A hierarchical multi-objective task scheduling approach for fast big data processing

Jalalian, Zahra; Sharifi, Mohsen

doi:10.1007/s11227-021-03960-9

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Clearly, its primary objectives are reducing waiting time for tasks, time for execution tasks, makespan, and improve utilization of resources. Some of them concerned with QoS and satisfying the needs of consumers, while others are concerned with maximizing supplier profits [20].…”

Section: Resource Management and Tasks Schedulingmentioning

confidence: 99%

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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: Resource Management and Tasks Schedulingmentioning

confidence: 99%

“…Initially, each task has a set of characteristics, Task = {ID, deadline, length, budget}. To carefully select the suitable computing node, need to use task information, status of computing node, and resource availability [20]. Tasks are clustered depending on their length and deadline by K-means method.…”

Section: Tasks Classificationmentioning

confidence: 99%

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

2023

IJIES

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“…energy consumption [30]. In the final reset phase, whether the actual total energy consumption is greater than the total energy constraint is judged first.…”

Section: Plos Onementioning

confidence: 99%

Film dance creation practice supported by Cyber Physical System

Lin

Zhang

2023

PLoS ONE

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The traditional eight arts include film and dance. Dance is often included in the creation of films. With the progress of the times, dance has shown unprecedented vitality in film. This also puts forward higher requirements for shooting technology in film creation. To solve the contradiction between high performance of equipment and energy sensing, a new energy aware scheduling framework is proposed based on Cyber Physical System, which utilizes the balance between performance and energy consumption optimization, and uses allocation technology and list scheduling to ensure energy constraints. Besides, a highly energy-efficient and stable scheduling algorithm for film creation is constructed. The algorithm problems are mainly divided into functional safety requirements, verification problems, and energy consumption optimization problems under functional safety. The experimental results show that the system can obtain better schedulability at a lower time complexity and reasonably reflect the dynamic and static energy constraints ratio. The basic framework system based on dynamic step size also achieves better time performance than other step sizes. According to the experiment’s findings, the energy consumption of all scheduling components is relatively low and can be maintained within a specific range. The research and analysis of this study can provide a theoretical reference for the equipment algorithm of film dance creation practice, promote interaction with technical practice, and assist in promoting the development process of film dance creation.

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An extended visual methods to perform data cluster assessment in distributed data systems

Reddy¹,

Prasad²,

Kamatam

et al. 2022

J Supercomput

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A hierarchical multi-objective task scheduling approach for fast big data processing

Cited by 6 publications

References 26 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

Film dance creation practice supported by Cyber Physical System

An extended visual methods to perform data cluster assessment in distributed data systems

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