Applying Ant Colony System algorithm in multi-objective resource allocation for virtual services

Pham, N.; Le, Van Son

doi:10.1080/24751839.2017.1356159

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…Pham and Le (2017) has developed a new strategy for solving the multi-objective issue on resource allocation for virtual services. This issue tends to balance the load of physical machines and thereby reduces energy consumption.…”

Section: Literature Surveymentioning

confidence: 99%

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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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Section: Literature Surveymentioning

confidence: 99%

“…However, needs exploration of a more multi-objective resource allocation algorithm. MORA-ACS (Pham and Le, 2017) has a better balance on system load and reduces the energy consumption of the system. Further needs expansion to achieve the dynamic balancing regarding the VM migration model.…”

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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“…Besides, an integration method to verify the desirability of current mapping is introduced. A multiobjective resource allocation scheme was proposed in Pham and Le based on ant colony system method to optimize energy consumption and load balancing of the system. Also, another research project was done by Ashraf and Porres using the ant colony system algorithm to control and optimize the number of migrations and active hosts.…”

Section: Multiobjective Vmp Mechanismsmentioning

confidence: 99%

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Vahed

Ghobaei‐Arani

Souri

2019

Int J Communication

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Cloud computing introduced a new paradigm in IT industry by providing ondemand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state-of-the-art multiobjective VM placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.KEYWORDS cloud computing, multiobjective optimization, nature-inspired metaheuristic algorithms, server consolidation, virtual machine placement | INTRODUCTIONCloud computing is an emerging paradigm in IT industry, which is mainly based on two important concepts including abstraction and virtualization. 1 As NIST 2 defined, this model provides on-demand, ubiquitous, rapid, and elastic access to shared resources, which is based on the pay-as-go model. Cloud services can appear into two distinct models: deployment models and service models. Cloud deployment models are based on where the cloud's infrastructure has deployed, and they can be classified into four categories: public clouds, private clouds, hybrid clouds, and community clouds. [3][4][5] On the other hand, the cloud service models are based on the services they provide. Generally, there are three common basic cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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“…The experimental outcomes of hybrid algorithms validate the efficient processing cost as compared to state-of-the-art algorithms. However, the authors in [6] resolved the issue of resource allocation by implementing the ant colony system, named "MORA-ACS". The performance is evaluated based on PT, energy consumption and standard deviation with round robin service broker policy.…”

Section: Motivationmentioning

confidence: 99%

Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes

et al. 2018

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The influence of Information Communication and Technology (ICT) in power systems necessitates Smart Grid (SG) with monitoring and real-time control of electricity consumption. In SG, huge requests are generated from the smart homes in residential sector. Thus, researchers have proposed cloud based centralized and fog based semi-centralized computing systems for such requests. The cloud, unlike the fog system, has virtually infinite computing resources; however, in the cloud, system delay is the challenge for real-time applications. The prominent features of fog are; awareness of location, low latency, wired and wireless connectivity. In this paper, the impact of longer delay of cloud in SG applications is addressed. We proposed a cloud-fog based system for efficient processing of requests coming from the smart homes, their quick response and ultimately reduced cost. Each smart home is provided with a 5G based Home Energy Management Controller (HEMC). Then, the 5G-HEMC communicates with the High Performance Fog (HPF). The HPFs are capable of processing energy consumers' huge requests. Virtual Machines (VMs) are installed on physical systems (HPFs) to entertain the requests using First Come First Service (FCFS) and Ant Colony Optimization (ACO) algorithms along with Optimized Response Time Policy (ORTP) for the selection of potential HPF for efficient processing of the requests with maximum resource utilization. It is analysed that size and number of virtual resources affect the performance of the computing system. In the proposed system model, micro grids are introduced in the vicinity of energy consumers for uninterrupted and cost optimized power supply. The impact of the number of VMs on the performance of HPFs is analysed with extensive simulations with three scenarios.

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Applying Ant Colony System algorithm in multi-objective resource allocation for virtual services

Cited by 15 publications

References 24 publications

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

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

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes

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