A Counter-Based Profiling Scheme for Improving Locality Through Data and Reducer Placement

Hussain, Mir Wajahat; Roy, Diptendu Sinha

doi:10.1007/978-981-16-8930-7_4

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The loss coefficient σ refers to the loss degree of the ith total alarm. Its main influencing factors include two aspects, one is the attack intensity of the opponent, and the other is the vulnerability of the equipment itself [13]. The calculation formula of the sag coefficient is as follows:…”

Section: Example Schemementioning

confidence: 99%

Design of heterogeneous network security device management platform based on multi-source data

Huang

Wang

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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With the gradual expansion of network scale, network security problems caused by intrusion attacks and Trojan horse viruses follow. Network attacks are highly targeted and diverse. With a large number of Internet of Things devices in the power system connected to the Internet, the interaction of heterogeneous information and the rapid change of network structure drive the dynamic development of the Internet of Things environment. It further expands the attack surface that may be threatened, and constantly generates new weaknesses and threats. In this paper, a rule-based reasoning method for multi-source knowledge in the security of the Internet of Things was proposed. First, a description logic-based language to represent the classes in the model was adopted. Reasoning rules were designed to supplement the semantic representation ability of the description language, which is to realize the reasoning of implicit facts from multi-source heterogeneous knowledge and data in the security field of the Internet of Things. Compared with the entropy method, the model is proved to be effective in predicting the actual network security situation and has certain practical guiding significance for the actual network security management.

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Section: Example Schemementioning

confidence: 99%

Design of heterogeneous network security device management platform based on multi-source data

Huang

Wang

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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Performance Optimization Strategies for Big Data Applications in Distributed Framework

Hussain

Roy

2023

Studies in Computational Intelligence

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Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques

Sundara Kumar,

Mohan

2024

IFS

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Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes.

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A Counter-Based Profiling Scheme for Improving Locality Through Data and Reducer Placement

Cited by 4 publications

References 18 publications

Design of heterogeneous network security device management platform based on multi-source data

Design of heterogeneous network security device management platform based on multi-source data

Performance Optimization Strategies for Big Data Applications in Distributed Framework

Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques

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