotes the vertex obtained by flipping the
-th bit of
). Let
; define two fault sets
and
. By Lemma 5,
for
. It is easy to verify that
, and neither
nor
, indicating that
and
are non-inclusive sets.
Since
and
, we have
and
; similarly,
implies
and
. Suppose there exists an isolated vertex
, then
. By Lemma 4,
for any
. Since
is
-regular,
when
, which implies that at least one neighbor of
is not in
. This contradiction shows that
and
, that is, each vertex in
and
has at least one good neighbors, so
and
are non-inclusive 1-good-neighbor fault sets.
According to the PMC model’s diagnosis rules,
and
are indistinguishable because there is no vertex
that can distinguish them. Thus,
is not non-inclusive 1-good-neighbor
-diagnosable, leading to
.
Lemma 12. Let
be an
-dimensional augmented cube with
. Then
.
Proof. Assume for contradiction that there exist two distinct non-inclusive 1-good-neighbor fault sets
and
of
with
and
, such that
and
are indistinguishable under the PMC model. First, we have
for
, which implies that
is non-empty.
Since
and
are 1-good-neighbor fault sets,
and
. For
,
, and
for
. This indicates that
contains a large connected component with at least 4 vertices.
Since
and
are indistinguishable, according to Proposition 1, all the vertices in
are not adjacent to each other in
, which is
. By Lemma 5,
when
, which implies
. However,
, which contradicts
for
. Thus, the lemma holds.
Theorem 13. For
, we have
.
Proof. The conclusion follows directly from Lemmas 11 and 12.
3.2. Result under the MM* Model
Lemma 14. For
, we have
.
Proof. Consider a path
of order 3 (with two edges) in
, where
and
(here,
denotes the vertex formed by flipping the
-th bit of
). Let
, and define two fault sets
and
. By Lemma 5, the size of
(i.e., the neighborhood of path
) is
.
It is straightforward to verify that
, and neither
is a subset of
nor
is contained in
, which confirms that
and
form a pair of non-inclusive sets. Since
and
, we have
and
; similarly,
implies
and
.
By the construction of
and
, the neighborhood of the vertex set
is entirely contained in
. For
,
, so Lemma 7 guarantees that
remains connected. Additionally, the size of
satisfies:
for
. This implies that
contains a large connected component, and thus
.
Under the MM* model,
and
are indistinguishable because there is no vertex outside
that can distinguish the two fault sets. Therefore,
is not non-inclusive 1-good-neighbor
-diagnosable, leading to the conclusion that
.
Lemma 15. For
, we have
.
Proof. Assume for contradiction that there exist two distinct non-inclusive 1-good-neighbor fault sets
and
of
such that
,
, and
and
are indistinguishable under the MM* model. Without loss of generality, suppose
and
.
According to Proposition 2, we can obtain the definition of the MM* model’s indistinguishability, that is, for any vertex
, the neighborhood
cannot contain vertices from both
and
simultaneously. Let
denote the set of vertices in
whose neighborhoods intersect with
. For
(the recursive decomposition of augmented cubes), we have
or
for any
.
By the structural properties of
,
and
. Since
is
-regular, the size of
satisfies:
However, for
,
, which contradicts the above inequality. Furthermore,
for
, implying the existence of a connected component
in
with
.
Since
and
are non-inclusive 1-good-neighbor fault sets,
and
. The contradiction arises from the fact that the structural constraints of
force
and
to be distinguishable under the MM* model. Thus,
.
Theorem 16. For
, we have
under the MM* model.
Proof. Combining the results of Lemmas 14 and 15, the non-inclusive 1-good-neighbor diagnosability of
under the MM* model is exactly
for
.
4. Discussion
The main results of this paper impose constraints on
:
for the PMC model and
for the MM* model. These constraints stem from two key aspects of the proof process: 1) The induction step relies on the minimum size of subgraph components in
. For smaller
, the subgraphs may not retain the required connectivity and expansion properties, leading to the failure of key inequalities in the proof. 2) The verification of 1-good-neighbor condition requires sufficient neighbors for each fault-free vertex. For
, the number of neighbors
is insufficient to guarantee at least one fault-free neighbor when facing the upper bound of fault sets.
Regarding the tightness of these constraints, we conjecture that they can be partially tightened. For example, preliminary analysis shows that the constraint for the MM* model may be reduced to
by optimizing the component size estimation in the proof. However, further verification requires refining the induction base and adjusting the fault set partitioning strategy, which will be the focus of future work.
Extending the current analysis to
-good-neighbor diagnosability with
faces three core challenges: 1) Fault set structure complexity: For
, each fault-free vertex must have at least two fault-free neighbors, which requires stricter control over the distribution of fault sets. Unlike
, the fault sets can no longer be concentrated in local regions, increasing the difficulty of partitioning and analysis. 2) Component connectivity requirements: The proof for
leverages the 1-extra connectivity of
, but for
, higher-order extra connectivity parameters (e.g., 2-extra connectivity) are needed and integrating this into diagnosability analysis requires new technical tools. 3. Model-specific constraints: Under the MM* model, the distinguishability conditions for
involve more complex test outcome combinations. The mutual testing between fault-free and faulty vertices requires more detailed case analysis, especially for non-adjacent fault-free vertices.
5. Conclusions
This study focuses on the non-inclusive 1-good-neighbor diagnosability of augmented cubes
, a class of interconnection networks with excellent topological properties. Through systematic analysis of the structural characteristics of
and rigorous mathematical proof, we derive the exact values of the non-inclusive 1-good-neighbor diagnosability under two classic fault diagnosis models: 1) Under the PMC model,
for
; 2) Under the MM* model,
for
.
These findings quantify the fault tolerance capability of augmented cubes in the context of non-inclusive fault sets and 1-good-neighbor constraints, providing valuable theoretical support for the design and optimization of reliable multiprocessor systems. The proof methods adopted in this study, including structural construction, proof by contradiction, and induction, can be extended to the analysis of other interconnection network topologies.
Future research directions may include extending the results to
(i.e., non-inclusive
-good-neighbor diagnosability) and investigating the non-inclusive diagnosability of other network structures such as folded hypercubes, alternating group graphs, and
-star networks. Additionally, exploring the non-inclusive diagnosability under more realistic fault models (e.g., intermittent fault models) could further advance the research on network reliability.