Storage Management of Data-intensive Computing Systems

Xu, Yiqi

doi:10.25148/etd.fidc000251

Cited by 1 publication

(4 citation statements)

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“…Both mechanisms aggravate the I/O contention on the storage. The storage systems can be scaled-out, but the compute to storage node ratio is still high, rendering the storage subsystem a highly contended component (Xu, 2016). Therefore, the lack of I/O performance isolation in the dataintensive computing systems causes severe storage interference which compromises the performance target set by other resource managers proposed or implemented in a large body of works.…”

Section: Introductionmentioning

confidence: 99%

“…Paid user in a Big Data system also require a predictable runtime even though the job is not time sensitive, and the provider may get penalized in revenues if jobs fail to complete in a timely manner. (Xu, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Before studying these systems and addressing their respective problems separately, the discussion of the differences between these two types of systems is established here. (Xu, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This is also why the check pointing I/O's are major sources of I/O's when running such applications, as the periodical save of application progress constitutes much higher amount of data than its original input and final output. (Xu, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing

Segall

Cook

Niu

2022

International Journal of Fog Computing

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Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Before studying these systems and addressing their respective problems separately, the discussion of the differences between these two types of systems is established here. (Xu, 2016).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations