Real-time workflows oriented online scheduling in uncertain cloud environment

Chen, Huangke; Zhu, Jianghan; Zhang, Zhenshi; Ma, Manhao; Shen, Xin

doi:10.1007/s11227-017-2060-4

Cited by 21 publications

(10 citation statements)

References 29 publications

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“…e evaluation results showed that PRS outperformed the cost-savings of modi ed SHEFT (Tang et al 2011) and RTC (Poola et al 2014) algorithms for multiple work ows by 50.94% and 67.23% respectively on workloads of 1000 multiple work ows. ey then extended the PRS into Event-driven and Periodic Rolling Strategies (EDPRS) algorithm (Chen et al 2017). EDPRS tackled a aw in the PRS algorithm, that, if none of the two disruption events happened, the scheduling process could not be pushed forward.…”

Section: Proactive and Reactive Scheduling For Multiple Workflowsmentioning

confidence: 99%

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

2020

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e work ow is a general notion representing the automated processes along with the ow of data. e automation ensures the processes being executed in the order. erefore, this feature a racts users from various background to build the work ow. However, the computational requirements are enormous and investing for a dedicated infrastructure for these work ows is not always feasible. To cater to the broader needs, multi-tenant platforms for executing work ows were began to be built. In this paper, we identify the problems and challenges in the multiple work ows scheduling that adhere to the platforms. We present a detailed taxonomy from the existing solutions on scheduling and resource provisioning aspects followed by the survey of relevant works in this area. We open up the problems and challenges to shove up the research on multiple work ows scheduling in multi-tenant distributed systems.

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Section: Proactive and Reactive Scheduling For Multiple Workflowsmentioning

confidence: 99%

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

2020

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“…This paper mainly focuses on the optimization of makespan and cost, but the QoS of users is also in°uenced by other factors, such as reliability and energy consumption. In this section, we divide the literatures [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] into three subsections based on the di®erent optimization objectives. They are brie°y introduced in Tables 9-11.…”

Section: Cost and Makespan-aware Multi-objective Optimizationmentioning

confidence: 99%

“…However, the most of researches ignored these uncertain factors, which may lead to a large di®erence between the expected behavior and the real execution behavior. To cover the issue, Chen et al 68 designed an uncertainty-aware scheduling framework to reduce the in°uence of uncertain factors on work°ow scheduling. Based on the framework, the dynamic work°ows scheduling algorithm, combining event driven and periodic rolling strategies (EDRPS), was proposed that aims at reducing the economic cost and maximizing resource utilization while meeting the deadline requirements.…”

Section: Priority Based Algorithmmentioning

confidence: 99%

A Review of Cost and Makespan-Aware Workflow Scheduling in Clouds

Zhang

Zhu

et al. 2019

J CIRCUIT SYST COMP

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Scientific workflow is a common model to organize large scientific computations. It borrows the concept of workflow in business activities to manage the complicated processes in scientific computing automatically or semi-automatically. The workflow scheduling, which maps tasks in workflows to parallel computing resources, has been extensively studied over years. In recent years, with the rise of cloud computing as a new large-scale distributed computing model, it is of great significance to study workflow scheduling problem in the cloud. Compared with traditional distributed computing platforms, cloud platforms have unique characteristics such as the self-service resource management model and the pay-as-you-go billing model. Therefore, the workflow scheduling in cloud needs to be reconsidered. When scheduling workflows in clouds, the monetary cost and the makespan of the workflow executions are concerned with both the cloud service providers (CSPs) and the customers. In this paper, we study a series of cost-and-time-aware workflow scheduling algorithms in cloud environments, which aims to provide researchers with a choice of appropriate cloud workflow scheduling approaches in various scenarios. We conducted a broad review of different cloud workflow scheduling algorithms and categorized them based on their optimization objectives and constraints. Also, we discuss the possible future research direction of the clouds workflow scheduling.

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“…[29] In 2018 Chen, Huangke, et al reported a research was on shedulability of tasks that are fault tolerant to increase reliability in clouds fault tolerance is too much important issue. [6] In 2018, Guo, Pengze, et al reported that Existing scheduling approaches for dynamic workflow in cloud environment is inadequate that's why a research on RT task in cloud environment was also done in same year [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…this approach shows that scheduler allow reclamation of a higher amount of system resources at runtime. [8] 3) PMC: It is a programmable MC for mixed-time criticality systems that include both soft and hard.. For a realtime periodic task scheduling, TDES (Timed discrete-event system) model is used. So by this, instead of scheduling tasks preemptively, schedule them non-preemptively to enhance efficiency.…”

Section: )mentioning

confidence: 99%

Scheduling in Real Time System

sirshar¹,

rizwan²,

shabbir³

et al. 2019

Preprint

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Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.

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Real-time workflows oriented online scheduling in uncertain cloud environment

Cited by 21 publications

References 29 publications

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

A Review of Cost and Makespan-Aware Workflow Scheduling in Clouds

Scheduling in Real Time System

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