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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

7 Scopus Citations
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Original languageEnglish
Article number106478
Journal / PublicationInformation and Software Technology
Online published6 Nov 2020
Publication statusPublished - Apr 2021


Context: Scheduling in cloud is complicated as a result of multi-tenancy. Diverse tenants have different requirements, including service functions, response time, QoS and throughput. Diverse tenants require different scheduling capabilities, resource consumption and competition. Multi-tenancy scheduling approaches have been developed for different service models, such as Software as a Service (SaaS), Platform as a service (PaaS), Infrastructure as a Service (IaaS), and Database as a Service (DBaaS).
Objective: In this paper, we survey the current landscape of multi-tenancy scheduling, laying out the challenges and complexity of software engineering where multi-tenancy issues are involved. This study emphasises scheduling policies, cloud provisioning and deployment with regards to multi-tenancy issues. We conduct a systematic literature review of research studies related to multi-tenancy scheduling approaches on cloud platforms determine the primary scheduling approaches currently used and the challenges for addressing key multi-tenancy scheduling issues.
Method: We adopted a systematic literature review method to search and review many major journal and conference papers on four major online electronic databases, which address our four predefined research questions. Defining inclusion and exclusion criteria was the initial step before extracting data from the selected papers and deriving answers addressing our enquiries.
Results: Finally, 53 papers were selected, of which 62 approaches were identified. Most of these methods are developed without cloud layers’ limitation (43.40%) and on SaaS, most of scheduling approaches are oriented to framework design (43.75%).
Conclusion: The results have demonstrated most of multi-tenancy scheduling solutions can work at any delivery layer. With the difference of tenants’ requirements and functionalities, the choice of cloud service delivery models is changed. Based on our study, designing a multi-tenancy scheduling framework should consider the following 3 factors: computing, QoS and storage resource. One of the potential research foci of multi-tenancy scheduling approaches is on GPU scheduling.

Research Area(s)

  • Systematic review, Survey, Cloud computing, Multi-tenancy, Scheduling