OP-MLB: An Online VM Prediction-Based Multi-Objective Load Balancing Framework for Resource Management at Cloud Data Center

Saxena, Deepika; Singh, Ashutosh Kumar; Buyya, Rajkumar

doi:10.1109/tcc.2021.3059096

Cited by 72 publications

(40 citation statements)

References 34 publications

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“…The algorithm is validated on benchmark datasets in terms of accuracy. To address the issues of excessive power consumption, imbalanced load, and inefficient resource utilization, an efficient resource management algorithm is presented in [23]. The algorithm targets better resource utilization and load management to improve performance.…”

Section: Literature Reviewmentioning

confidence: 99%

A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

et al. 2022

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Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques.

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Section: Literature Reviewmentioning

confidence: 99%

A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

et al. 2022

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“…Fig. 3(a) compares the resource utilization (RU CDC (%)) of SaS-LM model with that of state-of-the-art approaches: PEFS [13], SBA [11], BM-JS [4], OP-MLB [14], and WP-SM [17]. All the quartiles viz., lower, upper and median of the proposed model are higher than the respective values of quartiles of the compared approaches which indicates effectiveness of the proposed model in enhancing the RU CDC (%).…”

Section: Resource Utilizationmentioning

confidence: 99%

“…Step 2 evaluates fitness of N solutions with O(N ) complexity. Steps [3][4][5], steps [6][7][8][9][10][11][12], and steps [13][14][15] iterate K times have equal time complexity of O(K). Assume steps [16][17][18][19][20][21][22][23][24][25][26][27][28][29] repeat for t intervals, wherein steps [19][20][21] have O(K) while steps [24][25][26][27][28] have O(N ) complexities.…”

Section: Position Updatementioning

confidence: 99%

A Sustainable and Secure Load Management Model for Green Cloud Data Centres

Saxena

Singh

Lee

et al. 2022

Preprint

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Massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting into high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using a benchmark real world Google Cluster VM traces. The proposed modelis compared with state-of-the-arts which reveals its efficacy in termsof reduced carbon emission and energy consumption up to 46.9% and43.9%, respectively with improved resource utilization up to 16.5%.

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“…Commercial cloud services have strengthened the online and digital business enterprises with their surplus benefits including maximum computing at minimum capital investment, reliability, elasticity and scalability of resources [ 1 – 4 ]. The dependency of cloud users on third-party resources has increased manyfolds that will continue to grow in future [ 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments

Saxena

Singh

2022

J Supercomput

Self Cite

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The indispensable collaboration of cloud computing in every digital service has raised its resource usage exponentially. The ever-growing demand of cloud resources evades service availability leading to critical challenges such as cloud outages, SLA violation, and excessive power consumption. Previous approaches have addressed this problem by utilizing multiple cloud platforms or running multiple replicas of a Virtual Machine (VM) resulting into high operational cost. This paper has addressed this alarming problem from a different perspective by proposing a novel nline virtual machine ailure rediction and olerance odel (OFP-TM) with high availability awareness embedded in physical machines as well as virtual machines. The failure-prone VMs are estimated in real-time based on their future resource usage by developing an ensemble approach-based resource predictor. These VMs are assigned to a failure tolerance unit comprising of a resource provision matrix and Selection Box (S-Box) mechanism which triggers the migration of failure-prone VMs and handle any outage beforehand while maintaining the desired level of availability for cloud users. The proposed model is evaluated and compared against existing related approaches by simulating cloud environment and executing several experiments using a real-world workload Google Cluster dataset. Consequently, it has been concluded that OFP-TM improves availability and scales down the number of live VM migrations up to 33.5% and 83.3%, respectively, over without OFP-TM.

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OP-MLB: An Online VM Prediction-Based Multi-Objective Load Balancing Framework for Resource Management at Cloud Data Center

Cited by 72 publications

References 34 publications

A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

A Sustainable and Secure Load Management Model for Green Cloud Data Centres

OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments

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