Expanded cloud plumes hiding Big Data ecosystem

Sharma, Shweta

doi:10.1016/j.future.2016.01.003

Cited by 66 publications

(54 citation statements)

References 85 publications

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“…However, not all companies can take afford the investments that big data technology requires both in terms of financial and human resources. In such scenarios, cloud computing provides a robust alternative as the burden of providing and maintaining expensive computer resources shifts to the cloud service providers (Sharma, 2016). …”

Section: Big Data Using Cloud Computing -Current Trendsmentioning

confidence: 99%

“…Cloud storage offers a robust, distributed, scalable, and fault tolerant infrastructure that can match the processing power associated with parallel and distributed processing models (Sharma, 2016). Assunção et al (2015) state that cloud computing not only provides infrastructure and tools for big data but could also provide a business model that can be used in big data analytics, for instance either Analytics as a Service (AaaS) or Big Data as a Service (BDaaS).…”

Section: Big Data Using Cloud Computing -Current Trendsmentioning

confidence: 99%

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Big Data using Cloud Computing - Opportunities for Small and Medium-sized Enterprises

Vajjhala¹,

Ramollari²

2016

Eur. j. econ. bus. stud.

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Big Data has been listed as one of the current and future research frontiers by Gartner. Large-sized companies are already investing on and leveraging big data. Small-sized and medium-sized enterprises (SMEs) can also leverage big data to gain a strategic competitive advantage but are often limited by the lack of adequate financial resources to invest on the technology and manpower. Several big data challenges still exist especially in computer architecture that is CPU-heavy but I/O poor. Cloud computing eliminates the need to maintain expensive computing hardware and software. Cloud computing resources and techniques can be leveraged to address the traditional problems associated with fault tolerance and low performance causing bottlenecks to using big data. SMEs can take advantage of cloud computing techniques to avail the advantages of big data without significant investments in technology and manpower. This paper explores the current trends in the area of big data using cloud resources and how SMEs can take advantage of these technological trends. The results of this study will benefit SMEs in identifying and exploring possible opportunities and also understanding the challenges in leveraging big data.

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Section: Big Data Using Cloud Computing -Current Trendsmentioning

confidence: 99%

Section: Big Data Using Cloud Computing -Current Trendsmentioning

confidence: 99%

Big Data using Cloud Computing - Opportunities for Small and Medium-sized Enterprises

Vajjhala¹,

Ramollari²

2016

Eur. j. econ. bus. stud.

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“…Open source Virtualization technologies in Cloud computing provided this paper on multiple Node to measure its performance [2], [3], [4] and [5]. In this paper, we extend this evaluation to include Master Node as another Instance in virtualization platform, and test both under different scenarios including multiple VMs and multi-tiered systems.…”

Section: Related Workmentioning

confidence: 99%

Implementation of the Open Source Virtualization Technologies in Cloud Computing

Rashid¹,

Rana²,

Das³

2016

IJCCSA

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“…By caching input and intermediate data in memory, I/Ointensive jobs can be sped up by orders of magnitude [1], [5]. However, compared with stable storage, memory cache remains constrained in production clusters, and it is not possible to persist all data in memory [6]. Efficient cache management, therefore, becomes highly desirable for parallel data analytics.…”

Section: Introductionmentioning

confidence: 99%

LERC: Coordinated Cache Management for Data-Parallel Systems

Wang

Letaief

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Abstract-Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency-or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy.

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Expanded cloud plumes hiding Big Data ecosystem

Cited by 66 publications

References 85 publications

Big Data using Cloud Computing - Opportunities for Small and Medium-sized Enterprises

Big Data using Cloud Computing - Opportunities for Small and Medium-sized Enterprises

Implementation of the Open Source Virtualization Technologies in Cloud Computing

LERC: Coordinated Cache Management for Data-Parallel Systems

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