TY - JOUR
T1 - A Market-Based Framework for Multi-Resource Allocation in Fog Computing
AU - Nguyen, Duong Tung
AU - Le, Long Bao
AU - Bhargava, Vijay K.
N1 - Funding Information:
Manuscript received July 25, 2018; revised December 31, 2018 and March 25, 2019; accepted April 6, 2019; approved by IEEE/ACM TRANS-ACTIONS ON NETWORKING Editor J. Huang. Date of publication April 26, 2019; date of current version June 14, 2019. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada and in part by the Vanier Scholarship. (Corresponding author: Duong Tung Nguyen.) D. T. Nguyen and V. K. Bhargava are with the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail: duongnt@ece.ubc.ca; vijayb@ece.ubc.ca).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Fog computing is transforming the network edge into an intelligent platform by bringing storage, computing, control, and networking functions closer to end users, things, and sensors. How to allocate multiple resource types (e.g., CPU, memory, bandwidth) of capacity-limited heterogeneous fog nodes to competing services with diverse requirements and preferences in a fair and efficient manner is a challenging task. To this end, we propose a novel market-based resource allocation framework in which the services act as buyers and fog resources act as divisible goods in the market. The proposed framework aims to compute a market equilibrium (ME) solution at which every service obtains its favorite resource bundle under the budget constraint, while the system achieves high resource utilization. This paper extends the general equilibrium literature by considering a practical case of satiated utility functions. In addition, we introduce the notions of non-wastefulness and frugality for equilibrium selection and rigorously demonstrate that all the non-wasteful and frugal ME are the optimal solutions to a convex program. Furthermore, the proposed equilibrium is shown to possess salient fairness properties, including envy-freeness, sharing-incentive, and proportionality. Another major contribution of this paper is to develop a privacy-preserving distributed algorithm, which is of independent interest, for computing an ME while allowing market participants to obfuscate their private information. Finally, extensive performance evaluation is conducted to verify our theoretical analyses.
AB - Fog computing is transforming the network edge into an intelligent platform by bringing storage, computing, control, and networking functions closer to end users, things, and sensors. How to allocate multiple resource types (e.g., CPU, memory, bandwidth) of capacity-limited heterogeneous fog nodes to competing services with diverse requirements and preferences in a fair and efficient manner is a challenging task. To this end, we propose a novel market-based resource allocation framework in which the services act as buyers and fog resources act as divisible goods in the market. The proposed framework aims to compute a market equilibrium (ME) solution at which every service obtains its favorite resource bundle under the budget constraint, while the system achieves high resource utilization. This paper extends the general equilibrium literature by considering a practical case of satiated utility functions. In addition, we introduce the notions of non-wastefulness and frugality for equilibrium selection and rigorously demonstrate that all the non-wasteful and frugal ME are the optimal solutions to a convex program. Furthermore, the proposed equilibrium is shown to possess salient fairness properties, including envy-freeness, sharing-incentive, and proportionality. Another major contribution of this paper is to develop a privacy-preserving distributed algorithm, which is of independent interest, for computing an ME while allowing market participants to obfuscate their private information. Finally, extensive performance evaluation is conducted to verify our theoretical analyses.
KW - General equilibrium
KW - fog computing
KW - multi-resource allocation
KW - privacy-preserving distributed optimization
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U2 - 10.1109/TNET.2019.2912077
DO - 10.1109/TNET.2019.2912077
M3 - Article
AN - SCOPUS:85067570173
SN - 1063-6692
VL - 27
SP - 1151
EP - 1164
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 3
M1 - 8700615
ER -