A Learning Automata-Based Scheduling for Deadline Sensitive Task in The Cloud

Sahoo, Sampa; Sahoo, Bibhudatta; Turuk, Ashok Kumar

doi:10.1109/tsc.2019.2906870

Cited by 25 publications

(10 citation statements)

References 35 publications

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“…Next, based on these efficiency metrics, the performance and efficiency of the proposed hybrid algorithm is compared against the modified np‐EDF & p‐EDF and couple of latest cloud computing algorithms as benchmark algorithms. We have considered benchmark‐1 as Earliest Maximal Waiting Time First algorithm which also focus on guaranteed task completion within deadline along with preemption 29 and benchmark‐2 as Learning automata based algorithm which focuses on deadline of the tasks 30 . We ran a random set of tasks in a sequence of 10, 20, 30, 40, and 50 task count, using the proposed hybrid algorithm and existing benchmark algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…We have considered benchmark-1 as Earliest Maximal Waiting Time First algorithm which also focus on guaranteed task completion within deadline along with preemption 29 and benchmark-2 as Learning automata based algorithm which focuses on deadline of the tasks. 30 We ran a random set of tasks in a sequence of 10, 20, 30, 40, and 50 task count, using the proposed hybrid algorithm and existing benchmark algorithms. First, the comparison of proposed algorithm with other benchmark scheme is done on the basis of preemption count.…”

Section: Resultsmentioning

confidence: 99%

“…Next, If determiner function returns true and if min-heap is not empty then the top task is removed from the min heap's root, that is, Line (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), its execution time is added to total burst time. Next, the current time instant is incremented and this task for run 1 unit of time.…”

Section: Proposed Algorithm For Hybrid Edfmentioning

confidence: 99%

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Efficient task scheduling in cloud environment

Rana

Gupta

2022

Int J Communication

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Cloud computing is the provision of on-demand computing resources over the internet and on a pay-as-you-go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p-EDF) and Non-Preemptive EDF (np-EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de-merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Proposed Algorithm For Hybrid Edfmentioning

confidence: 99%

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Efficient task scheduling in cloud environment

Rana

Gupta

2022

Int J Communication

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“…In this paper, the authors introduced a new framework called learning automata scheduling (LAS) which is an adaptive decision-making unit to deal with tasks sensitive to deadline in the cloud environment. They formulated the issue of scheduling tasks as a biobjective to decrease makespan and energy consumption, but they did not use a real dataset and did not consider user budget [14].…”

Section: Related Workmentioning

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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“…Machine learning-based approaches have hence attracted lots of attention in the recent decade [15] [16] [35]. Machine learning approaches are tried to handle makespan of task flows [36], resource utilization rate [37], Quality of Service [38] and pricing models [39].…”

Section: B Related Workmentioning

confidence: 99%