A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques

Karpagam, M.; Geetha, K.; Rajan, C.

doi:10.1007/s00500-019-04484-4

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…It brings great challenges to routing design and congestion design while handling the nodes in mobile opportunistic networks [12]. In the existing literature, interference free clustering algorithms for mobile opportunistic networks (MONs) based on multiobjective optimization are proposed [13]. On the premise of ensuring no communication interference between clusters, many algorithms take network energy consumption and network coverage as optimization objectives [14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

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Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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“…In this section, some recent works on scheduling problem for cloud environments are highlighted. Karpagam et al [16] have proposed a vertical node partitioning approach based on a heuristic and novel SFLA clustering algorithm for scheduling scientific workflows. SFLA with clustering and without clustering have been explored.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

Malti

Benmammar

Hakem

2022

Modelling and Implementation of Complex Systems

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Cloud computing is a standard way of hosting software applications and services that is booming day by day thanks to the different utilities offered to users according to their needs and contracts. Most of these services are in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural features. The objective is to orchestrate the suitable assignment of the submitted tasks to the available resources on the basis of various functional requirements of end users. To overcome the scheduling issue, which is an NP-hard problem, various metaheuristic algorithms are used in literature to achieve near optimal solution. For this purpose, this paper aims to perform a comparative investigation of three common metaheuristic algorithms in the optimization process such as Shuffled Frog Leaping Algorithm (SFLA), Flower Pollination Algorithm (FPA) and Gray Wolf Optimization (GWO). Both standard and synthetic workloads are employed to analyze the performance of these algorithms by evaluating its objective function in term of two metrics which are makespan and resource utilization rate. The simulation results obtained using the CloudSim framework are very satisfactory and clearly show the value of our study.

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“…It works by employing two main models: (a) a LP model that applies linear programming which substantially optimizes the allocation of VM, and, (b) formulation to model virtual machine allocation that solves the issues of resource allocation by using different heuristics that are first—fit decreasing (FFD), best—fit decreasing (BFD), and worst-fit decreasing (WFD). Karpagam et al [ 31 ] presented a heuristic and a novel shuffled frog leaping (SFLA) algorithm for workflow scheduling. The proposed technique is compared with SFLA without clustering and opportunistic load balancing (OLB).…”

Section: Related Workmentioning

confidence: 99%

An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Goyal

Bhushan²,

Kumar³

et al. 2021

Sensors

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Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.

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A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques

Cited by 16 publications

References 15 publications

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

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