Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Mathematical Problems in Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 11

Published: May 17, 2022

As the cloud data centers size increases, number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in physical machine (PM). The rapid growth Internet services has created an imbalance network resources. Some hosts have high bandwidth usage and can cause congestion. Network congestion affects overall performance. Cloud computing load balancing is important feature that needs to optimized. Therefore, this research proposes a 3-tier architecture, which consists layer, Fog Consumer layer. serves world, analyzes at local edge network. stores temporarily, transmitted cloud. world classified into 6 regions on basis continents consumer Consider Area 0 as North America, for two fogs cluster buildings considered. Microgrids (MG) used supply energy consumers. In research, real-time VM migration algorithm fog been proposed. Load algorithms focus effective resource utilization, maximum throughput, optimal response time. Compared closest center (CDC), achieves 18% better cost results optimized time (ORT). Realtime ORT increase 11% compared dynamic reconFigure with (DRL) load. always seeks best solution minimize processing

Language: Английский

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Optimal Virtual Machine Placement Based on Grey Wolf Optimization

Electronics, Journal Year: 2019, Volume and Issue: 8(3), P. 283 - 283

Published: March 4, 2019

Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP has substantial impact on energy efficiency data centers, as it reduces number active physical servers, thereby reducing power consumption. In this paper, computational intelligence technique applied to address problem optimization. The formulated minimization which objective reduce hosts and Based promising performance grey wolf (GWO) for combinatorial problems, GWO-VMP proposed. We propose transforming into binary discrete problems via two algorithms. proposed method effectively minimizes servers that are used host virtual machines (VMs). evaluated various VM sizes CloudSIM environment homogeneous heterogeneous servers. experimental results demonstrate consumption more efficient use CPU memory resources.

Language: Английский

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An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Electronics, Journal Year: 2019, Volume and Issue: 8(2), P. 218 - 218

Published: Feb. 16, 2019

Cloud computing offers various services. Numerous cloud data centers are used to provide these services the users in whole world. A center is a house of physical machines (PMs). Millions virtual (VMs) minimize utilization rate PMs. There chance unbalanced network due rapid growth Internet An intelligent mechanism required efficiently balance network. Multiple techniques solve aforementioned issues optimally. VM placement great challenge for service providers fulfill user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm proposed problem efficiently. archive store and retrieve true Pareto front. grid improve non-dominated VMs archive. also maintenance The mimics leadership hunting behavior wolves (GWs) search space. was tested on nine well-known bi-objective tri-objective benchmark functions verify compatibility work done. LMOGWO then compared with simple (MOGWO) particle swarm (MOPSO). Two scenarios were considered simulations check adaptivity algorithm. outperformed MOGWO MOPSO University Florida 1 (UF1), UF5, UF7 UF8 Scenario 1. However, performed better than UF2. For 2, other two algorithms UF9. well UF2 UF4. results Moreover, PM (%) minimized by 30% LMOGWO, 11% 10% MOPSO.

Language: Английский

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A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e834 - e834

Published: Jan. 12, 2022

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, placement (VMP) is now critical provision efficient resource management in cloud data centers. VMP process considers a set machines onto physical machines, accordance with criteria. optimal solution multi-objective can be determined by using fitness function that combines objectives. This paper proposes novel model enhance performance decision-making process. Placement decisions are made based on three criteria: time, power consumption, wastage. proposed aims satisfy minimum values objectives all available machines. To optimize solution, was implemented optimization algorithms: particle swarm Lévy flight (PSOLF), flower pollination (FPO), hybrid algorithm (HPSOLF-FPO). Each tested experimentally. results comparative study between algorithms show strongest performance. Moreover, against bin packing best fit strategy. outperforms strategy total server utilization.

Language: Английский

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EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Symmetry, Journal Year: 2021, Volume and Issue: 13(4), P. 690 - 690

Published: April 15, 2021

The rapid demand for Cloud services resulted in the establishment of large-scale Data Centers (CDCs), which ultimately consume a large amount energy. An enormous energy consumption eventually leads to high operating costs and carbon emissions. To reduce with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware VM (MLLM), etc.) have been considered. Most these techniques do aggressive that results performance degradation CDCs terms utilization consumption. In this paper, an Adaptive (EAMA) is proposed effective placement VMs on Physical Machines (PMs) dynamically. approach has two distinct features: first, selection PM locations optimum access delay where are required be migrated, second, reduces number migrations. Extensive simulation experiments conducted using CloudSim toolkit. compared PACPA RUAEE algorithms Service-Level Agreement (SLA) violation, hosts shut down, Results show EAMA significantly migrations by 16% 24%, SLA violation 20% 34%, increases 8% 17% increased down from 10% 13% as RUAEE, respectively. Moreover, improvement also observed.

Language: Английский

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EnTruVe: ENergy and TRUst-aware Virtual Machine allocation in VEhicle fog computing for catering applications in 5G

Future Generation Computer Systems, Journal Year: 2021, Volume and Issue: 126, P. 196 - 210

Published: Aug. 4, 2021

Language: Английский

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Application of virtual machine consolidation in cloud computing systems

Sustainable Computing Informatics and Systems, Journal Year: 2021, Volume and Issue: 30, P. 100524 - 100524

Published: Feb. 11, 2021

Language: Английский

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Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Mathematical Problems in Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 11

Published: Jan. 30, 2022

Cloud computing provides unprecedented advantages of using resources with very less efforts and cost. The energy utilization in cloud data centers has forced the service providers to raise expense its services increased carbon footprints environment. Many static bin-packing algorithms exist which can reduce by some percentage, but new era digitization, advanced dynamic techniques are required serve heterogeneous users random users’ requests. Thus, this paper, two best-fit decreasing-based proposed wherein first technique is for focuses on increasing server second approach acts as a switcher harness best results among all algorithms. Both deliberately achieve high performance terms total consumption, resource utilization, makespan along serving continuous varying requests from customers. simulations performed Java. exhibited that DEE-BFD escalate 96% EM consumption 49% 56%.

Language: Английский

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A hybrid energy-aware algorithm for virtual machine placement in cloud computing

Computing, Journal Year: 2024, Volume and Issue: 106(5), P. 1297 - 1320

Published: April 3, 2024

Language: Английский

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Abstract Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of data centers in a flexible scalable manner. It allows placing multiple independent virtual machines (VMs) on single server order improve resource utilization energy efficiency. However, determining optimal VM placement is crucial as it directly impacts load balancing, consumption, performance degradation within center. Furthermore, deciding based factor usually insufficient center because many factors must be considered, ignoring them may too expensive. This paper improves new multi-objective (MVMP) algorithm using quantum particle swarm optimization (QPSO) technique. We call QPSO-MOVMP, its objective find Pareto solution for problem by balancing different goals. generates solutions that save power minimizing number running machines, avoid maintaining service level agreement (SLA), keeping loads at utilization. The experimental results show QPSO-MOVMP had superior terms consumption compared three other algorithms conventional single-objective algorithms. Simulation proposed achieves 2.4 × 10 4 watts power. outperformed others, achieving minimum 12% SLA breaches while experiencing significant surge requests from VMs. Moreover, model generated better distribution than those derived comparative method.

Language: Английский

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