An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing DOI Creative Commons

Ajay Prashar,

Jawahar Thakur

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 16, 2023

Abstract Cloud computing is popular among industries, academia, and government to supply reliable scalable computational power. High speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the service providers manage resources effectively but unoptimized inefficient services degrade performance of system. The scheduling architecture includes Machines (PMs), (VMs) allocation migration policy VMs over PMs. overutilized PMs get a few migrations this paper introduces novel behaviour VM selection from PM using Swarm intelligence. evaluation proposed algorithm compared other state art optimization same series. has been done on base Quality Service (QoS) parameters such as SLA-Violation, energy consumption against various load variation scenario support elasticity. work outcasted techniques significant margin terms QoS illustrations are discussed result.

Language: Английский

A Combined Trend Virtual Machine Consolidation Strategy for Cloud Data Centers DOI

Yuxuan Chen,

Zhen Zhang, Yuhui Deng

et al.

IEEE Transactions on Computers, Journal Year: 2024, Volume and Issue: 73(9), P. 2150 - 2164

Published: June 19, 2024

Language: Английский

Citations

1
An Energy-Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing DOI Open Access

Ajay Prashar,

Jawahar Thakur

International Journal of Computer Theory and Engineering, Journal Year: 2024, Volume and Issue: 16(3), P. 76 - 86

Published: Jan. 1, 2024

Language: Английский

Citations

1
BTVMP: A Burst-Aware and Thermal-Efficient Virtual Machine Placement Approach for Cloud Data Centers DOI
Jie Li, Yuhui Deng, Rui Wang

et al.

IEEE Transactions on Services Computing, Journal Year: 2023, Volume and Issue: 17(5), P. 2080 - 2094

Published: Dec. 1, 2023

With the rapid growth of cloud computing, frequent workload bursts show an increasing influence on Quality Service (QoS) and energy efficiency cloud-based data centers. Existing virtual machine placement schemes are expected to optimize either QoS or for centers running under bursty conditions. To bridge this gap, we propose a burst-aware thermal-efficient technique called BTVMP . BTVMP adopts two-step strategy achieve while assuring QoS. First, leverages split-and-recombine algorithm – SAR deal with workloads. prioritizes critical workloads preventing low-priority from starvation, thereby Second, utilizes enhanced simulated annealing xmlns:xlink="http://www.w3.org/1999/xlink">ESA offer optimal (VMP) solutions, aiming minimize consumption facilitate estimating consumption, integrate into thermal model that takes account heat re-circulation effects. We conduct extensive experiments real-world trace. compare leading-edge VMP strategies, including Genetic Algorithm (XINT-GA), Power-Aware Performance-Guaranteed Virtual Machine Placement (PPVMP), Peak Load Scheduling Control Method (PLSC), First Come Serve (FCFS), GReedy based scheduling miNImizing Total Energy (GRANITE). The experimental results unveil not only enhances but also exhibits superb efficiency. In particular, reduces PLSC's delay FCFS's by 18 $\%$ 11 , respectively. Moreover, lowers total three alternative algorithms –GRANITE, XINTGA, PPVMP, PLSC anywhere between 27.8 49.4

Language: Английский

Citations

2
enCloud: Aspect‐oriented trusted service migration on SGX‐enabled cloud VM DOI Creative Commons
Seehwan Yoo, Youngpil Kim, Hyunchan Park

et al.

Software Practice and Experience, Journal Year: 2024, Volume and Issue: 54(12), P. 2454 - 2480

Published: June 18, 2024

Abstract This paper presents enCloud, a new aspect‐oriented trusted service migration with SGX‐enabled cloud VM. Addressing the challenge of reconciling end‐to‐end security VM migration, enCloud incorporates two key aspects: (1) for enclave context and (2) abstraction conventional migration. provides practical guideline applicable APIs In case study, demonstrates effective DB on VM, achieving minimal trust boundaries. The framework supports pre‐copy live to minimize downtime. contributes concise solution in form secure

Language: Английский

Citations

0
An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing DOI Creative Commons

Ajay Prashar,

Jawahar Thakur

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 16, 2023

Abstract Cloud computing is popular among industries, academia, and government to supply reliable scalable computational power. High speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the service providers manage resources effectively but unoptimized inefficient services degrade performance of system. The scheduling architecture includes Machines (PMs), (VMs) allocation migration policy VMs over PMs. overutilized PMs get a few migrations this paper introduces novel behaviour VM selection from PM using Swarm intelligence. evaluation proposed algorithm compared other state art optimization same series. has been done on base Quality Service (QoS) parameters such as SLA-Violation, energy consumption against various load variation scenario support elasticity. work outcasted techniques significant margin terms QoS illustrations are discussed result.

Language: Английский

Citations

0