Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

Jeba, Jenia A; Roy, Shanto; Rashid, Mahbub Or; Atik, Syeda Tanjila; Whaiduzzaman, Md

doi:10.4018/ijcac.2019010105

Cited by 32 publications

(12 citation statements)

References 43 publications

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“…Furthermore, in the study of Jeba et al, 20 the practice of server consolidation for improved programming abilities and lower power and cooling costs was adopted by a virtual machine migration-based algorithm to decrease power consumption. The analysis relies on inferred complex resource planning based on three search algorithms-sequential search, random search, and optimum justice search, with a view to competently using data center services, findings have shown that about 30% of energy savings have been achieved.…”

Section: Literature Reviewmentioning

confidence: 99%

An analytic framework for enhancing the performance of big heterogeneous data analysis

Salama

Kader

Abdelwahab

2021

International Journal of Engineering Business Management

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The use of social media networks is becoming a current phenomenon in the world today where people are sharing posts and tweets, connect with different groups, and share their opinions about things. This data is extremely heterogeneous and so it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. New techniques are therefore needed to handle these huge amounts of data to find the hidden information thus improve the results of the analysis. We are developing a framework for the analysis of heterogeneous data using machine learning (ML) techniques. In contrast to most of the literature frameworks that focus on a specific type of heterogeneous data for evaluating the proposed framework, we have analyzed 15k tweets data from six American airlines. These tweets are collected from the open stream of Twitter, also predict, classify each tweet as a negative or positive review, and test the ability of deep learning (DL) algorithms by comparing it with traditional ML algorithms. The findings confirmed the validity of the proposed framework and helped to achieve the study objective by providing excellent analysis performance and provide insights into additional aspects of information extraction from heterogeneous data.

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Section: Literature Reviewmentioning

confidence: 99%

An analytic framework for enhancing the performance of big heterogeneous data analysis

Salama

Kader

Abdelwahab

2021

International Journal of Engineering Business Management

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“…Jeba et al [53] proposed two algorithms for reduction of power and VM migrations. The working mechanism of virtualization technology with its system model has been discussed.…”

Section: Energy-aware Load Balancing Techniquesmentioning

confidence: 99%

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

2021

IJATCSE

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The Internet has become a fundamental necessity of daily activity. It has a more significant impact on modernizing the digital world. Therefore, cloud computing has been one of the most promising technological advances in recent days. It aims to provide millions of consumers around the globe with smooth computing services. In workload distributions and system behavior, the cloud environment is extremely dynamic, leading to load imbalances across data center resources. Balancing load across the systems is a crucial component of cloud computing, avoiding some nodes' overloading while others have little or no work to do. The problem of load balancing in the cloud has been linked to a large amount of research, recognizing its role and impact. This paper bestows a thorough investigation of the new load balancing algorithms for the cloud environment. This study's primary focus is the concept of load balancing, a literature review on load balancing and task scheduling techniques, and different measurement parameters

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“…Due to the variety of processing requirements, best task-to-resource mapping at runtime ensures the high performance gain in terms of throughput and helps to optimize the energy consumption [14]. To reduce the energy consumption of the servers, such strategy may allow the migration of the VMs from one server to another [15] server. Migrations not only help to reduce the number of SR's rejection by proper load balancing but also manage the processing resources efficiently.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Majumder

Saha

Chakrabarti

et al. 2022

IEEE Trans. Sustain. Comput.

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Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called "Efficient Resource Allocation of Service Request" (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90% resource utilization with only 26% SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17% reduction in SR rejection rate and 21% reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.

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Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

Cited by 32 publications

References 43 publications

An analytic framework for enhancing the performance of big heterogeneous data analysis

An analytic framework for enhancing the performance of big heterogeneous data analysis

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

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