Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

Abstract

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.

Joint computation and bandwidth resources allocation for generalized computing model in mobile edge computing

“…The input-bits of the tasks l i is yield the uniform distribution of interval ð0; 1Þ Mbits, the required CPU cycles of users w i satisfy the discrete uniform distribution of interval ½1; 3 Â 10 9 cycles. The reasonability of the selected parameters above is supported by the references [4] and [7]. Different values of the above parameters will influence the performance of the proposed model.…”

“…For a single-user MEC system, [3,4] presented task offloading schedule approaches. In multi-user case, it is crucial on how to jointly allocate radio and computation resources [5,6,7].…”

Mecanismo de Comunicação para Migração de Serviços Ciente da Localização de Nuvem e Névoas

“…Para fornecer um serviço de nuvem de borda móvel flexível, foi proposto em [Chen et al 2018] um framework, que consiste em um mecanismo de computação com eficiência de recursos para usuários e um mecanismo de alocação de recursos de comunicação e computação para o operador de rede. O primeiro mecanismo estuda o problema de transferência de computação com eficiência de recursos para um usuário, enquanto o segundo aborda o problema crítico do controle de admissão de usuários para alocação de recursos, a fim de selecionar corretamente o conjunto de usuários para satisfação da demanda de recursos.…”

Task admission control for application service operators in mobile cloud computing

“…They formulated the admission control problem to a multi-choice integer program and utilized Ben's genetic algorithm to solve it. Chen et al proposed a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation resource allocation mechanism for network operator [25]. They formulated the admission control problem as a NP-hard optimization problem and designed an approximation algorithm based on the user ranking criteria to develop the admission policy.…”