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Nowadays erasure coding is one of the most significant techniques in cloud storage systems, which provides both quick parallel I/O processing and high capabilities of fault tolerance on massive data accesses. In these systems, triple disk failure tolerant arrays (3DFTs) is a typical configuration, which is supported by several classic erasure codes like Reed-Solomon (RS) codes, Local Reconstruction Codes (LRC), Minimum Storage Regeneration (MSR) codes, etc. For an online recovery process, the foreground application workloads and the background recovery workloads are handled simultaneously, which requires a comprehensive understanding on both two types of workload characteristics. Although several techniques have been proposed to accelerate the I/O requests of online recovery processes, they are typically unilateral due to the fact that the above two workloads are not combined together to achieve high cost-effective performance.To address this problem, we propose Erasure Codes Fusion (EC-Fusion), an efficient hybrid erasure coding framework in cloud storage systems. EC-Fusion is a combination of RS and MSR codes, which dynamically selects the appropriate code based on its properties. On one hand, for write-intensive application workloads or low risk on data loss in recovery workloads, EC-Fusion uses RS code to decrease the computational overhead and storage cost concurrently. On the other hand, for read-intensive or frequent reconstruction in workloads, MSR code is a proper choice. Therefore, a better overall application and recovery performance can be achieved in a cost-effective fashion. To demonstrate the effectiveness of EC-Fusion, several experiments are conducted in hadoop systems. The results show that, compared with the traditional hybrid erasure coding techniques, EC-Fusion accelerates the response time for application by up to 1.77×, and reduces the reconstruction time by up to 69.10%.