Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment

Lavanya, M.; Shanthi, B.; Saravanan, S.

doi:10.1016/j.comcom.2019.12.050

Cited by 55 publications

(26 citation statements)

References 24 publications

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“…The MinMin algorithm was used as benchmark because it has been used by several researchers as benchmark [24][25][26][27][28][29][30]18] and it offers easy and robust implementation.…”

Section: Parallel Scheduling Of Grid Jobs On a Quadcore Systemmentioning

confidence: 99%

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

Abraham

Osaisai

Dienagha

2021

AJRCoS

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As Grid computing continues to make inroads into different spheres of our lives and multicore computers become ubiquitous, the need to leverage the gains of multicore computers for the scheduling of Grid jobs becomes a necessity. Most Grid schedulers remain sequential in nature and are inadequate in meeting up with the growing data and processing need of the Grid. Also, the leakage of Moore’s dividend continues as most computing platforms still depend on the underlying hardware for increased performance. Leveraging the Grid for the data challenge of the future requires a shift away from the traditional sequential method. This work extends the work of [1] on a quadcore system. A random method was used to group machines and the total processing power of machines in each group was computed, a size proportional to speed method is then used to estimates the size of jobs for allocation to machine groups. The MinMin scheduling algorithm was implemented within the groups to schedule a range of jobs while varying the number of groups and threads. The experiment was executed on a single processor system and on a quadcore system. Significant improvement was achieved using the group method on the quadcore system compared to the ordinary MinMin on the quadcore. We also find significant performance improvement with increasing groups. Thirdly, we find that the MinMin algorithm also gained marginally from the quadcore system meaning that it is also scalable.

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“…The MinMin algorithm was used as benchmark because it has been used by several researchers as benchmark [24][25][26][27][28][29][30]18] and it offers easy and robust implementation.…”

Section: Parallel Scheduling Of Grid Jobs On a Quadcore Systemmentioning

confidence: 99%

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

Abraham

Osaisai

Dienagha

2021

AJRCoS

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“…Lavanya et al [34] proposed two task scheduling algorithms for heterogeneous systems. Their offline and online scheduling algorithms aimed at reducing the overall makespan of task allocation in cloud computing environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

2021

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The financial data supply chain is vital to the economy, especially for banks. It affects their customer service level, therefore, it is crucial to manage the scheduling of the financial data supply chain to elevate the efficiency of banking sectors’ performance. The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. This work investigates the problem of scheduling the processing of tasks with non-identical sizes and different priorities on a set of parallel processors. An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. The objective is to minimize different cost types while satisfying constraints such as resources availability, customer service level, and tasks dependency relation. The algorithm proved its effectiveness by allocating tasks with higher priority and weight while taking into consideration customers’ Service Level Agreement, time, and different types of costs, which led to a lower total cost of the batching process. The developed algorithm proved effective by testing it on an illustrative network. Also, a sensitivity analysis is conducted by varying the model parameters for networks with different sizes and complexities to study their impact on the total cost and the problem under study.

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“…In 2020, Lavanya et al (2020), developed two scheduling algorithms called as thresholdbased task scheduling (TBTS) algorithm and service level agreement-based load balancing algorithm. TBTS was a two-phase scheduling algorithm that schedules the tasks in a batch.…”

Section: Other Allocation Modelsmentioning

confidence: 99%

Self adaptive fruit fly algorithm for multiple workflow scheduling in cloud computing environment

Aggarwal

Dimri

Agarwal

et al. 2020

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Purpose In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling. Design/methodology/approach To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling. Findings The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively. Originality/value This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

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Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment

Cited by 55 publications

References 24 publications

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain

Self adaptive fruit fly algorithm for multiple workflow scheduling in cloud computing environment

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