A two-stage scheduling method for deadline-constrained task in cloud computing

He, Xin; Shen, Junmin; Liu, Fagui; Wang, Bin; Zhong, Guoxiang; Jiang, Jun

doi:10.1007/s10586-022-03561-y

Cited by 7 publications

(2 citation statements)

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“…The IGA algorithm consists of the following steps: (1) design the encoding and initialize the population; (2) evaluate fitness as 1/𝐶, where 𝐶 is the total cost; (3) perform selection, crossover, and mutation operations; (4) perform greedy selection operation; (5) generate a new population; (6) repeat steps (2)-( 5) iteratively until the termination criteria are met; (7) decode and obtain the optimal scheduling solution.…”

Section: Proposed Algorithm: Igamentioning

confidence: 99%

“…Task scheduling has been an important problem in the field of cloud services [1][2][3] . Most studies focus on independent tasks scheduling, such as proposing ant colony algorithms [4] , and whale optimization algorithms [5] to reduce costs [6,7] . However, approximate query task scheduling for BDAaaS involves the sequential dependencies between tasks.…”

Section: Introductionmentioning

confidence: 99%

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Task scheduling optimization considering SLA for big data analytics-as-a-service in cloud computing services

Ye,

Cheng,

Jin

2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

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Big Data Analytics-as-a-Service (BDAaaS) is an emerging cloud computing service designed to provide big data analytics. BDAaaS offers approximate query services to meet the requirements of fast response but without high accuracy by users. So, service response in Service Agreement Level (SLA) are more stringent and BDAaaS faces greater risk of penalty payout. In this paper, we proposed a task scheduling model that takes SLA into account for the problem of sequential dependencies of some tasks. And a greedy strategy was used to improve the genetic algorithm to solve the model efficiently. Simulation experimental results show that the method in this paper can obtain task scheduling schemes with lower cost and SLA violation rate under limited resources.

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Section: Proposed Algorithm: Igamentioning

confidence: 99%