5,256 research outputs found
Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching
Fog computing is a promising architecture to
provide economical and low latency data services for future
Internet of Things (IoT)-based network systems. Fog computing
relies on a set of low-power fog nodes (FNs) that are located
close to the end users to offload the services originally targeting
at cloud data centers. In this paper, we consider a specific
fog computing network consisting of a set of data service operators
(DSOs) each of which controls a set of FNs to provide the
required data service to a set of data service subscribers (DSSs).
How to allocate the limited computing resources of FNs to all
the DSSs to achieve an optimal and stable performance is an
important problem. Therefore, we propose a joint optimization
framework for all FNs, DSOs, and DSSs to achieve the optimal
resource allocation schemes in a distributed fashion. In the
framework, we first formulate a Stackelberg game to analyze
the pricing problem for the DSOs as well as the resource allocation
problem for the DSSs. Under the scenarios that the DSOs
can know the expected amount of resource purchased by the
DSSs, a many-to-many matching game is applied to investigate
the pairing problem between DSOs and FNs. Finally, within the
same DSO, we apply another layer of many-to-many matching
between each of the paired FNs and serving DSSs to solve
the FN-DSS pairing problem. Simulation results show that our
proposed framework can significantly improve the performance
of the IoT-based network systems
Optimal Resource Allocation in Ultra-low Power Fog-computing SWIPT-based Networks
In this paper, we consider a fog computing system consisting of a
multi-antenna access point (AP), an ultra-low power (ULP) single antenna device
and a fog server. The ULP device is assumed to be capable of both energy
harvesting (EH) and information decoding (ID) using a time-switching
simultaneous wireless information and power transfer (SWIPT) scheme. The ULP
device deploys the harvested energy for ID and either local computing or
offloading the computations to the fog server depending on which strategy is
most energy efficient. In this scenario, we optimize the time slots devoted to
EH, ID and local computation as well as the time slot and power required for
the offloading to minimize the energy cost of the ULP device. Numerical results
are provided to study the effectiveness of the optimized fog computing system
and the relevant challenges
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