As a special scenario of mobile cloud computing, mobile edge computing can meet the requirements of low latency of data integrity verification and support of mobility in mobile scenarios. However, most existing data integrity verification methods have relatively large computational overhead and few considerations of data dynamic update. To address the above problems, we propose a lightweight data integrity verification method that can support data dynamics in mobile edge computing scenarios. The proposed method is based on an algebraic signature and data integrity verification framework, which ensures security and reduces the computational overhead to achieve the requirement of lightweight. On this basis, analysis and proof of the feasibility, security, and privacy are given. At the same time, in order to support the dynamic update of the data, an optimized strategy based on matrix index is designed with low overhead. In comparison with other baseline methods, simulation experiments show that our method is superior in terms of computational overhead and has good performance in supporting data dynamics.
How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.
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