iSCSI Multi-connection and Error Recovery Method for Remote Storage System in Mobile Appliance

Allayear, Shaikh Muhammad; Park, Sung Soon

doi:10.1007/11751588_67

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Our Q-Chained cluster load balancer does this job [3]. So that the reducer can continue its work, which reduces the job completion time (Fig.…”

Section: Proposed Model: Mapreduce Agentmentioning

confidence: 93%

“…As mentioned in [3], in a single TCP network connection when congestion occurs by a timeout or the reception of duplicate ACKs (Acknowledgement) then one half of the current window size is saved in sstresh (slow start window). Additionally, if the congestion is indicated by a timeout, cwnd (congestion window) is set to one segment.…”

Section: Multi-connection and Error Recovery Methods Of Iscsimentioning

confidence: 99%

“…A Q-chained cluster [3] is able to balance the workload fully among data connections in the event of packet losses due to bad channel characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem MRA creates iSCSI [2] Multi-Connection and Error Recovery Method [3] to avoid drastic reduction of transmission rate from TCP congestion control mechanism and guarantee fast retransmission of corruptive packet without TCP re-establishment.…”

Section: Introductionmentioning

confidence: 99%

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The Emergence of Modified Hadoop Online-Based MapReduce Technology in Cloud Environments

Allayear

Salahuddin

Hossain

et al. 2015

Big Data Benchmarking

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Abstract. The exponential growth of data first presented challenges to cuttingedge businesses such as Goggle, Yahoo, Amazon, Microsoft, Facebook, and Twitter. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Hadoop MapReduce has become a powerful computation model that addresses those problems. MapReduce is a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters. Through a simple interface with two functions, map and reduce, this model facilitates parallel implementation of many real world tasks such as data processing for search engines and machine learning. Earlier versions of Hadoop MapReduce had several performance problems like connection between map to reduce task, data overload and slow processing. In this paper, we propose a modified MapReduce architectureMapReduce Agent (MRA) -that resolves those performance problems. MRA can reduce completion time, improve system utilization, and give better performance. MRA employs multi-connection which resolves error recovery with a Q-chained load balancing system. In this paper, we also discuss various applications and implementations of the MapReduce programming model in cloud environments.

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“…Our Q-Chained cluster load balancer does this job [3]. So that the reducer can continue its work, which reduces the job completion time (Fig.…”

Section: Proposed Model: Mapreduce Agentmentioning

confidence: 93%

Section: Multi-connection and Error Recovery Methods Of Iscsimentioning

confidence: 99%

“…A Q-chained cluster [3] is able to balance the workload fully among data connections in the event of packet losses due to bad channel characteristics.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Emergence of Modified Hadoop Online-Based MapReduce Technology in Cloud Environments

Allayear

Salahuddin

Hossain

et al. 2015

Big Data Benchmarking

Self Cite

View full text Add to dashboard Cite

show abstract

“…For that reason, TCP connection being disconnected and after that data can be retransmitted which takes long time. So MRA mechanism [17] that can send data without retransmission using iSCSI multi-connection [3] and also manage load balancing of data. Finally to process big data MRA mechanism provide better performance and reduce the time to job completion than pipelining mechanism and Google proposed mechanism.…”

Section: H Mapreduce Agent (Mra) Mechanismmentioning

confidence: 99%