MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Durairaj, Selvam; Ramaswamy, Sridhar

doi:10.1007/s10586-023-04040-8

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…This PSOA includes restricted amount of particles (M) in the populace, each with attributes namely Position (P) as well as velocity (V). The particle moves toward the ideal location of the neighbouring area in every iteration [29]. The values of velocity as well as location are modified.…”

Section: Primitives Of Psoamentioning

confidence: 99%

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

2024

JMC

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In ad hoc wireless sensor networks, the mobile nodes are deployed to gather data from source and transferring them to base station for reactive decision making. This process of data forwarding attributed by the sensor nodes incurs huge loss of energy which has the possibility of minimizing the network lifetime. In this context, cluster-based topology is determined to be optimal for reducing energy loss of nodes in WSNs. The selection of CH using hybrid metaheuristic algorithms is identified to be significant to mitigate the quick exhaustion of energy in entire network. This paper explores the concept of hybrid Crow Search and Particle Swarm Optimization Algorithm-based CH Selection (HCSPSO-CHS) mechanism is proposed with the merits of Flower Pollination Algorithm (FPA) and integrated Crow Search Algorithm (CSA) for efficient CH selection. It further adopted an improved PSO for achieving sink node mobility to improve delivery of packets to sink nodes. This HCSPSO-CHS approach assessed the influential factors like residual energy, inter and intra-cluster distances, network proximity and network grade during efficient CH selection. It facilitated better search process and converged towards the best global solution, such that frequent CH selection is avoided to maximum level. The outcomes of the suggested simulation HCSPSO-CHS confirm better performance depending on the maximum number of active nodes by 23.18%, prevent death of sensor nodes by 23.41% with augmented network lifetime of 33.58% independent of the number of nodes and rounds of data transmission.

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Section: Primitives Of Psoamentioning

confidence: 99%

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

2024

JMC

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“…𝑟1 = 𝐸 𝐸 ′ 𝑡+1+ 𝜂 (11) Et depicts the total energy consumed by all PM at time step t, and E t+1 depicts the total energy consumed by all PM after the current VM gets allocated to PM. 𝑟2 = 𝑅 𝑅 ′ 𝑡+1+ 𝜂 (12) Rt depicts the resources wasted by all PM at time step t, and Rt+1 depicts the resource wasted by all PM after the current VM gets allocated to PM. By traversing from one state to another and by collecting the rewards, the agent achieves its goal in the form of an optimal VM solution.…”

Section: ) Vm Placement Based On Reinforcement Learning (Vmrl)mentioning

confidence: 99%

“…In [12], a multi-objective Mayfly Strategy has been designed for large-scale cloud data centers. It involves the collection of five dependent objective functions and converting them into minimum matrix reduction with the help of principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Bhatt,

Singhal

2024

IJACSA

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The rapid demand for cloud services has provoked cloud providers to efficiently resolve the problem of Virtual Machines Placement in the cloud. This paper presents a VM Placement using Reinforcement Learning that aims to provide optimal resource and energy management for cloud data centers. Reinforcement Learning provides better decision-making as it solves the complexity of VM Placement problem caused due to tradeoff among the objectives and hence is useful for mapping requested VM on the minimum number of Physical Machines. An enhanced Tournament-based selection strategy along with Roulette Wheel sampling has been applied to ensure that the optimization goes through balanced exploration and exploitation, thereby giving better solution quality. Two heuristics have been used for the ordering of VM, considering the impact of CPU and memory utilizations over the VM placement. Moreover, the concept of the Pareto approximate set has been considered to ensure that both objectives are prioritized according to the perspective of the users. The proposed technique has been implemented on MATLAB 2020b. Simulation analysis showed that the VMRL performed preferably well and has shown improvement of 17%, 20% and 18% in terms of energy consumption, resource utilization and fragmentation respectively in comparison to other multi-objective algorithms.

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“…Thermal Comfort Constraints: The system must maintain indoor thermal conditions within acceptable ranges, EQU (2) and EQU (3).…”

Section: Subject To Constraintsmentioning

confidence: 99%

“…With the aid of Energy Management Systems (EMS) as part of SG, sustainable distribution and consumption of power are controlled. A segment of EMS, Building Energy Management Systems (BEMS), determines if the EC of a building is in line with the grid's infrastructure and the demands of the people who live there [3]. The primary methods by which BEMS enable the SG to perform with greater effectiveness is by regulating HVAC and electricity, two particularly energy-intensive building systems.…”

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confidence: 99%

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

Alsharafa,

Krishna

et al. 2024

JMC

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This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

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MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Cited by 4 publications

References 38 publications

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

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