Crow search based virtual machine placement strategy in cloud data centers with live migration

Satpathy, Anurag; Addya, Sourav Kanti; Majhi, Banshidhar; Sahoo, G.

doi:10.1016/j.compeleceng.2017.12.032

Cited by 92 publications

(49 citation statements)

References 10 publications

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“…They proposed two different technique CSA-based travel salesman problem (TSPCS) and Greedy Crow Search (GCS). The same problem has been handled by Satpathy et al [153] where a 2-tier VM placement algorithm has been proposed. First, a queuing structure to schedule VMs, whereas the second (CSAVMP) CSA-based VM problem was developed to reduce the consumption of power at data centers.…”

Section: ) Cloudmentioning

confidence: 96%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: ) Cloudmentioning

confidence: 96%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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“…VM consolidation by bin packing into fewer servers can significantly improve energy efficiency. The authors in [38] proposed a VM placement algorithm that uses VM popularity to explore the search space, achieving up to 40% power savings and reducing the number of servers used by up to 50% compared to placement based on first fit decreasing (FFD) techniques. In [39], the authors proposed a VM placement algorithm that balances the processing and memory resources of servers resulting in reducing the total power consumption by 15% compared to FFD techniques.…”

Section: Energy Efficient Placement Of Virtual Machines Over Cloumentioning

confidence: 99%

“…Constraints (31), (34), and (37) calculate the VM replica workload in a cloud, a metro fog, and an access fog, respectively, as a linear function of the traffic resulting from serving users of the replicas plus the workload baseline. Constraints (32), (35), and (38) calculate the total workload of a cloud, a metro fog, and an access fog, respectively, by summing the workloads of VMs hosted in them. Constraints (30)- (38) ensure that the VM CPU workload v satisfies user requirements to maintain QoS.…”

Section: Sw (P) Smentioning

confidence: 99%

Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

2020

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Fog computing is an emerging paradigm that aims to improve the efficiency and QoS of cloud computing by extending the cloud to the edge of the network. This paper develops a comprehensive energy efficiency analysis framework based on mathematical modeling and heuristics to study the offloading of virtual machine (VM) services from the cloud to the fog. The analysis addresses the impact of different factors including the traffic between the VM and its users, the VM workload, the workload versus number of users profile and the proximity of fog nodes to users. Overall, the power consumption can be reduced if the VM users' traffic is high and/or the VMs have a linear power profile. In such a linear profile case, the creation of multiple VM replicas does not increase the computing power consumption significantly (there may be a slight increase due to idle / baseline power consumption) if the number of users remains constant, however the VM replicas can be brought closer to the end users, thus reducing the transport network power consumption. In our scenario, the optimum placement of VMs over a cloud-fog architecture significantly decreased the total power consumption by 56% and 64% under high user data rates compared to optimized distributed clouds placement and placement in the existing AT&T network cloud locations, respectively.

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“…Data, placed in close proximity to the VM, with the decrease in VM communication distance, resulted in a reduction in job completion time [39]. A queuing structure to handle a large set of VMs and crow-search-based multi-objective optimization was used to reduce resource wastage and power consumption in datacenters [40]. The results were compared against the genetic algorithm and the first fit decreasing approach.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

et al. 2020

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Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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Crow search based virtual machine placement strategy in cloud data centers with live migration

Cited by 92 publications

References 10 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

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