Merkat: A Market-Based SLO-Driven Cloud Platform

Costache, Stefania; Parlavantzas, Nikos; Morin, Christine; Kortas, Samuel

doi:10.1109/cloudcom.2013.59

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…CArS is not able to update/adjust the maximum response time SLO agreed. However, the job acceptance can be conditioned to a set of slack factors previously defined large enough, such as Costache et al have proposed for cloud environments. Hence, the Convergence Area Scheduler is feasible and has the capacity to manage the number of SLO violations.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Job acceptance increases due to a long slack factor set for the jobs' processing times. The authors propose a market‐based scheduler to maximize CPU usage in IaaS. The response time SLO is divided into deadline acceptance levels, such as 100, 150, or 200 units of time.…”

Section: Problem Domainmentioning

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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“…• An evaluation of Merkat in simulation and on a real testbed We evaluated the performance of Merkat's proportional-share market in terms of total tenant satisfaction when applications adapt their resource demands per VM to track a tenant-given SLO [16] using CloudSim [17]. We also tested Merkat on the Grid'5000 [18] testbed with two application types: static MPI applications and malleable task processing frameworks [19].…”

Section: Introductionmentioning

confidence: 99%

Market-based autonomous resource and application management in private clouds

Costache

Kortas

Morin

et al. 2017

Journal of Parallel and Distributed Computing

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High Performance Computing (HPC) clouds need to be efficiently shared between selfish tenants having applications with different resource requirements and Service Level Objectives (SLOs). The main difficulty relies on providing concurrent resource access to such tenants while maximizing the resource utilization. To overcome this challenge, we propose Merkat, a market-based SLO-driven cloud platform. Merkat relies on a market-based model specifically designed for on-demand fine-grain resource allocation to maximize resource utilization and it uses a combination of currency distribution and dynamic resource pricing to ensure proper resource distribution among tenants. To meet the tenant's SLO, Merkat uses autonomous controllers, which apply adaptation policies that: (i) dynamically tune the application's provisioned CPU and memory per virtual machine in contention periods, or (ii) dynamically change the number of virtual machines. Our evaluation with simulation and on the Grid'5000 testbed shows that Merkat provides flexible support for different application types and SLOs and good tenant satisfaction compared to existing centralized systems, while the infrastructure resource utilization is improved.

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SLA‐based PaaS profit optimization

Dib

Parlavantzas

Morin

2017

Concurrency and Computation

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International audienceThe socioeconomic impact of cloud solutions and particularly PaaS (platform as a service) solutions is becoming increasingly critical. The main objective of PaaS providers is to generate the maximum profit from the services they provide. This requires them to face a number of challenges such as efficiently managing the underlying resources and satisfying the SLAs of the hosted applications. In this paper, we consider a cloud-bursting PaaS environment where the PaaS provider owns some private resources and is able to rent public cloud resources on demand. In that context, we propose a profit-efficient solution for managing such a system under SLA constraints. We define a profit optimization policy that evaluates the cost of hosting each application using public and private resources and chooses the option that generates the highest profit. During peak periods, the optimization policy considers two more options. The first option is to obtain some resources from running applications, taking into account the payment of penalties if their promised quality of service is affected. The second option is to wait until private resources become available, taking into account the payment of penalties if the quality of service promised to the new application is affected. Furthermore, we designed and implemented an open cloud-bursting PaaS system, called Meryn, which integrates the proposed optimization policy and provides support for batch and MapReduce applications. The results of our evaluation show the effectiveness of our approach in optimizing the provider profit. Indeed, compared to a basic approach, our approach provides up to 11.59% and 9.02% more provider profit in, respectively, simulations and experiments

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Merkat: A Market-Based SLO-Driven Cloud Platform

Cited by 6 publications

References 21 publications

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

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

Market-based autonomous resource and application management in private clouds

SLA‐based PaaS profit optimization

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