A Market-Based Framework for Multi-Resource Allocation in Fog Computing

Abstract

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 work extends the General Equilibrium literature by considering a practical case of satiated utility functions. Also, 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 work 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.

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“…where µ i,r := λ i,r + q i,r . Multiplying both sides of (43) by x n,l /w * n,l , and using the inequality in (41), result in…”

Energy-based Proportional Fairness in Cooperative Edge Computing

“…( 24) and 4 constraints for the resources requirements at the edge node as (C 2j ), (C 3j ), (C 4j ), and (C 9j ) described in Eq. (24). In the worst case, (SP 2 ) is assigned all |Φ| tasks, and thus it has at most 4|Φ| resources allocation variables and (|Φ| + 4) constraints.…”

“…Thus, We will study how the numbers of UEs/tasks and the percentage of available resources at ENs affect the fairness, energy benefits, and total consumed energy of all UEs. The work [24] used the envy-free index (EF) to evaluate the fairness of game-based problems. However, the EF index is only suitable for the resource allocation problems that are formed as multi-agent problems.…”

“…Similarly, the authors of [22] introduce a heuristic scheme for computing resource allocation that can mitigate the effect of selfish mobile users. Lately, a few works investigate fairness [23], [24] using market equilibrium approaches. These papers rely on the game theory and market-based frameworks, which design the price for resources in a multiple edge node and budgetconstrained buyer environment.…”

“…These papers rely on the game theory and market-based frameworks, which design the price for resources in a multiple edge node and budgetconstrained buyer environment. However, the market-based framework is only applicable to the two-layer model (i.e., UEs and edge node layers) [23], [24]. Second, these approaches cannot capture the coupling among different types of resources (i.e., the task duration depends on the uplink, downlink, and computation resources).…”