Advanced resource management with access control for multitenant Hadoop

Won, Heesun; Gil, Myeong-Seon; Moon, Yang-Sae

doi:10.1109/jcn.2015.000106

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…A pricing approach has been designed to regulate the prices dynamically as per the demand of a kind. In [21,22], the service penalty has diminished and enhances the profit by VM replacement approach through a mixed-integer nonlinear program called NP-hard; subsequently, a novel heuristic method has been designed to optimize the penalties and profits. CPS profit maximization approaches have been extensively examined in this literature survey.…”

Section: Profit Maximizationmentioning

confidence: 99%

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

Mekala

Patan

Islam

et al. 2021

Security and Communication Networks

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The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

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Section: Profit Maximizationmentioning

confidence: 99%

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

Mekala

Patan

Islam

et al. 2021

Security and Communication Networks

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“…Reference [4] discussed a closed-up [55] is energy saving and will ultimately save on costs. Won et al in [56] investigated the advance resource management for multiple tenants using access control to share the computing resources. In this environment, multiple tenants having different demands can share computing resources like data, storage, network, memory, and Central Processing Unit (CPU).…”

Section: Management Perspectivementioning

confidence: 99%

“…Energy-efficient predictive resource allocation and planning is presented in [55] based on predictive analysis and results that show that the proposed policy can drastically reduce the energy consumed by the BSs. Won et al [56] introduced an advanced resource management (e.g., network, memory, storage, and data) with an access control in a multitenant environment. Multitenancy facilitates management of multiple users who use similar systems.…”

Section: Network Resource Managementmentioning

confidence: 99%

A Review of Big Data Resource Management: Using Smart Grid Systems as a Case Study

Fawad

AzamMuhammad

Asghar

et al. 2021

Wireless Communications and Mobile Computing

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Big data has recently been a prominent topic of research due to the exponential growth of data every year. This massive growth of data is causing problems with resource management. The available literature does not address this problem in depth. Therefore, in this article, we aim to cover the topic of resource management for big data in detail. We addressed resource management from the perspective of smart grids for a better understanding. This study includes a number of tools and methods, such as Hadoop and MapReduce. Large data sets created by smart grids or other data-generating sources may be handled using such tools and approaches. In this article, we also discussed resource management in terms of various vulnerabilities and security risks to data and information being transmitted or received, as well as big data analytics. In summary, our comprehensive study of big data in terms of data creation, processing, resource management, and analysis gives a full picture of big data.

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An Optimal Solution of Storing and Processing Small Image Files on Hadoop

Feng

2018

Advances in Brain Inspired Cognitive Systems

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Advanced resource management with access control for multitenant Hadoop

Cited by 12 publications

References 9 publications

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

A Review of Big Data Resource Management: Using Smart Grid Systems as a Case Study

An Optimal Solution of Storing and Processing Small Image Files on Hadoop

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