Liqing Liu's research while affiliated with Northeast University At Qinhuangdao Campus and other places

In this work, we propose a dynamic optimization scheme for an edge computing system with multiple users, where the radio and computational resources, and offloading decisions, can be dynamically allocated with the variation of computation demands, radio channels and the computation resources. Specifically, with the objective to minimize the energy consumption of the considered system, we propose a joint computation offloading, radio and computational resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, the main problem is divided into several sub-problems at each time slot and are addressed separately. The simulation results demonstrate the effectiveness of the proposed scheme.

Fog computing system emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this paper, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization to minimize the system cost. Through performance evaluation, the effectiveness of the proposed scheme can be verified.

In the mobile cloud computing (MCC), although offloading requests to the distant central cloud or nearby cloudlet can reduce energy consumption at the mobile devices (MDs), it may also incur a large execution delay including transmission time from the MDs to the servers and waiting time at the servers. Therefore, how to balance the energy consumption and delay performance is of great research importance. In this paper, we bring a thorough study on the energy consumption and execution delay of offloading process in a cloudlet-assisted MCC. Specifically, heterogeneity of request executions are explicitly considered. When there is a small cell base station (SBS) available, the MDs can connect with cloudlet via the SBS and if only a macro cell base station is available, the MD can connect with the central cloud through it. We derive the analytic results of the energy consumption and execution delay performance with the assumption of three different queue models at the MD, cloudlet and central cloud. Based on the theoretical analysis, the multi-objective optimization problems are formulated with the joint objectives to minimize the energy consumption and delay by finding the optimal offloading probability. The simulation results demonstrate the effectiveness of the proposed scheme. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting techniques, the system performance could be further improved. In this paper, we take the social relationships of the energy harvesting MDs into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with energy harvesting MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a Generalized Nash Equilibrium Problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical NEP and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can offload those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to offload sets the price strategies for different SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme.

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet-of-Things (IoT). In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied respectively to the MD, fog and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multi-objective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.