A systematic review of scheduling approaches on multi-tenancy cloud platforms

Ru, Jia; Yang, Yun; Grundy, John; Keung, Jacky; Li, Hao

doi:10.1016/j.infsof.2020.106478

Cited by 25 publications

(14 citation statements)

References 24 publications

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“…In considering the first group, we have found studies that highlight the performance evaluation at task‐level in order to measure and predict requirements of task resources in workflows; 14‐17 improve running processes; 18‐20 and measure performance in workflow engines; 21,22 and in workflow systems 23‐27 . Other approaches involve measuring workflow‐level performance, cloud environment performance, 28‐30 monitoring collected information of events in the execution of a workflow, 31‐34 analytical performance modelling, 35 and simulation 36,37 …”

Section: Related Workmentioning

confidence: 99%

“…The number of links evaluates the number of accesses in a particular task. Jia et al 28 presented a systematic literature review of research studies to determine the primary multitenancy scheduling approaches currently used on cloud platforms and their impact on the performance and consumption of computational resources. Similarly, the study presented by Masdari and Zangakani 29 describes an overview of scheduling schemes of tasks and workflows on multiple clouds.…”

Section: Related Workmentioning

confidence: 99%

“…In considering the first group, we have found studies that highlight the performance evaluation at task-level in order to measure and predict requirements of task resources in workflows; [14][15][16][17] improve running processes; [18][19][20] and measure performance in workflow engines; 21,22 and in workflow systems. [23][24][25][26][27] Other approaches involve measuring workflow-level performance, cloud environment performance, [28][29][30] monitoring collected information of events in the execution of a workflow, [31][32][33][34] analytical performance modelling, 35 and simulation. 36,37 Studies described by Deelman et al, 14 Ferreira et al, 15 and Ferreira et al 16 highlighted techniques related to machine learning algorithms to model and automatically predict the use of such workflow task resources such as: execution time, storage usage, and performance.…”

Section: Measuring Performancementioning

confidence: 99%

See 2 more Smart Citations

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

et al. 2022

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Increasingly enterprises rely on software applications to support their business processes. Since such processes are continually evolving to keep up with market dynamism, companies strive to increase their efficiency, for example, by optimising the integration of applications supporting these processes. Integration platforms are specialised software tools that allow creating integration processes so that applications can share data and functionality. However, this integration involves several challenges, especially when large volumes of heterogeneous data should be integrated and shared. The performance of an integration process, in terms of message processing, is directly related to the run-time system of the integration platform. This article investigates the impact of the volume of messages and the number of threads used by a run-time system on makespan and performance of an integration process. The greater is the number of messages per second received by the integration process, the high is the volume of messages. The study was based on a run-time system with task-based execution model and follows a strict protocol to conduct and report our empirical study. We observed an increment of makespan when increasing the volume of messages to integration processes and different behaviours when increasing the number of threads used in their executions. Makespan reduces as the number of threads increases, but only when the volume of inbound messages is not very high. We confirmed that there is a performance gain by increasing the number of threads to execute an integration process, but observed that the continuous increment of threads leads to degradation of the performance in this model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Measuring Performancementioning

confidence: 99%

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On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

et al. 2022

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“…Database as a service (DBaaS) has become more popular due to the rise of cloud service models. Due to the fact that users are freed from having to worry about database setup and administration, conventional IT departments and business departments may operate at higher levels of productivity [10]. In Section 2, the review of literature is given which highlights the related works in the field.…”

Section: Introductionmentioning

confidence: 99%

Corpus-Based Japanese Reading Teaching Database Cloud Service Model

Liu

2022

Mathematical Problems in Engineering

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With the widespread application of mobile Internet technology, big data, and artificial intelligence, the demand for computing resources in the whole society is growing rapidly. Database cloud service is an online managed high-availability database service built on a cloud computing platform. It can be used as a basic service of the cloud computing platform and has the characteristics of high service availability and high data reliability. The purpose of this paper is to design a corpus-based Japanese reading teaching database cloud service system and integrate a machine learning-based Structure Query Language (SQL) injection detection function for the system. The proposed work is as follows: (1) Design the basic framework of database cloud service. The module of cloud database service is introduced, and the realization logic of main system functions is analyzed. (2) A SQL injection classifier based on feature engineering and machine learning is designed for database cloud services, which can determine whether the input SQL command is an injection statement. The classifier is deployed between the database instance and the front-end server to integrate the SQL injection detection function into the cloud database instance. Then use the support vector machine algorithm to perform simulation experiments on the classifier model to compare the classification performance of the support vector machine algorithm when using different kernel functions. It is found that when the classification decision threshold is 0.5, the classifier using linear kernel function or radial basis kernel function has better classification performance for SQL sentences.

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“…among the tenants. Considering this, it is postulated that segregation of these diverse tenants based on their distinguished features will lead to better storage allocation and improved system performance [7,8].…”

Section: Introductionmentioning

confidence: 99%

TOSDS: Tenant-centric Object-based Software Defined Storage for Multitenant SaaS Applications

Sharma

Kaur

2021

Arab J Sci Eng

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Enormous amounts of unstructured data such as images, videos, emails, sensors’ data and documents of multiple types are being generated daily by varied applications. Apart from the challenges related to collection or processing of this data, its efficient storage is also a significant challenge since this data do not conform to any predefined storage model. Therefore, any enterprise dealing with huge unstructured data requires a scalable storage system that can provide data durability and availability at a low cost. The paper proposes a tenant-centric approach to develop an object-based software defined storage system for SaaS multi-tenant applications. We present TOSDS (Tenant-centric Object-based Software Defined Storage), a system that can efficiently meet the storage requirements of users or tenants with diverse needs who are using a multitenant SaaS application. The experimental verification of TOSDS illustrates its effectiveness in storage utilization as well as tenant isolation.

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A systematic review of scheduling approaches on multi-tenancy cloud platforms

Cited by 25 publications

References 24 publications

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

Corpus-Based Japanese Reading Teaching Database Cloud Service Model

TOSDS: Tenant-centric Object-based Software Defined Storage for Multitenant SaaS Applications

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