Analyzing I/O Request Characteristics of a Mobile Messenger and Benchmark Framework for Serviceable Cold Storage
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaemyoun | - |
dc.contributor.author | Song, Chang | - |
dc.contributor.author | Park, Chanyoung | - |
dc.contributor.author | Kim, Soyeun | - |
dc.contributor.author | Kang, Kyungtae | - |
dc.date.accessioned | 2021-06-22T15:25:24Z | - |
dc.date.available | 2021-06-22T15:25:24Z | - |
dc.date.issued | 2017-05 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/11723 | - |
dc.description.abstract | Cloud computing systems require massive storage infrastructures, which have significant implications on power bills, carbon emissions, and the logistics of data centers. Various proprietary "cold storage" services, based on spun-down disks or tapes, offer reduced tariffs but also lead to extended times to first access. One way to improve cold-storage systems is to build them on a file system that allows for the I/O patterns of storage devices. We have developed a cold-storage test-bed for mobile messenger services, which takes into account the power consumption of each hard disk in the system. We analyzed a trace of I/O requests from a messenger service and found that they had a strongly skewed Zipfian distribution and that most of the stored data is cold. Current cloud benchmarking tools cannot reproduce this pattern of I/O. Therefore, we have developed a tool for benchmarking cold-storage systems that emulates this type of long-tail distribution and can contribute to reducing the power consumption of mobile messenger services. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Analyzing I/O Request Characteristics of a Mobile Messenger and Benchmark Framework for Serviceable Cold Storage | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2017.2701839 | - |
dc.identifier.scopusid | 2-s2.0-85028945980 | - |
dc.identifier.wosid | 000404270600098 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.5, pp 9797 - 9811 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 5 | - |
dc.citation.startPage | 9797 | - |
dc.citation.endPage | 9811 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Benchmarking | - |
dc.subject.keywordPlus | Carbon | - |
dc.subject.keywordPlus | Cold storage | - |
dc.subject.keywordPlus | Distributed computer systems | - |
dc.subject.keywordPlus | Electric power utilization | - |
dc.subject.keywordPlus | Energy efficiency | - |
dc.subject.keywordPlus | Hard disk storage | - |
dc.subject.keywordPlus | Statistical methods | - |
dc.subject.keywordPlus | Virtual storage | - |
dc.subject.keywordAuthor | Cold storage | - |
dc.subject.keywordAuthor | large-scale storage systems | - |
dc.subject.keywordAuthor | energy-efficiency | - |
dc.subject.keywordAuthor | statistical analysis | - |
dc.subject.keywordAuthor | benchmark | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7921529 | - |
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