Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing

Sreenivasulu, G.; Paramasivam, Ilango

doi:10.1007/s12065-020-00517-2

Cited by 22 publications

(18 citation statements)

References 20 publications

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“…On the one hand, some metrics such as makespan and waiting time are user metrics. On the other hand, metrics such as the cost of the provider and energy consumption are the metrics of the provider [12,13]. The popularity of cloud computing is growing day by day, so with an increasing demand for high-performance computing resources, energy consumption in the cloud data center is greatly increased [14].…”

Section: Task Schedulingmentioning

confidence: 99%

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An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

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Section: Task Schedulingmentioning

confidence: 99%

“…Sreenivasulu and Paramasivam [34] presented a hybrid algorithm to efficiently assign tasks to VMs. The proposed algorithm first uses a hierarchical process to prioritize tasks.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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“…For Refs. [9,[12][13][14], these studies are based on ant colony algorithm to solve scheduling problems in network environment. They improve the pheromone update rule of the ant colony algorithm by setting different parameters and controlling the constraints to make the algorithm perform better in specific network scenarios.…”

Section: Resource Schedulingmentioning

confidence: 99%

“…The data packet transmission only starts within a specific time period. For a complete data D, the transmission time is calculated as Equation (9).…”

Section: Multi-link Optimization Mechanismmentioning

confidence: 99%

“…So far, most existing studies-such as [4,5]-focused on various constraints on the individual links, but ignored the optimization of the overall network. Or, studies such as [9] achieved optimization of the overall network, but not enough consideration for individual links' constraints. However, constraints of individual links should be taken into account, and also overall optimization should be pursued.…”

Section: Introductionmentioning

confidence: 99%

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ACTS: An Ant Colony Based Transmission Scheduling Approach for Cloud Network Collaboration Environment

et al. 2021

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In traditional cloud computing research, it is often considered that the network resources between edge devices and cloud platform are sufficient, and the symmetry between the upward link from edge devices to the cloud platform and the downward link from cloud platform to edge devices is definite. However, in many application scenarios, the network resources between edge devices and cloud platform might be limited, and the link symmetry may not be guaranteed. To solve this problem, network relay nodes are introduced to realize the data transmission between edge devices and the cloud platform. The environment in which network relay nodes that can cooperate with the cloud platform is called cloud network collaborative environment (CNCE). In CNCE, how to optimize data transmission from edge devices to cloud platform through relay nodes has become one of the most important research topics. In this paper, we focus on the following two influencing factors that previous studies ignored: (1) the multi-link and multi-constraint transmission process; and (2) the timely resource state of the relay node. Taking these factors into consideration, we design a novel data transmission scheduling algorithm, called ant colony based transmission scheduling approach (ACTS). First, we propose a multi-link optimization mechanism to optimize the constraint limits. This mechanism divides the transmission into two links called the downlink relay link and uplink relay link. For the downlink relay link, we use the store-and-forward method for the optimization. For the uplink relay link, we use the min–min method for the optimization. We use the ant colony algorithm for the overall optimization of the two links. Finally, we improve the pheromone update rule of the ant colony algorithm to avoid the algorithm from falling into a local optimum. Extensive experiments demonstrate that our proposed approach has better results in transmission efficiency than other advanced algorithms.

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Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment

Ghafari

Mansouri

2023

Cluster Comput

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Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing

Cited by 22 publications

References 20 publications

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

ACTS: An Ant Colony Based Transmission Scheduling Approach for Cloud Network Collaboration Environment

Improved Harris Hawks Optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment

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