Software Hint-Driven Data Management for Hybrid Memory in Mobile Systems

Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and DRAM, have been proposed to address the growing memory demand of current mobile applications. Recently emerging NVM technologies, such as phase-change memories (PCM), memristor, and 3D XPoint, have higher capacity density, minimal static power consumption and lower cost per GB. However, NVM has longer access latency and limited write endurance as opposed to DRAM. The different characteristics of distinct memory classes render a new challenge for memory system design. Ideally, pages should be placed or migrated between the two types of memories according to the data objects’ access properties. Prior system software approaches exploit the program information from OS but at the cost of high software latency incurred by related kernel processes. Hardware approaches can avoid these latencies, however, hardware’s vision is constrained to a short time window of recent memory requests, due to the limited on-chip resources. In this work, we propose OpenMem: a hardware-software cooperative approach that combines the execution time advantages of pure hardware approaches with the data object properties in a global scope. First, we built a hardware-based memory manager unit (HMMU) that can learn the short-term access patterns by online profiling, and execute data migration efficiently. Then, we built a heap memory manager for the heterogeneous memory systems that allows the programmer to directly customize each data object’s allocation to a favorable memory device within the presumed object life cycle. With the programmer’s hints guiding the data placement at allocation time, data objects with similar properties will be congregated to reduce unnecessary page migrations. We implemented the whole system on the FPGA board with embedded ARM processors. In testing under a set of benchmark applications from SPEC 2017 and PARSEC, experimental results show that OpenMem reduces 44.6% energy consumption with only a 16% performance degradation compared to the all-DRAM memory system. The amount of writes to the NVM is reduced by 14% versus the HMMU-only, extending the NVM device lifetime.

Prevalence of SARS-Cov-2 antibodies and living conditions: the French national random population-based EPICOV cohort

Background We aimed to estimate the seroprevalence of SARS-CoV-2 infection in France and to identify the populations most exposed during the first epidemic wave. Methods Random selection of individuals aged 15 years or over, from the national tax register (96% coverage). Socio-economic data, migration history, and living conditions were collected via self-computer-assisted-web or computer-assisted-telephone interviews. Home self-sampling was performed for a random subsample, to detect IgG antibodies against spike protein (Euroimmun), and neutralizing antibodies with in-house assays, in dried blood spots (DBS). Results The questionnaire was completed by 134,391 participants from May 2nd to June 2st, 2020, including 17,441 eligible for DBS 12,114 of whom were tested. ELISA-S seroprevalence was 4.5% [95% CI 3.9–5.0] overall, reaching up to 10% in the two most affected areas. High-density residences, larger household size, having reported a suspected COVID-19 case in the household, working in healthcare, being of intermediate age and non-daily tobacco smoking were independently associated with seropositivity, whereas living with children or adolescents did not remain associated after adjustment for household size. Adjustment for both residential density and household size accounted for much of the higher seroprevalence in immigrants born outside Europe, twice that in French natives in univariate analysis. Conclusion The EPICOV cohort is one of the largest national representative population-based seroprevalence surveys for COVID-19. It shows the major role of contextual living conditions in the initial spread of COVID-19 in France, during which the availability of masks and virological tests was limited.

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.

A Hierarchical Provable Massive Data Migration Method under Multicloud Storage

Now, many users have stored files on multiple clouds, and sometime, a large number of files are migrated between clouds. Because cloud providers and cloud servers are not entirely trusted, the corruption of user’s files event occur from time to time during the processes of storage and migration. Therefore, integrity verification must be performed, and the time verification overhead should be as low as possible. The existing provable data migrate methods still have the issue of high time overhead when a large number of files are migrated. Aiming at this problem, this paper proposed a hierarchical provable data migration method, which can provide the efficiency of data transfer integrity verification when moving large number of continuous files between clouds. In this paper, the proposed method is described in detail as well as the security analysis performance evaluation. The results proved that the proposed method can significantly decrease the detection latency of files transfer between clouds.

Blockchain-based Integrity Verification of Data Migration in Multi-cloud Storage

In the process of multi-cloud storage data migration, data integrity is vulnerable to corruption, but the existing data integrity verification schemes for data migration across clouds are not highly reliable. To address this problem, a blockchain-based data integrity verification scheme for migration across clouds is proposed in this paper. In this scheme, a blockchain network is used instead of a third-party auditor. For each migration, a multi-cloud broker will send an integrity verification request to blockchain at three different times, and a smart contract will verify the data integrity according to the RSA-based homomorphic verification tags. Then, the security of the scheme is analyzed. Finally, simulation experiments and tests are conducted on Ethereum, and the results show the feasibility of the scheme.

Leveraging NVMe SSDs for Building a Fast, Cost-effective, LSM-tree-based KV Store

Key-value (KV) stores support many crucial applications and services. They perform fast in-memory processing but are still often limited by I/O performance. The recent emergence of high-speed commodity non-volatile memory express solid-state drives (NVMe SSDs) has propelled new KV system designs that take advantage of their ultra-low latency and high bandwidth. Meanwhile, to switch to entirely new data layouts and scale up entire databases to high-end SSDs requires considerable investment. As a compromise, we propose SpanDB, an LSM-tree-based KV store that adapts the popular RocksDB system to utilize selective deployment of high-speed SSDs . SpanDB allows users to host the bulk of their data on cheaper and larger SSDs (and even hard disc drives with certain workloads), while relocating write-ahead logs (WAL) and the top levels of the LSM-tree to a much smaller and faster NVMe SSD. To better utilize this fast disk, SpanDB provides high-speed, parallel WAL writes via SPDK, and enables asynchronous request processing to mitigate inter-thread synchronization overhead and work efficiently with polling-based I/O. To ease the live data migration between fast and slow disks, we introduce TopFS, a stripped-down file system providing familiar file interface wrappers on top of SPDK I/O. Our evaluation shows that SpanDB simultaneously improves RocksDB's throughput by up to 8.8 \times and reduces its latency by 9.5–58.3%. Compared with KVell, a system designed for high-end SSDs, SpanDB achieves 96–140% of its throughput, with a 2.3–21.6 \times lower latency, at a cheaper storage configuration.

PENDAMPINGAN PENGELOLAAN REKAM MEDIS DAN INFORMASI KESEHATAN DI PUSKESMAS CIBEUREUM KOTA TASIMALAYA TAHUN 2021

Management of medical records and health information is one of the elements in the assessment of Public Health Center accreditation. Medical records play an important role in collecting accurate and comprehensive data for targeted health policies. The most appropriate management of medical records at the Public Health Center is the regional storage system or often referred to as the family folder system. Community service activities at the Cibeureum Health Center are carried out using consultation, training, and mentoring methods. Compiled 10 quality documents and the implementation of the e-family folder in stages. Furthermore, continuous data migration was carried out assisted by RMIK students. Activities carried out well.