An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

Sheeba, Adlin; Maheswari, B. Uma

doi:10.1002/cpe.7610

Cited by 10 publications

(7 citation statements)

References 46 publications

(83 reference statements)

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“…It was suggested to use the heuristic algorithm DBSA to solve the NP-hard issue of the scheduling problem. Later, in [25], the Dynamic Clustering League Championship algorithm (DCLCA) for fault-tolerant-based scheduling was suggested to minimize the early failure of the jobs. Advance reservation technique was used in [26] for handling faults.…”

Section: B Fault Tolerance Approachesmentioning

confidence: 99%

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Next-Gen Cloud Efficiency: Fault-Tolerant Task Scheduling With Neighboring Reservations for Improved Resource Utilization

Umar Mushtaq,

Sheikh,

Idrees

2024

IEEE Access

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One of the main goals in any computational system like the cloud is to effectively allocate resources proficiently for task scheduling. However, the dynamic characteristics of the cloud make it more prone to faults and failures. The flexible and responsive changes are made to redistribute virtual machines (VMs) to address these faults and failures for maintaining continuous services. However, it may inadvertently lead to uneven load distribution. Therefore, thorough attention is required to ensure carefully monitored load equilibrium following fault tolerance. Addressing all these issues simultaneously with optimized Quality of Service (QoS) parameters is a good need of time. In this paper, a novel hybrid model: the Hybrid Faulttolerant Scheduling and Load balancing Model (HFSLM) has been proposed to optimize the makespan of the dynamically arriving tasks and efficiently utilize the available VMs. Moreover, the model also provides solutions for several crucial concerns in cloud systems including VM failure, and VM/task heterogeneity. In the consequence of a VM failure, the approach offers a Neighbouring VM as a substitute for the corresponding task to complete its execution. Furthermore, the model is escorted by a load-balancing algorithm to maintain the equilibrium of load distribution after fault handling for further optimization of the considered QoS parameters. HFSLM is evaluated by comparing it with FTHRM, MAX-MIN, MIN-MIN, and OLB on a small task scale over diverse task and machine heterogeneities and with ELISA and MELISA on an extremely large task scale. The evaluation results show that the recommended HFSLM tops the compared approaches in all the considered cases and heterogeneities.INDEX TERMS cloud computing, task allocation, fault tolerance, resource reservation, load balancing I. INTRODUCTION

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Section: B Fault Tolerance Approachesmentioning

confidence: 99%

“…The proposed HFSLM is compared with four different approaches based on two main parameters i.e., makespan and average utilization. HFSLM is evaluated by comparing it with FTHRM [22], OLB [23], MIN-MIN [24], and MAX-MIN [25] for less than 1000 tasks. Also, compared with ELISA and MELISA with greater than 10,000 tasks.…”

Section: B Fault-tolerancementioning

confidence: 99%

Next-Gen Cloud Efficiency: Fault-Tolerant Task Scheduling With Neighboring Reservations for Improved Resource Utilization

Umar Mushtaq,

Sheikh,

Idrees

2024

IEEE Access

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“…To address this, the K-means clustering algorithm is utilized. Furthermore, the adaptive particle swarm optimization with the coyote optimization algorithm is employed to obtain the optimal cluster for virtual machine placement and reduce the challenge [19,21]. Zhang, Chen and Jiang [26] establishes a model of initial placement for fault-tolerant virtual machines in star topological data centers of cloud systems, taking into account several factors such as the violation rate of service-level agreements, the remaining rate of resources, the rate of power consumption, the rate of failure, and the cost of fault tolerance.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

et al. 2023

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The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

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“…Simulation outcomes validate the effectiveness of these three strategies in adapting ETA-ACO to the VM allocation problem. Addressing the challenging and critical issue of VM allocation for highly reliable cloud applications, Sheeba and Uma Maheswari [28] propose an improved Firefly algorithm-based approach. A Kmeans clustering algorithm is used to reduce migration time.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Allocation in Cloud Computing Environments using Giant Trevally Optimizer

Zhang

2023

IJACSA

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Cloud computing has gained prominence due to its potential for computational tasks, but the associated energy consumption and carbon emissions remain significant challenges. Allocating Virtual Machines (VMs) to Physical Machines (PMs) in cloud data centers, a known NP-hard problem, offers an avenue for enhancing energy efficiency. This paper presents an energy-conscious optimization approach utilizing the Giant Trevally Optimizer (GTO) which is inspired by the hunting strategies of the giant trevally, a proficient marine predator. Our study mathematically models the trevally's hunting behavior when targeting seabirds. The trevally's approach involves strategic selection of optimal hunting locations based on food availability, including pursuing seabird prey in the air or seizing it from the water's surface. Through extensive simulations, our method demonstrates superior performance in terms of skewness, CPU utilization, memory utilization, and overall resource allocation efficiency. This research offers a promising avenue for addressing the energy consumption challenges in cloud data centers while optimizing resource utilization for sustainable and cost-effective cloud operations.

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An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

Cited by 10 publications

References 46 publications

Next-Gen Cloud Efficiency: Fault-Tolerant Task Scheduling With Neighboring Reservations for Improved Resource Utilization

Next-Gen Cloud Efficiency: Fault-Tolerant Task Scheduling With Neighboring Reservations for Improved Resource Utilization

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

Virtual Machine Allocation in Cloud Computing Environments using Giant Trevally Optimizer

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