Abstract
In this paper, we address the problem of implementing distributed slice allocation algorithms for tactical networks deployed in regions lacking a communication infrastructure. We propose a multi-agent game implementation in which Application Slice Agents (ASAs) compete for shared computational and bandwidth resources in the network. We prove that the game can be implemented as a two stage potential game, with the first stage ensuring the selection of computational servers with load balancing, and the second stage improving connectivity and network throughput by promoting inter-slice cooperation. Our proposed analysis framework also incorporates a theoretical model to capture the impact of mitigation methods for timing channel leaks for the shared resources for different slices, and proposes a theoretical framework that allows for a security-delay tradeoff analysis.











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This work was supported in part by CACI International, Inc. (CACI). The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of CACI.
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Appendices
Appendix A: Proof of Theorem 1
We construct the potential function as:
We show that this is an exact potential function for the game.
Let a single slice agent i change its strategy from \(a_i\) to \(b_i\), the change in potential function \(\bigtriangleup \Phi\) should exactly be equal to the change in the players’ utility \(\bigtriangleup u_i\): i.e., \(\bigtriangleup \Phi = \bigtriangleup U\).
Let,
Therefore, (A.2) can be written as,
Then,
Let,
Then, (A.5) and (A.6) can be written as,
Because \(Q(a_{-i})\) is not influenced by the strategy changing of player i from \(a_i\) to \(b_i\), the equation of \(\bigtriangleup \Phi\) can be written as,
Let,
Therefore, (A.10) can be written as,
According to (A.3) and (A.12), it is obvious that \(U_i^1 = T_1\). Therefore, (7) holds if \(U_i^2 = T_2\).
Considering,
where the second equality comes from exchanging two sums.
Let,
We can write (A.14) as,
We enumerate and discuss all the cases to check if each \(j \epsilon F\) in \(a_i\) and \(b_i\), then we can get:
-
Case 1:
If j is in both \(a_i\) and \(b_i\), then \(n_j(b_i, a_{-i}) = n_j(a_i, a_{-i})\). We can get:
$$\begin{aligned} E(a_i, b_i) = 0 \end{aligned}$$(A.17) -
Case 2:
If j is in \(a_i\) but not in \(b_i\). then \(n_j(a_i, a_{-i}) = n_j(b_i, a_{-i}) + 1\). We can get:
$$\begin{aligned} E(a_i, b_i) = n_j(a_i, a_{-i}) \end{aligned}$$(A.18) -
Case 3:
If j is in \(b_i\) but not in \(a_i\). then \(n_j(b_i, a_{-i}) = n_j(a_i, a_{-i}) + 1\). We can get:
$$\begin{aligned} E(a_i, b_i) = -n_j(b_i, a_{-i}) \end{aligned}$$(A.19)
Therefore, We can write (A.16) as,
According to (A.4) and (A.20), we can see that \(U_i^2 = T_2\). This proves that (7) holds for the game \(\langle P, F, A, (u_i)_{i \epsilon P}^I \rangle\), and the game is an exact potential game.
Appendix B: Proof of Theorem 2
We construct the potential function as:
We show that this is an exact potential function for the game.
Let a single slice agent i change its strategy from \(a_i\) to \(b_i\), the change in potential function \(\bigtriangleup \Phi\) should exactly be equal to the change in the players’ utility \(\bigtriangleup U_i\): i.e., \(\bigtriangleup \Phi = \bigtriangleup U\).
Let,
Therefore, (B.2) can be written as,
Then,
Let,
Then, (B.6) and (B.7) can be written as,
Because \(Q(a_{-i})^{II}\) is not influenced by the strategy changing of player i from \(a_i\) to \(b_i\), the equation of \(\bigtriangleup \Phi\) can be written as,
Let,
Therefore, (B.11) can be written as,
According to (B.3) and (B.12), it is obvious that \(U_i^3 = T_3\). Therefore, (14) holds if \(U_i^4 = T_4\).
Referring to the proof steps in Theorem 1, we follow the same steps as (A.14)-(A.20) and prove that \(U_i^4 = T_4\).
Consequently, (14) holds for the game \(\langle P, F, A, (u_i)_{i \epsilon P}^{II} \rangle\).
Appendix C: Distributed Algorithms


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Yang, F., Comaniciu, C., Krishnan, P. et al. A Game Theoretic Framework for Distributed Mission Slice Allocation and Management for Tactical Networks. J Netw Syst Manage 31, 23 (2023). https://doi.org/10.1007/s10922-022-09709-w
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DOI: https://doi.org/10.1007/s10922-022-09709-w