Memcached is a widely adopted, high-performance, in-memory key-value object caching system utilized in data centers. Nonetheless, its data is stored in volatile DRAM, making the cached data susceptible to loss during system shutdowns. Consequently, cold restarts experience significant delays. Persistent memory is a byte-addressable, large-capacity, and non-volatility storage media, which can be employed to avoid the cold restart problem. However, deploying Memcached on persistent memory requires consideration of issues such as write endurance, asymmetric read/write latency and bandwidth, and write granularity of persistent memory. In this paper, we propose Hybrid-Memcached, an optimized Memcached framework based on a hybrid combination of DRAM and persistent memory. Hybrid-Memcached includes three key components: (1) a DRAM-based data aggregation buffer to avoid multiple fine-grained writes, which extends the write endurance of persistent memory, (2) a data-object alignment mechanism to avoid write amplification, and (3) a non-temporal store instruction-based writing strategy to improve the bandwidth utilization. We have implemented Hybrid-Memcached on the Intel Optane persistent memory. Several micros-benchmarks are designed to evaluate Hybrid-Memcached by varying read/write ratios, access distributions, and key-value item sizes. Additionally, we evaluated it with the YCSB benchmark, showing a 21.2% performance improvement for fully write-intensive workloads and 11.8% for read-write balanced workloads.

This letter presents a lossless compression algorithm for hyperspectral images, which is based on the strength of correlations between bands. First, a searching model is constructed using the tree structure. Second, multibands which have strong correlations to each chosen band are found out and are then used to predict the chosen band in a couple-group manner. Lastly, residual images are encoded using entropy coders. Experimental results show that our compression algorithm provides a competitive compression performance compared with most existing compression algorithms.