Mechanisms for SLA provisioning in cloud-based service providers

Casalicchio, Emiliano; Silvestri, Luca

doi:10.1016/j.comnet.2012.10.020

Cited by 70 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SLA support for service providers The articles on the sub-topic of SLA support for service providers proposed methods and models that consider the viewpoint of the application service provider. Casalicchio and Silvestri [95] presented a method for analysing the problems of an application service provider that uses a cloud infrastructure to achieve scalable provisioning of its services within specific QoS constraints. The experimental evaluation results of the proposed approach demonstrated that the model was able to manage system resources and that it made the system selfadaptable under a dynamic workload.…”

Section: Cloud Service Providersmentioning

confidence: 99%

Quality of service approaches in cloud computing: A systematic mapping study

Abdelmaboud

Jawawi

Ghani

et al. 2015

Journal of Systems and Software

119

View full text Add to dashboard Cite

Section: Cloud Service Providersmentioning

confidence: 99%

Quality of service approaches in cloud computing: A systematic mapping study

Abdelmaboud

Jawawi

Ghani

et al. 2015

Journal of Systems and Software

119

View full text Add to dashboard Cite

“…[2,3,10,17,19,24,26]) and data center consolidation (e.g. [1,5,13,14,16]) and with respect to our previous results [4] we introduce the following novelties:…”

Section: Research Contributionmentioning

confidence: 99%

“…While Horizontal scaling, vertical scaling and configutation tuning approaches are somentime mixed, optimal placement (e.g. [1,5,13,14,16]) is never considered in combination with the other adaptation strategies.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

Self Cite

View full text Add to dashboard Cite

Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

show abstract

“…A few works [11][12][13][14][15][16] [20]. While, vertically scaling up a VM can cause costly migration if its host has no enough resource.…”

Section: Elasticity Managementmentioning

confidence: 99%

“…Some elasticity managements [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] applied one or the combination of vertical scaling (resizing a VM) and horizontal scaling (adding/deleting VMs) to provide the mapping between elastic applications and VMs leased from a public cloud for minimizing the rent cost in the perspective of cloud users. While they do not consider the placement of VMs on PMs, which is critical for improving energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Server Consolidation for Internet Applications in Virtualized Data Centers

Wang

Song²,

Sun³

et al. 2016

24th High Performance Computing Symposium

View full text Add to dashboard Cite

Server consolidation based on virtualization technology simplifies system administration and improves energy efficiency by improving resource utilizations and reducing the physical machine (PM) number in contemporary service-oriented data centers. The elasticity of Internet applications changes the consolidation technologies from addressing virtual machines (VMs) to PMs mapping schemes which must know the VMs statuses, i.e. the number of VMs and the profiling data of each VM, into providing the application-to-VM-to-PM mapping. In this paper, we study on the consolidation of multiple Internet applications, minimizing the number of PMs with required performance. We first model the consolidation providing the application-to-VM-to-PM mapping to minimize the number of PMs as an integer linear programming problem, and then present a heuristic algorithm to solve the problem in polynomial time. Extensive experimental results show that our heuristic algorithm consumes less than 4.3% more resources than the optimal amounts with few overheads. Existing consolidation technologies using the input of the VM statuses output by our heuristic algorithm consume 1.06% more PMs.

show abstract

Mechanisms for SLA provisioning in cloud-based service providers

Cited by 70 publications

References 12 publications

Quality of service approaches in cloud computing: A systematic mapping study

Quality of service approaches in cloud computing: A systematic mapping study

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Server Consolidation for Internet Applications in Virtualized Data Centers

Contact Info

Product

Resources

About