A Survey on Task Scheduling using Intelligent Water Drops Algorithm in Cloud Computing

Ahari, Vinay; Venkatesan, Ragav; Latha, D. Ponmary Pushpa

doi:10.1109/icoei.2019.8862777

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…The proposed research focuses on improving the work of Rafique et al [15]. by considering communication cost between cloud and fog collaboration, latency in fog, this is because the algorithm used in their model exhibit some nature of slowness in the exploration and exploitation in the search space, therefore this research will introduce Spanning Tree (SPT) algorithm which is one of the fastest deterministic algorithms as task scheduling in fog computing is generally dynamic in nature [6].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Jakwa

Gital²,

Souley

et al. 2023

IJASRE

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

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Section: Methodsmentioning

confidence: 99%

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Jakwa

Gital²,

Souley

et al. 2023

IJASRE

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“…Therefore, the travel time between two points is inversely related to the distance between the two points and is directly related to velocity. Also, because there is less soil in areas where more IWDs are available, it can be said that soil is a source of information that remains in the memory of the environment and water drops [38], [39]. With this in mind, the IWD must identify its next step in the new situation by a mechanism.…”

Section: Problem Solving Methodsmentioning

confidence: 99%

A novel energy management framework incorporating multi‐carrier energy hub for smart city

Esapour

Moazzen²,

Karimi

et al. 2022

IET Generation Trans & Dist

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The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi-carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy.

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“…Figure 2 (Ahari, et al, 2019) depicts the scheduling of a set of tasks in cloud computing environment. In this figure, customers submit their tasks to the cloud, and the task scheduling algorithm schedule these tasks to the most appropriate virtual machines running in the hosts at the datacenters in an optimal way.…”

Section: The Task Scheduling Problemmentioning

confidence: 99%

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

2022

IKM

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Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.

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A Survey on Task Scheduling using Intelligent Water Drops Algorithm in Cloud Computing

Cited by 9 publications

References 19 publications

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

A novel energy management framework incorporating multi‐carrier energy hub for smart city

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

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