Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm

Shabeera, T P; Kumar, Sandeep; Salam, Sameera Muhamed; Krishnan, K. Murali

doi:10.1016/j.jestch.2016.11.006

Cited by 77 publications

(42 citation statements)

References 19 publications

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“…The constraint satisfaction rule for makespan minimization adopted in [22] failed to address the issue of transition loops which causes delay in convergence and longer time of the assigned couplings. The proposed research will focus on improving the rule by injecting the mechanism of loop free SPT algorithm to improve on convergence, makespan time and cost in a multi-dimensional spaces.…”

Section: Methodsmentioning

confidence: 99%

“…A rule for pheromone updates should be strategized and also promote faster convergence. 4 Shabeera et al [22] Heuristic method that reduced bandwidth usage and cross network traffic by placing required number of data in PMs and VMs which are physically closer.…”

Section: Related Workmentioning

confidence: 99%

“…To our knowledge, this is the first application of ACO and SPT for eliminating loops in order to obtain faster convergence. Numerous researchers recently proposed several hybrid ACO algorithms [19] [20] [21] [22] in cloud task scheduling. Some of these techniques scale well in terms of convergence, throughput, makespan, execution time, and waiting time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

Aliyu*,

Murali,

Gital

et al. 2019

IJRTE

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Paper Several Ant Colony Optimization (ACO) techniques for Cloud resources management are considered by many researchers. ACO techniques in existence still need some improvements for effective resource management and planning with the heterogeneous and voluminous services offered. Hence, an optimized hybrid scheme that combined deterministic characteristics for exploiting ACO search process is proposed. Spanning Tree (ST) algorithm was chosen in the hybridization that obtained a faster convergence speed, minimized makespan time and throughput that ensured resource utilization in least time and cost. Extensive experiments were conducted in cloudsim simulator provided an efficient result compared to other ACO techniques as it significantly improves performance.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

Aliyu*,

Murali,

Gital

et al. 2019

IJRTE

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“…VMs are installed on computing environments for efficient resource utilization and processing. In [14], experimental analysis is conducted to evaluate the number of VMs for a physical machine in energy efficient way. However, the authors concluded that the capacity of VMs and their collocation reduce the job completion time.…”

Section: Problem Statementmentioning

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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“…A VM allocation policy for data intensive applications is implemented [11] which would select a subset of available PMs for placement. This selection using Ant Colony Optimization would be such that the VMs demand must be fulfilled based on applications request.…”

Section: Related Workmentioning

confidence: 99%

Energy Saving VM Placement in Cloud

Acharya¹,

D’Mello²

2018

IJMECS

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The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.

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Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm

Cited by 77 publications

References 19 publications

AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

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

Energy Saving VM Placement in Cloud

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