Abstract
Distributed computing network-systems are modeled as directed/undirected graphs with vertices representing compute elements and adjacency-edges capturing their uni- or bi-directional communication. To quantify an intuitive tradeoff between two graph-parameters: minimum vertex-degree and diameter of the underlying graph, we formulate an extremal problem with the two parameters: for all positive integers n and d, the extremal value \(\nabla (n, d)\) denotes the least minimum vertex-degree among all connected order-n graphs with diameters of at most d. We prove matching upper and lower bounds on the extremal values of \(\nabla (n, d)\) for various combinations of n- and d-values.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ayaso, O., Shah, D., Dahleh, M.A.: Information theoretic bounds for distributed computation over networks of point-to-point channels. IEEE Trans. Inf. Theory 56(12), 6020–6039 (2010)
Bondy, J.A., Murty, U.S.R.: Graph Theory. Graduate Texts in Mathematics, vol. 244. Springer London (2008)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)
Dai, H.K., Toulouse, M.: Lower bound for function computation in distributed networks. In: Dang, T.K., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds.) FDSE 2018. LNCS, vol. 11251, pp. 371–384. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03192-3_28
Dai, H.K., Toulouse, M.: Lower bound on network diameter for distributed function computation. In: Dang, T.K., Küng, J., Takizawa, M., Bui, S.H. (eds.) FDSE 2019. LNCS, vol. 11814, pp. 239–251. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35653-8_16
Dai, H.K., Toulouse, M.: Lower-bound study for function computation in distributed networks via vertex-eccentricity. Springer Nat. Comput. Sci. 1(1), 10:1–10:14 (2019)
Fich, F.E., Ruppert, E.: Hundreds of impossibility results for distributed computing. Distrib. Comput. 16(2–3), 121–163 (2003)
Hendrickx, J.M., Olshevsky, A., Tsitsiklis, J.N.: Distributed anonymous discrete function computation. IEEE Trans. Autom. Control 56(10), 2276–2289 (2011)
Kashyap, A., Basar, T., Srikant, R.: Quantized consensus. Automatica 43(7), 1192–1203 (2007)
Katz, G., Piantanida, P., Debbah, M.: Collaborative distributed hypothesis testing. Computing Research Repository, abs/1604.01292 (2016)
Kuhn, F., Moscibroda, T., Wattenhofer, R.: Local computation: lower and upper bounds. J. ACM 63(2), 17:1–17:44 (2016)
Olshevsky, A., Tsitsiklis, J.N.: Convergence speed in distributed consensus and averaging. SIAM J. Control Optim. 48(1), 33–55 (2009)
Raceala-Motoc, M., Limmer, S., Bjelakovic, I., Stanczak, S.: Distributed machine learning in the context of function computation over wireless networks. In: Matthews, M.B. (ed.) 52nd Asilomar Conference on Signals, Systems, and Computers, ACSSC 2018, Pacific Grove, CA, USA, 28–31 October 2018, pp. 291–297. IEEE (2018)
Sundaram, S.: Linear iterative strategies for information dissemination and processing in distributed systems. Ph.D. thesis, University of Illinois at Urbana-Champaign (2009)
Sundaram, S., Hadjicostis, C.N.: Distributed function calculation and consensus using linear iterative strategies. IEEE J. Sel. Areas Commun. 26(4), 650–660 (2008)
Sundaram, S., Hadjicostis, C.N.: Distributed function calculation via linear iterative strategies in the presence of malicious agents. IEEE Trans. Autom. Control 56(7), 1495–1508 (2011)
Toulouse, M., Minh, B.Q.: Applicability and resilience of a linear encoding scheme for computing consensus. In: Muñoz, V.M., Wills, G., Walters, R.J., Firouzi, F., Chang, V. (eds.) Proceedings of the Third International Conference on Internet of Things, Big Data and Security, IoTBDS 2018, Funchal, Madeira, Portugal, 19–21 March 2018, pp. 173–184. SciTePress (2018)
Toulouse, M., Minh, B.Q., Minh, Q.T.: Invariant properties and bounds on a finite time consensus algorithm. Trans. Large-Scale Data Knowl. Centered Syst. 41, 32–58 (2019)
Wang, L., Xiao, F.: Finite-time consensus problems for networks of dynamic agents. IEEE Trans. Autom. Control 55(4), 950–955 (2010)
Xiao, L., Boyd, S.P., Kim, S.-J.: Distributed average consensus with least-mean-square deviation. J. Parallel Distrib. Comput. 67(1), 33–46 (2007)
Xu, A.: Information-theoretic limitations of distributed information processing. Ph.D. thesis, University of Illinois at Urbana-Champaign (2016)
Xu, A., Raginsky, M.: Information-theoretic lower bounds for distributed function computation. IEEE Trans. Inf. Theory 63(4), 2314–2337 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, H.K., Toulouse, M. (2020). Relating Network-Diameter and Network-Minimum-Degree for Distributed Function Computation. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. FDSE 2020. Lecture Notes in Computer Science(), vol 12466. Springer, Cham. https://doi.org/10.1007/978-3-030-63924-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-63924-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63923-5
Online ISBN: 978-3-030-63924-2
eBook Packages: Computer ScienceComputer Science (R0)