Multi Objective Task Scheduling Algorithm in Cloud Computing Using the Hybridization of Particle Swarm Optimization and Cuckoo Search

Mangalampalli, Sudheer; Mangalampalli, Vamsi Krishna; Swain, S. K.

doi:10.1166/jctn.2020.9427

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Similarly, [12] also uses a hybrid approach and combines PSO for local search and BSO to further optimize the search, reducing the cloud center's overall cost in the simulated environment. Additionally, there are many hybrid approaches, like the GA-PSO hybrid approach [16], and the PSO-Cuckoo hybrid approach [23] to improve the QoS parameters of cloud centers. The main limitation of these hybrid approaches is that they fail to explain why they are better than other combinations.…”

Section: Meta-heuristic Techniquementioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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Cloud data centers remove the hardware barriers for the world with enormous storage and computing capabilities and provide access from anywhere over the internet with the concept of virtual machines (VM) and physical machines (PM). These cloud centers raise the problem of lack of resource utilisation and energy waste. These drawbacks have led many researchers to propose VM placement techniques that improve resource utilization and energy consumption. The research proposes VM migration/consolidation and host selection techniques to improve the resource utilisation of cloud centers. This research suggests hybrid black widow optimization (HBWO) and a combined machine learning model to mitigate the problem of cloud centers. HBWO optimizes the VM placement sub-process using an evolutionary approach and a combined machine-learning-based model to predict the over and under-utilised hosts. The effectiveness of the proposed approach was analysed and compared with a Power-Aware Best-Fit Decrease (PABFD) approach and a Particle Swarm Optimization (PSO) approach. The proposed approach improves energy utilisation by 30-40\% over PABFD and 20-15\% over PSO. Moreover, the combined approach of HBWO and machine learning improves the overall performance by 40-50\% compared to PABFD and 20-30\% compared to PSO.

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Section: Meta-heuristic Techniquementioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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“…Mapping of tasks or workflows onto suitable virtual resources is an important aspect in cloud computing as when a task or workflow which consists of huge length, high processing capacity and if your scheduler maps it onto a virtual resource which can take huge amount of time to process that task or workflow then it will incur huge overhead and it impacts the makespan of the scheduler which also impacts many of the other operational costs. Nature inspired algorithms such as dynamic PSO [2], Hybridized CSPSO [3], Whale Optimization algorithm [4] and Cat Swarm Optimization [5] were used by earlier authors. In this paper, we come up with a workflow-scheduling algorithm, which modeled by cat swarm optimization considering task priority, which is coming onto cloud console and to effectively map the task onto suitable virtual resources by considering length and processing capacity of tasks.…”

Section: Introductionmentioning

confidence: 99%

An Effective Workflow Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

Mangalampalli¹,

Sree²,

Satish³

et al. 2022

ECS Trans.

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Effective workflow scheduling approach is needed, as it is huge challenge in cloud computing as variable and heterogeneous workflows comes onto cloud console. To handle these complex workflows and to schedule them onto appropriate virtual resources an effective workflow scheduler is necessary. Earlier authors used various nature inspired algorithms to solve this workflow scheduling by minimizing makespan and maximizing resource utilization, but still there is a chance to minimize makespan and improve resource utilization. In this paper, we have calculated the task priorities of all tasks, which are incoming onto cloud console, and thereby mapping tasks onto VMs to effectively map tasks. Cat swarm optimization used to solve scheduling problem and finally we have addressed parameters named as makespan and resource utilization. Simulation was carried out on workflowsim and we evaluated the efficacy of our algorithm by comparing with existing algorithms PSO and CS. From simulation results we observed that our algorithm shows great improvement over existing algorithms for mentioned parameters.

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“…Said Nabi et al[29] contributed an Adaptive PSO assisted task scheduling by introducing an novel strategy for updating the inertia weights called as "Linearly descending and adaptive inertia weight (LDAIW)" for task scheduling in cloud environments. M. Sudheer et al[30] combined PSO with cuckoo search to perform ask scheduling based on the priority of VM and Task in cloud environments.…”

mentioning

confidence: 99%

Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

2023

IJIES

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From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

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Multi Objective Task Scheduling Algorithm in Cloud Computing Using the Hybridization of Particle Swarm Optimization and Cuckoo Search

Cited by 5 publications

References 22 publications

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

An Effective Workflow Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

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