Management platform for Cloud Computing

Sefraoui, Omar; Aissaoui, Mohammed; Eleuldj, Mohsine

doi:10.1109/cloudtech.2015.7336968

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…From the aforementioned sections with few other studies [36][37][38][39][40][41], we compare the performance of the available BaaS platforms. Table 1 provides availability of the several blockchain hosting platforms by top BaaS platforms:…”

Section: Comparison Of Baas Platformsmentioning

confidence: 99%

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Performance Analytical Comparison of Blockchain-as-a-Service (BaaS) Platforms

Onik

Miraz

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Both blockchain technologies and cloud computing are contemporary emerging technologies. While the application of Blockchain technologies is being spread beyond cryptocurrency, cloud computing is also seeing a paradigm shift to meet the needs of the 4 th industrial revolution (Industry 4.0). New technological advancement, especially by the fusion of these two, such as Blockchain-as-a-Service (BaaS), is considered to be able to significantly generate values to the enterprises. This article surveys the current status of BaaS in terms of technological development, applications, market potentials and so forth. An evaluative judgement, comparing amongst various BaaS platforms, has been presented, along with the trajectory of adoption, challenges and risk factors. Finally, the study suggests standardisation of available BaaS platforms.

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Section: Comparison Of Baas Platformsmentioning

confidence: 99%

“…From the aforementioned sections with few other studies [36][37][38][39][40][41], we compare the performance of the available BaaS platforms. In summary, as Azure and AWS have already established cloud infrastructure, they are in a strong position than other services.…”

Section: Comparison Of Baas Platformsmentioning

confidence: 99%

Performance Analytical Comparison of Blockchain-as-a-Service (BaaS) Platforms

Onik

Miraz

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Chauhan and Babar present examples of job migration in such context. Sefraoiu et al describe a middleware that captures job specifications from users and produces configurations for use in a cloud. However, migrating a cloud job to an HPC platform is not a service usually available to external users.…”

Section: Cloud‐hpc Convergence In Data Centersmentioning

confidence: 99%

Reducing the number of response time service level objective violations by a cloud‐HPC convergence scheduler

Kraemer

Maziero

Richard

et al. 2017

Concurrency and Computation

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Summary Job scheduling is an old topic in High‐Performance Computing (HPC), and it is more and more studied in data centers. Large data centers are often split into separate partitions for cloud computing and HPC; each partition normally has its specific scheduler. The possibility of migrating jobs from the HPC partition to the cloud one is a topic widely discussed in the literature. However, job migration from cloud to HPC is a much less explored topic. Nevertheless, such migration may be useful in many situations, in particular when the HPC platform has a low resource usage level, and the cloud usage level is high. A large number of jobs that could migrate from the cloud to the HPC partition may be observed in Google data center workloads. Job scheduling using overbooking strategy is seen as the main reason for the high resource usage level in clouds. However, overbooking can lead to a high rate of rescheduling and job dumping, which potentially causes response time violations. This work shows that HPC platforms can host and execute some cloud jobs with low interference in HPC jobs and a low number of response time violations. We introduce the definition of a cloud‐HPC convergence area and propose a job scheduling strategy for it, aiming at reducing the number of response time violations of cloud jobs without interfering with HPC jobs execution. Our proposal is formally defined and then evaluated in different execution scenarios, using the SimGrid simulation framework, with workload data from production HPC grid. The experimental results show that often, there is a large number of empty areas in the scheduling plan of HPC platforms, which makes it possible to allocate cloud jobs by backfilling. This is due to the sparse HPC job submission pattern and the low resource usage level in some HPC platforms. One performed simulation scenario considered a set of 11K parallel HPC jobs running on a 2560‐processor platform having an average resource usage level of 38.0%. The proposed convergence scheduler succeeded to inject around 267K cloud jobs in the HPC platform, with a response time violation rate under 0.00094% for such jobs, considering 80 processors in the convergence area and no effects on the HPC workload. This experiment considered cloud jobs based on job features of Google public cloud workloads, with a processing time slack factor of 1.25 (which is considered as high priority in the Google cloud SLA—Service Level Agreement). Usually, most cloud jobs show a slack factor higher than 1.25 (most cloud jobs are medium or low priority). The same simulation, repeated with a higher slack factor (4), showed no response time violations.

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