Nature-inspired cost optimisation for enterprise cloud systems using joint allocation of resources

Mishra, Suchintan; Sahoo, Manmath Narayan; Sangaiah, Arun Kumar; Bakshi, Sambit

doi:10.1080/17517575.2019.1605001

Cited by 25 publications

(12 citation statements)

References 36 publications

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“…Mapetu et al [31] discussed load balancing mechanism using binary PSO to schedule the task with low-cost and low-time complexity. A cost optimization-based mechanism is presented for the enterprise cloud to optimize computing cost, bandwidth cost, and I/O cost for resource allocation [32].…”

Section:  Pe-dca Vs Benchmark Broker Policymentioning

confidence: 99%

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Parida

Pati

Chandan³

et al. 2022

COMSIS J

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In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

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Section:  Pe-dca Vs Benchmark Broker Policymentioning

confidence: 99%

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Parida

Pati

Chandan³

et al. 2022

COMSIS J

View full text Add to dashboard Cite

show abstract

“…In 2019, Mishra et al 43 suggested a combined allocation model depending on the ant colony optimization (ACO)‐fuzzy approach. In this scheme, the resources were allocated efficiently to minimize the costs of end‐user.…”

Section: Literature Surveymentioning

confidence: 99%

Self‐improved moth flame for optimal container resource allocation in cloud

Vhatkar¹,

Bhole²

2022

Concurrency and Computation

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SUMMARY Resource allocation in the cloud is becoming more complicated and challenging due to the rising necessities of cloud services. Effective management of virtual resources in the cloud is of large significance since it has a great impact on both the operational cost and scalability of the cloud environment. Nowadays, containers are becoming more popular in this regard due to their characteristics like reduced overhead and portability. Conventional resource allocation schemes are usually modeled for the migration and allocation of virtual machines (VM), as a result; the question may arise on, “how these strategies can be adapted for the management of a containerized cloud”. This work evolves the solution to this issue by introducing a new fitness oriented moth flame algorithm (F‐MFA) for optimizing the allocation of containers. Further in this work, the optimal allocation relies on certain constraints like balanced cluster use, system failure, total network distance (TND), security and threshold distance, and credibility factor as well. In the end, the supremacy of the presented model is computed to the conventional models in terms of cost and convergence analysis.

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“…Mishra et al (2019) have established a joint resource allocation algorithm, which was based on the metaheuristic ant colony optimization (ACO)-fuzzy model. The network resources were assigned and computed efficiently to reduce the end-user cost.…”

Section: Literature Surveymentioning

confidence: 99%

“…MPTCP (Qiu et al , 2019) has effective and robust container migration and enhances the resilience of the container migration process but has increased overhead. ACO-fuzzy model (Mishra et al , 2019) attained to minimize cost and reduced the required time for each task. However, needs exploration of a more multi-objective resource allocation algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Improved rider optimization for optimal container resource allocation in cloud with security assurance

Vhatkar¹,

Bhole²

2020

IJPCC

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Purpose The containerization application is one among the technologies that enable microservices architectures, which is observed to be the model for operating system (OS) virtualization. Containers are the virtual instances of the OS that are structured as the isolation for the OS atmosphere and its file system, which are executed on the single kernel and a single host. Hence, every microservice application is evolved in a container without launching the total virtual machine. The system overhead is minimized in this way as the environment is maintained in a secured manner. The exploitation of a microservice is as easy to start the execution of a new container. As a result, microservices could scale up by simply generating new containers until the required scalability level is attained. This paper aims to optimize the container allocation. Design/methodology/approach This paper introduces a new customized rider optimization algorithm (C-ROA) for optimizing the container allocation. The proposed model also considers the impact of system performance along with its security. Moreover, a new rescaled objective function is defined in this work that considers threshold distance, balanced cluster use, system failure, total network distance and security as well. At last, the performance of proposed work is compared over other state-of-the-art models with respect to convergence and cost analysis. Findings For experiment 1, the implemented model at 50th iteration has achieved minimal value, which is 29.24%, 24.48% and 21.11% better from velocity updated grey wolf optimisation (VU-GWO), whale random update assisted LA (WR-LA) and rider optimization algorithm (ROA), respectively. Similarly, on considering Experiment 2, the proposed model at 100th iteration attained superior performance than conventional models such as VU-GWO, WR-LA and ROA by 3.21%, 7.18% and 10.19%, respectively. The developed model for Experiment 3 at 100th iteration is 2.23%, 5.76% and 6.56% superior to VU-GWO, WR-LA and ROA. Originality/value This paper presents the latest fictional optimization algorithm named ROA for optimizing the container allocation. To the best of the authors’ knowledge, this is the first study that uses the C-ROA for optimization.

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Nature-inspired cost optimisation for enterprise cloud systems using joint allocation of resources

Cited by 25 publications

References 36 publications

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Self‐improved moth flame for optimal container resource allocation in cloud

Improved rider optimization for optimal container resource allocation in cloud with security assurance

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