A Self-Scalable and Auto-Regulated Request Injection Benchmarking Tool for Automatic Saturation Detection

Tchana, Alain; Dillenseger, Bruno; Palma, Noël De; Etchevers, Xavier; Vincent, Jean-Marc; Salmi, Nabila; Harbaoui, Ahmed

doi:10.1109/tcc.2014.2321169

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of cloud computing, RUBiS has been employed in the construction of a performance model of a 3-tier application in cloud environments [9] and on the evaluation of a Benchmark-as-a-Service platform, enabling the determination of the bottleneck tier and to tune the application servers to improve application performance [11]. The used methodology is not limited to RUBiS and thus it can be extrapolated to other workload categories [2].…”

Section: Workloadmentioning

confidence: 99%

Workload modeling for resource usage analysis and simulation in cloud computing

Magalhães

Calheiros

Buyya

et al. 2015

Computers & Electrical Engineering

View full text Add to dashboard Cite

Section: Workloadmentioning

confidence: 99%

Workload modeling for resource usage analysis and simulation in cloud computing

Magalhães

Calheiros

Buyya

et al. 2015

Computers & Electrical Engineering

View full text Add to dashboard Cite

“…Data semantic information outcomes are applied into the cloud server for evaluating the authenticity of each cloud client [1]. Preventing anomaly based intrusion detection systems can also be applied for security solution [2]. Another approach is a secure cloud computing based framework for big data information management of smart grid.…”

Section: Related Workmentioning

confidence: 99%

A Security Solution for Cloud Users to Protect the Private Data through Fuzzy Bayesian Decision Method (FBDM)

Poorvadevi¹,

Rajalakshmi²,

Devi³

2015

IJCA

View full text Add to dashboard Cite

Fuzzy technique is a forum which provides the linguistic object sets and solutions in certainty and uncertainty based elements. Numbers of decisions were derived for various domains by using the fuzzy modeling method. In fuzzy there will be a one approach is, appropriate and approximate reasoning, this can be used to find the solution for critical problem which is happening in all platforms. Likewise, by using fuzzy Bayesian decision method we are trying to obtain the solution for cloud security and privacy issue. If user is consuming any resource or services from the cloud service provider such as, communication, monitoring services, instant transaction, verification of service availability, ensuring the service access privileges, however these applications and processing data sets are stored in the cloud database. Still, customers do not believe the security measurements and storage of secret information which is offered by the cloud vendors. Cloud users are in need of protecting the confidential or private data by embedding and applying their own security tools and parameters. This proposed model which helps to an end-users whoever in need of securing the individual data in cloud environment by using the certain sort of mathematical based derived functions and apply these outcomes into the fuzzy Bayesian decision block to eliminate and prohibit the hackers entry by setting the threshold log value in cloud environment to achieve an efficient security results General TermsCloud security.

show abstract

“…Cloud services are now established as an important part of the expenses of many companies. Managing applications on virtual machines (VMs) as imposed by the IaaS (Infrastructure as a Service) [1] cloud model is a difficult task [2], [3]. The user is the one that has to provide fault tolerance, scalability on workload increase, etc.…”

Section: Introductionmentioning

confidence: 99%

Your Containers Should be WYSIWYG

Bacou

Tchana

Hagimont

2019

2019 IEEE International Conference on Services Computing (SCC)

Self Cite

View full text Add to dashboard Cite

Modern cloud platforms rely on containers in order to deploy applications and allocate resources to them. Users of Container-as-a-Service platforms interact with another layer of abstraction, container orchestrators, to set resource allocations. Regarding the CPU allocation, orchestrators can use one of two strategies to apply the specified allocation: (1) the allocation of cores, reserved for one application; or (2) the allocation of quotas, which can be provided by any of the available processors. However current orchestrators only use the quota strategy. We benchmark both, demonstrating that the quota strategy can show up to 68% of degradation in our experiments when compared to the first strategy. We identify that this degradation comes from violating what we call the What You See Is What You Get (WYSIWYG) principle: a container's view of its available resources is wrong under the quota strategy. We state that a better trade-off can be found in combining these two strategies, and we design a hybrid resource allocation algorithm that can be integrated into any container orchestrator. Our evaluations show that it prevents resource management problems that come from allocating cores, while canceling the performance overhead associated with the quota allocation strategy that violates the WYSIWYG principle.

show abstract

A Self-Scalable and Auto-Regulated Request Injection Benchmarking Tool for Automatic Saturation Detection

Cited by 5 publications

References 21 publications

Workload modeling for resource usage analysis and simulation in cloud computing

Workload modeling for resource usage analysis and simulation in cloud computing

A Security Solution for Cloud Users to Protect the Private Data through Fuzzy Bayesian Decision Method (FBDM)

Your Containers Should be WYSIWYG

Contact Info

Product

Resources

About