Abstract: A distributed task offloading and computing resources management method based on energy harvesting is provided, including: establishing a task local computing model and an edge cloud computing model; establishing a device maximum benefit objective function based on the perturbation Lyapunov optimization algorithm and a mobile edge computing server maximum benefit objective function; pre-selecting, by the device based on a pre-screening criteria, a mobile edge computing server for task offloading; calculating an optimal task size strategy for performing task offloading by the device to the selected mobile edge computing server by using a Lagrange multiplier algorithm and a KKT condition; obtaining an optimal quotation strategy of the mobile edge computing server for the device in each of time slots; and obtaining a solution of the optimal task size strategy meeting a Stackelberg equilibrium and a solution of the optimal dynamic quotation strategy meeting the Stackelberg equilibrium as a resource allocation str

Abstract: A distributed task offloading and computing resources management method based on energy harvesting is provided, including: establishing a task local computing model and an edge cloud computing model; establishing a device maximum benefit objective function based on the perturbation Lyapunov optimization algorithm and a mobile edge computing server maximum benefit objective function; pre-selecting, by the device based on a pre-screening criteria, a mobile edge computing server for task offloading; calculating an optimal task size strategy for performing task offloading by the device to the selected mobile edge computing server by using a Lagrange multiplier algorithm and a KKT condition; obtaining an optimal quotation strategy of the mobile edge computing server for the device in each of time slots; and obtaining a solution of the optimal task size strategy meeting a Stackelberg equilibrium and a solution of the optimal dynamic quotation strategy meeting the Stackelberg equilibrium as a resource allocation str

Abstract: A task offloading and resource allocation method in an uncertain network environment is provided. A task offloading process is modeled as a two-stage offloading model. The model is optimized to a task offloading and resource allocation problem based on two-stage stochastic programming. Based on a stochastic simulation algorithm, the task offloading and resource allocation problem is transformed to a sample mean approximation problem. The sample mean approximation problem is decoupled to a local computing resource allocation sub-problem, a transmission power and edge computing resource joint allocation sub-problem, and an offloading decision sub-problem. The three sub-problems are solved respectively by using a standard Lagrange multiplier algorithm, by using a genetic algorithm, and by analyzing delay estimation and energy consumption budget of local computing and delay estimation and energy consumption budget of edge computing.

Abstract: A distributed computation offloading method based on computation-network collaboration in a stochastic network is provided. The distributed computation offloading method includes: building a device revenue maximization problem model and a MEC server revenue maximization problem model based on a local computing model and an edge cloud computing model; building composite sets of scenarios for dwell time and waiting latency based on a random movement of a user and burst computation demands; compensating for a game strategy by using a posteriori recourse action and building a game-based stochastic programming model between the device and the MEC server; transforming a multi-stage stochastic regularization problem for both the device and the MEC server into a DEP problem by constructing a scenario tree, and solving the DEP problem to obtain an optimal task strategy for the offloading from the MEC server and an optimal offering strategy of the MEC server to the device.

Abstract: A distributed computation offloading method based on computation-network collaboration in a stochastic network is provided. The distributed computation offloading method includes: building a device revenue maximization problem model and a MEC server revenue maximization problem model based on a local computing model and an edge cloud computing model; building composite sets of scenarios for dwell time and waiting latency based on a random movement of a user and burst computation demands; compensating for a game strategy by using a posteriori recourse action and building a game-based stochastic programming model between the device and the MEC server; transforming a multi-stage stochastic regularization problem for both the device and the MEC server into a DEP problem by constructing a scenario tree, and solving the DEP problem to obtain an optimal task strategy for the offloading from the MEC server and an optimal offering strategy of the MEC server to the device.