Comparative Analysis of Various Simulation Tools Used in a Cloud Environment for Task-Resource Mapping

Singh, Harvinder; Tyagi, Sanjay; Kumar, Pardeep

doi:10.1007/978-981-15-7533-4_32

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…which include several models, and algorithms, such as heuristic-algorithms, VM migration approach, scalability, more precision, and ease to operate [28]. Additionally provides fundamental classes for characterizing cloud system ingredients such as datacenters, brokering policy, computational resources, VMs and cloudlet [29].…”

Section: Methodsmentioning

confidence: 99%

Dynamic Cost-Optimized Resources Management and Task Scheduling with Deadline Constraint for Mobile Crowd Sensing Environment

2023

IJIES

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The unpredictable and huge data generation nowadays by smart devices from IoT and mobile crowd sensing (MCS) applications like sensors, and smartphones requires processing power and rapid responses in a specific time frame (deadline), and user cost, i.e., budget. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important is task scheduling. Nevertheless, the most of earlier scheduling algorithms have focused on only one evaluation parameter such as makespan or cost and so on, as the scheduling process goal. Optimizing a single metric may not ensure an improvement in the cloud performance in general, so the need may arise for algorithms that concentrate more broadly on enhancing more than one parameter. Therefore, this paper presents a deadline-budget multi-objective dynamic scheduling scheme (DB-MODS) to execute user tasks on VMs within QoS limits in order to finish the task within the deadline and budget if possible. DB-MODS using K-Means based on task length and deadline for clustering incoming tasks into groups and categories VMs based on capacity thresholds. In addition, the task belonging to every cluster is assigned to a suitable VM in VMs groups. based on objective functions, which can be one for user request depending on (deadline and budget) and the second is for system depending on (execution time and cost). This paper used CloudSim plus to simulate and evaluate our approach. The DB-MODS approach performance was compared to scheduling algorithms from the previous literature. Both random and Google cloud jobs (GOCJ) workloads are used to evaluate the efficiency of DB-MODS. The results show that DB-MODS outperforms the other algorithms by minimizing makespan by 41%, energy by 46%, and increasing success ratio by 35%, in comparison to existing load balancing and scheduling methods: EEVS, LAS, Greedy-R, DTS, Random, and Greedy-P in the first scenario. Minimizing makespan by 46%, cost by 44%, and maximize throughput by 80% when compared with MOCS, Max-Min, HABC-LJF, FCFS, MOPSO, Q-learning, MOABCQ-FCFS, and MOABCQ-LJF in a second scenario, and minimizing makespan by 46%, and increase throughput by 39% when compared with HESGA, GA, and ACO in a third scenario in average. In addition to the possibility of applying and employing DB-MODS for scheduling deadline-critical tasks in a heterogeneous cloud system.

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Section: Methodsmentioning

confidence: 99%

Dynamic Cost-Optimized Resources Management and Task Scheduling with Deadline Constraint for Mobile Crowd Sensing Environment

2023

IJIES

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“…The evaluation of various QoS constraints as per the service level agreement between cloud providers and cloud consumers, can be done with the help of cloud simulators. There are number of cloud simulators available in the market that differs in terms of their layers of implementation, applicability, constraints modelling, portability and so on [71] [92]. The comparative analysis of various cloud simulators on the basis of different parameters is shown in Table 5.…”

mentioning

confidence: 99%

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Singh

Tyagi

Kumar

et al. 2021

Simulation Modelling Practice and Theory

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Enhancement in performance of cloud computing task scheduling using optimization strategies

Sandhu,

Faiz,

Kaur

et al. 2024

Cluster Comput

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Comparative Analysis of Various Simulation Tools Used in a Cloud Environment for Task-Resource Mapping

Cited by 7 publications

References 16 publications

Dynamic Cost-Optimized Resources Management and Task Scheduling with Deadline Constraint for Mobile Crowd Sensing Environment

Dynamic Cost-Optimized Resources Management and Task Scheduling with Deadline Constraint for Mobile Crowd Sensing Environment

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Enhancement in performance of cloud computing task scheduling using optimization strategies

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