Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

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

doi:10.1007/s11277-021-09018-6

Cited by 14 publications

(7 citation statements)

References 20 publications

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“…The authors report a decrease in energy consumption and an increase in performance compared to a multi-objective monotonically increasing sorting-based algorithm. Mangalampalli et al [15] elaborate on the task scheduling problem in cloud computing, emphasizing energy consumption and power cost reduction in data centers. Their approach utilizes the WOA to schedule tasks, mapping them to suitable virtual machines (VMs) considering task computations and VM priorities.…”

Section: Related Workmentioning

confidence: 99%

“…In the whale optimization algorithm (WOA), as introduced in [15,20], three main phases are defined: the encircling phase, the exploitation phase, and the exploration phase.…”

Section: Woa-based Offloading Techniquementioning

confidence: 99%

“…The constructed model facilitates more efficient navigation in the decision space by considering the inherent structure, nodes' physical capabilities, and network dependencies within the computational continuum. On top of the graph model, we map and adapt the whale optimization algorithm (WOA) [15] to explore solution spaces and accelerate convergence toward optimal solutions. The offloading decision variables are represented as a binary vector, where each index corresponds to a specific computational node, and the total number of nodes determines the vector's dimension.…”

Section: Introductionmentioning

confidence: 99%

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Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

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Section: Related Workmentioning

confidence: 99%

“…In the whale optimization algorithm (WOA), as introduced in [15,20], three main phases are defined: the encircling phase, the exploitation phase, and the exploration phase.…”

Section: Woa-based Offloading Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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“…In [8] this paper introduces a new task scheduling algorithm based on the Whale optimization algorithm, whose goal is to schedule tasks on appropriate VMSs based on the calculation of Task and VM priorities, as well as to reduce datacenter power costs and energy consumption. First, the priorities for tasks and VMs are calculated in order to effectively map tasks onto VMs and thus evaluate the multi-objective fitness function that addresses energy consumption and power cost at datacenters.…”

Section: Related Workmentioning

confidence: 99%

RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

2022

IJIES

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Cloud computing, a term which has become very popular on the internet platform which has evolved over time and gained more attention due to various features that are being introduced like multiplex numerous users on the identical physical structure, computing of resource on request and many more. Cloud computing is considered to be an enormous pool having essential resources which the users can make use of to complete their task through the internet. Therefore, in order to complete the tasks requested by the users, a scheduling algorithm is required making scheduling a vital part of cloud computing. Though numerous solutions with respect to scheduling techniques have been proposed in in recent literature, researcher's continuously carryout performance upgradation by considering a mixture of QoS parameters thereby enhancing the cloud performance. In this regard, we had introduced an optimal and ideal task scheduling algorithm and challenge the other prevailing algorithms in terms of QoS parameters like makespan and energy. The proposed algorithm increases the performance of the scheduling in cloud by making use of an enhanced meta-heuristic hybrid algorithm called Rider Cuckoo Optimization Algorithm (RCOA), which is an advanced version of the already existing Rider Optimization Algorithm. The proposed RCOA technique had produced an improvement of 5.6%, 4.36% and 2.27% for makespan and had reduced the energy to the tune of 16.8%, 19.18%, and 16.15% when compared with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Rider Optimization Algorithm (ROA) algorithms respectively when used with 25 VMs. Also the proposed approach attains the objective of optimal scheduling swiftly and achieves convergence in a small duration of time.

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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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Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Cited by 14 publications

References 20 publications

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

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

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