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An Adaptable Pricing-Based Resource Allocation Scheme Considering User Offloading Needs in Edge Computing
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AI
DOI：10.1109/JIOT.2024.3464641.png)
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Abstract
En 中文
Multiaccess edge computing (MEC) is extensively utilized within the Internet of Things (IoT), wherein end-users pay services to meet the latency demands of their respective tasks. The pricing is impacted not solely by the quantity of data offloaded by the user but also associated with the leased computing and communication resources. Nevertheless, prevailing pricing strategies seldom account for the personalized resource requisites during user offloading. In this article, we present an adaptive pricing-oriented approach for concomitant task offloading and resource allocation, considering hybrid resources, comprising two key components. First, we propose a differential pricing framework for communication and computation resources, where the unit price will be influenced by the proportion of resources rented by users. Subsequently, we design a two-stage Stackelberg game model: 1) employing convex optimization theory to mitigate problem intricacies and 2) employing gradient descent to ascertain the potentially optimal price, thus achieving a balance between minimizing user expenses and maximizing server profitability. Simulation outcomes demonstrate that our approach slashes user costs by 23.3% and enhances average server revenue by 65.6% compared to a flat pricing model with a high-user request rate (five user-initiated requests per 100 ms). This maintains server occupancy within 60% to 80%, thereby alleviating user queuing and refining user Quality of Experience (QoE).
Keywords:
Pricing
Resource management
Servers
Costs
Internet of Things
Optimization
Games
Computational offloading
multiple-access edge computing (MEC)
offloading decision
pricing
resources allocation
Stackelberg game
Journal
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8.9
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1.4W
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7.8W