Resilin: Elastic MapReduce over Multiple Clouds

Iordache, Anca; Morin, Christine; Parlavantzas, Nikos; Feller, Eugen; Riteau, Pierre

doi:10.1109/ccgrid.2013.48

Cited by 38 publications

(25 citation statements)

References 18 publications

(14 reference statements)

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“…It is possible to find works focusing on scientific workflows [6], MapReduce [20], MPI [7] and master-slave applications [8]. In this paper, we use Cloudine framework to include elasticity features to OLAM.…”

Section: Cloud Elasticitymentioning

confidence: 99%

Improving OLAM with Cloud Elasticity

Galante

Bona

Schepke

2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. Elasticity is considered one of the fundamental properties of cloud computing, and can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. This feature is suitable for dynamic applications, whose resources requirements cannot be determined exactly in advance, either due to changes in runtime requirements or in application structure. A good candidate for using cloud elasticity is the Ocean-Land-Atmosphere Model (OLAM), since it presents a significant load variation during its execution and due to online mesh refinement (OMR), that causes load unbalancing problems. In this paper, we present our efforts to adapt OLAM to use the elasticity offered in cloud environments to dynamic allocate resources according to the demands of each execution phase, and to minimize the load unbalancing caused by OMR. The results show that elasticity was successfully used to provide these features, improving the OLAM performance and providing a better use of resources.

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Section: Cloud Elasticitymentioning

confidence: 99%

Improving OLAM with Cloud Elasticity

Galante

Bona

Schepke

2014

Computational Science and Its Applications – ICCSA 2014

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“…Adding or removing servers takes minutes, which is much faster than making similar changes in clusters running on physical servers. There has been consistent progress on resizable Hadoop cluster as well [4,28,29,30,31,32], although preference limitation protocols remain less understood than their channel counterparts.…”

Section: Introductionmentioning

confidence: 99%

How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?

Ravi¹,

Kiran²

2016

IJAIS

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We develop a novel technique for resizable Hadoop cluster's lower bounds, the template matching rectangular array of inverted Index summarization expressions. Specifically, fix an arbitrary hybrid kernel function ݂ ∶ {0,1} → {0,1} and let ‫ܣ‬ be the rectangular array of inverted Index summarization expressions whose columns are each an application of ݂ to some subset of the variables ‫ݔ‬ ଵ , ‫ݔ‬ ଶ , … , ‫ݔ‬ ସ . We prove that ‫ܣ‬ has bounded-capacity resizable Hadoop cluster's complexity Ω(݀), where ݀ is the approximate degree of ݂. This finding remains valid in the MapReduce programming model, regardless of prior measurement. In particular, it gives a new and simple proof of lower bounds for robustness and other symmetric conjunctive predicates. We further characterize the discrepancy, approximate PageRank, and approximate trace distance norm of ‫ܣ‬ in terms of well-studied analytic properties of ݂, broadly generalizing several findings on small-bias resizable Hadoop cluster and agnostic inference. The method of this paper has also enabled important progress in multi-cloud resizable Hadoop cluster's complexity.

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“…As multi-cloud environments become more and more accessible, deploying MapReduce applications over multi-cloud is emerging [5], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient High-Performance Internet-Scale Data Analytics over Multi-cloud Environments

Imai

Patterson

Varela

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-To analyze data distributed across the world, one can use distributed computing power to take advantage of data locality and achieve higher throughput. The multi-cloud model, a composition of multiple clouds, can provide cost-effective computing resources to process such distributed data. As multicloud becomes more and more accessible from cloud users, the use of MapReduce/Hadoop over multi-cloud is emerging; however, existing work has two issues in principle. First, it mainly focuses on maximizing throughput by improving data locality, but the perspective of cost optimization is missing. Second, conventional centralized optimization methods would not be able to scale well in multi-cloud environments due to its highly dynamic nature. We plan to solve the first issue by formalizing an optimization framework for MapReduce over multi-cloud including virtual machine and data transfer costs, and then the second issue by creating decentralized resource management middleware that considers multi-criteria (cost and performance) optimization. This paper reports progress we have made so far on these two directions.

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Resilin: Elastic MapReduce over Multiple Clouds

Cited by 38 publications

References 18 publications

Improving OLAM with Cloud Elasticity

Improving OLAM with Cloud Elasticity

How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?

Cost-Efficient High-Performance Internet-Scale Data Analytics over Multi-cloud Environments

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