Abstract Along with the rapid development of B5G/6G, the number of applications grows rapidly and the data amount explodes exponentially, putting a massive burden on the resource‐limited edge servers. To fully utilize the limited resources, virtualization technology is introduced to provide elastic deployment for applications in edge servers. But for I/O‐intensive applications, allocating elastic resources is not as easy as for compute‐intensive ones, because the amount of required I/O resources is unknown due to the request uncertainty. Many existing researches try to solve this multi‐application deployment problem by peaks clipping and valleys filling, to resource utilization. However, in fact, the times of peaks and valleys of most hybrid deployed applications are similar to each other, which invalidates those traditional solutions. To address this challenge, the actual data is analysed and complementary peak and valley periods in time and space dimensions are found. Based on this finding, an elastic hybrid deployment strategy HybridDep is proposed, for multiple I/O‐intensive applications. Validated by simulation experiments using real datasets and traces, this algorithm can reduce about 3.2% deployment cost than the compared algorithm.

In recent years, with the maturity of 5G and Internet of Things technologies, the traffic in mobile network is growing explosively. To reduce the burden of cloud data centers and CDN network, edge servers that are closer to users are widely deployed, caching hot contents and providing higher Quality of Service (QoS) by shortening access latency. Storage resources on edge servers are much limited compared with CDN servers, so the research on cache replacement strategy of edge servers is critical to edge computing and storage area. Many efforts have been made to improve caching performance on edge servers. Existing caching strategies only focus on the high access frequency period to solve the caching problem, they ignore low access frequency period with two characteristics, including that hot contents are difficult to predict and hot topics usually change unstably, which makes it inefficient to improve the hit rate on edge servers.In this paper, we deeply analyzed the real traces from Chuang-Cache and found some specific user groups are playing more important roles than general users during low access frequency period, and the contents accessed by these specific user groups have a much higher possibility to become hot contents. Therefore, we firstly classify such users to core users, and treat others as common users. Then we adopt the principal component analysis algorithm to analyze the relationship between hot contents and core users. On this basis, we finally propose a hot contents pre-cache protection mechanism, which is a significant part of our cache replacement algorithm CRATES. To improve CRATES’s efficiency, we extract key part of historical data by designing a sliding window method. Through a series of experiments using real application data, we demonstrate that CRATES reaches about 98% in caching hit rate and outperforms the state-of-the-art algorithm LRB by 1.4X.