Renewable energy source based quality of service (QoS)-aware routing mechanism in cloud network

Bhoi, Ashok Kumar; Kabat, Manas Ranjan; Nayak, Suvendu Chandan; Palai, G.

doi:10.1007/s11276-022-02935-9

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Therefore if we don't manage it intelligently it may emit huge CO2, which may impact the environment. For managing effectively, the Backbone of Cloud Computing resources, the requirement of the cooling system can also be reduced [33]- [34].…”

Section: Related Workmentioning

confidence: 99%

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VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

2022

KSII TIIS

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With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

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Section: Related Workmentioning

confidence: 99%

“…1. System Architecture of Cloud EnvironmentCloud Computing is internet-based leasing compute resource[33]. The architecture of the Cloud Computing and Cloud service requests by the Cloud Consumer requests are handled at the Backend Cloud System as depicted in Fig.1.…”

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confidence: 99%