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Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO. [ABSTRACT FROM AUTHOR]

The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station's storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods. [ABSTRACT FROM AUTHOR]