# Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage
## 2 Related Work  
With the development of cloud computing technology, researchers have put forward some new system architectures based on cloud computing technology to meet the growing demand for video surveillance system. [PERSON] et al. [8] proved cloud computing could be a new deployment solution for video surveillance system. [PERSON] [9] investigated the reliability cloud-based video surveillance technology, but there is no real breakthrough in data management. In [10], [PERSON] et al. proposed a video surveillance system under IaaS abstraction layer. The system is based on the Hadoop distributed file system to provide scalable video recording and backup capabilities. [PERSON] analyzed the suitability of cloud-based multimedia surveillance systems and proposed a cloud-based multimedia surveillance system framework [11; 12; 13]. The system can efficiently deal with system overload, meet the storage requirements of the large-scale monitoring system, and provide data access to users. [PERSON] et al. proposed the Racki selection algorithm and DNik selection algorithm to guarantee the cluster load balance [14]. The two algorithms can fully consider cluster load balance when providing the data replicas pre-written into HDFS reasonable DataNodes. However, many unimportant data occupy a lot of space resource. Big data stream mobile computing (BDMSC) has some challenges in performing real-time energy-efficient management of the distributed resources available at both mobile devices and Internet-connected data centers. A fundamental problem for coping with the variable volume of data generated by the emerging BDMSC applications is the design of integrated computing-networking technological platforms [15]. [PERSON] et al. [16] proposed a replicas mechanism based on computing capacity of each node in HDFS. Similarly, inactive data in the system still causes the consumption of storage resources. [PERSON] et al. [17] proposed a distributed Fog-supported IoE-based framework. This approach saves energy consumption impressively in the Fog Data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm. [PERSON] et al. [18] implemented a hotness-proportional replication strategy (HP) to improve the efficiency of storage space. [PERSON] et al. [19] presented a cost-effective, dynamic replication management scheme referred to as CDRM. CDRM maintains minimal replica number for a given availability requirement. However, they did not address the replica replacement issue. [PERSON] [20] presented adaptive data replication strategy (ADRS), including a new replica placement and replacement strategy. They implemented ADRS and evaluation results apparently show that ADRS can improve the performance of cloud storage. However, in the massive video surveillance storage system, the retention time of video file will also affect the replica factor, so we should consider this factor. [PERSON] et al. [21] proposed a cache mechanism based on multi-agents that can automatically manage related video streams. Besides, they designed a caching framework to implement the cache interaction, cache activity control, and video streaming migration. However, the framework is too complicated and costly. [PERSON] and [PERSON] [22] used a prefetching mechanism to support higher quality cache streaming during subsequent playback of the hierarchically encoded video stream. However, when users play back the video, they pay more attention to the speed of data querying. [PERSON] et al. [23] presented a Cost-Effective Cloud Storage Caching Strategy referred to as CloudCache. They first utilized nearly free desktop machines in a local area network environment to build a local distributed file system, which is deployed as a data cache of remote cloud storage service. After that, they presented a cache replacement and file reading/writing algorithm. Performance evaluation is accomplished, using Amazon Simple Storage Service (S3) and desktop PCs in the laboratory to build the experimental environment.