• Journal of Platform Technology
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• v.11 no.4
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• pp.50-61
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• 2023

The advancement of various big data technologies has had a tremendous impact on many industries. Diverse big data research studies have been conducted to process and analyze massive data quickly. Under these circumstances, new emerging technologies such as high-capacity persistent memory (PMEM) and Compute Express Link (CXL) have lately attracted significant attention. However, little investigation into a big data "search" platform has been made. Moreover, most big data software platforms have been still optimized for traditional DRAM-based computing systems. This paper first evaluates the basic performance of Intel Optane PMEM, and then investigates both indexing and searching performance of Elasticsearch, a widely-known enterprise big data search platform, on the PMEM-based computing system to explore its effectiveness and possibility. Extensive and comprehensive experiments shows that the proposed Optane PMEM-based Elasticsearch achieves indexing and searching performance improvement by an average of 1.45 times and 3.2 times respectively compared to DRAM-based system. Consequently, this paper demonstrates the high I/O, high-capacity, and nonvolatile PMEM-based computing systems are very promising for big data search platforms.

• Electronics and Telecommunications Trends
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• v.35 no.3
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• pp.55-65
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• 2020

With the development of nonvolatile memory technology, Intel has released the Optane datacenter persistent memory module (DCPMM) that can be deployed in the dual in-line memory module. The results of research and experiments on Optane DCPMMs are significantly different from the anticipated results in previous studies through emulation. The DCPMM can be used in two different modes, namely, memory mode (similar to volatile DRAM: Dynamic Random Access Memory) and app direct mode (similar to file storage). It has buffers in 256-byte granularity; this is four times the CPU (Central Processing Unit) cache line (i.e., 64 bytes). However, these properties are not easy to use correctly, and the incorrect use of these properties may result in performance degradation. Optane has the same characteristics of DRAM and storage devices. To take advantage of the performance characteristics of this device, operating systems and applications require new approaches. However, this change in computing environments will require a significant number of researches in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.93-98
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• 2022

Recently, fast storage media such as Optane have emerged, and memory system configurations designed for disk storage should be reconsidered. In this paper, we analyze the effect of the page size on the memory system performances when fast storage is adopted. Based on this, we design a page size model that can guide an appropriate page size for given workloads in virtualized environments. Configuring different page sizes for various workloads is not an easy matter in traditional systems, but due to the widespread adoption of cloud systems, page sizing performed in our model is feasible for virtual machines, which are generated for executing specific workloads. Simulation experiments under various virtual machine scenarios show that the proposed model improves the memory access time significantly by configuring page sizes for given workloads.