FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

Naik, Banavath Balaji; Singh, Dhananjay; Samaddar, Arindam

doi:10.1049/iet-com.2019.1149

Cited by 48 publications

(9 citation statements)

References 23 publications

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“…To increase the precision of forecasts, it incorporates seven unique predicting algorithms across the domains of statistical analysis of time series, linear regression, and artificial intelligence. For forecasting a server workload pattern in a cloud-based storage center, a cloud load prediction based on a weighted fractal support vector machine algorithm is presented 34 . In this study, parametric optimization using a method called particle optimization technique was created.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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“…4. In the fourth experiment, the power consumption and execution time are evaluated based [37] dataset. Four physical machines and 5 -50 virtual machines are used in this experiment.…”

Section: Simulationmentioning

confidence: 99%

Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm

DENG¹,

Ning²,

Yang³

2023

IJACSA

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With cloud computing, resources can be networked globally and shared easily between users. A range of heterogeneous needs are met on demand by software, hardware, storage, and networking. Dynamic resource allocation and load distribution pose challenges for cloud servers. In this regard, task scheduling plays a significant role in enhancing the performance of cloud computing. With the increase in the number of users and the capability of cloud computing, cloud data centers are experiencing concerns regarding energy consumption. To leverage cloud resources energy efficiently and provide real-time services to users, a viable cloud task scheduling solution is required. To address these problems, this paper proposes a new hybrid task scheduling algorithm based on squirrel search and improved genetic algorithms for cloud environments. The proposed scheduling algorithm surpasses existing scheduling algorithms across multiple parameters, including makespan, energy consumption, and execution time.

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“…This method indicates that it significantly impacts the performance of cloud centers. Naik et al [27] also use a hybrid approach on fruit fly search and the cuckoo optimisation algorithm for local search and the global search for efficient VM placement with a high convergence rate and validate the proposed techniques on the simulation model. As meta-heuristic algorithms have their limitations, the hybrid approach can efficiently reduce the disadvantages of other algorithms that improve the efficiency of QoS parameters.…”

Section: Meta-heuristic Techniquementioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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Cloud data centers remove the hardware barriers for the world with enormous storage and computing capabilities and provide access from anywhere over the internet with the concept of virtual machines (VM) and physical machines (PM). These cloud centers raise the problem of lack of resource utilisation and energy waste. These drawbacks have led many researchers to propose VM placement techniques that improve resource utilization and energy consumption. The research proposes VM migration/consolidation and host selection techniques to improve the resource utilisation of cloud centers. This research suggests hybrid black widow optimization (HBWO) and a combined machine learning model to mitigate the problem of cloud centers. HBWO optimizes the VM placement sub-process using an evolutionary approach and a combined machine-learning-based model to predict the over and under-utilised hosts. The effectiveness of the proposed approach was analysed and compared with a Power-Aware Best-Fit Decrease (PABFD) approach and a Particle Swarm Optimization (PSO) approach. The proposed approach improves energy utilisation by 30-40\% over PABFD and 20-15\% over PSO. Moreover, the combined approach of HBWO and machine learning improves the overall performance by 40-50\% compared to PABFD and 20-30\% compared to PSO.

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FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

Cited by 48 publications

References 23 publications

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

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