Improving cloud infrastructure utilization through overbooking

Tomás, Luis; Tordsson, Johan

doi:10.1145/2494621.2494627

Cited by 77 publications

(69 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The values for F ree and U nReq are predicted using exponential smoothing [4], [6]. Req is either obtained from previous knowledge of the service or specified by the cloud user.…”

Section: Admission Control With Risk Assessmentmentioning

confidence: 99%

“…In our previous work [4], we demonstrate how resource overbooking can be used to increase provider utilization and revenue. Basic admission control strategies were presented to cope with horizontal elasticity issues while scheduling techniques were proposed to handle the effects of vertical elasticity and predefined VM sizes.…”

Section: Introductionmentioning

confidence: 99%

“…We also use risk assessment information together with the ideas presented in our previous work [4] to minimize the risk being taken even when using the more optimistic approaches. The main steps of the simple admission control method implemented in [4] are outlined in Algorithm 2.…”

mentioning

confidence: 99%

“…The main steps of the simple admission control method implemented in [4] are outlined in Algorithm 2. This algorithm uses a overbooking threshold to decide on admission.…”

mentioning

confidence: 99%

“…-ACSOS: overbooking technique presented in [4] where long term risks are not evaluated (See Algorithm 2). -Pessimistic, Realistic and Optimistic: conservative, regular, and aggressive risk aware admission control technique preAlgorithm 2 Overbooking Admission Control (ACSOS) 1: Let RC used the predicted data center usage 2: Let AppP rof ile the predicted app usage 3: Let T C the total data center capacity 4: Let OBF indicated how overbookable is the data center 5: Let α be the acceptable margin for data center overcapacity 6: if P rediction(RC used ) + AppP rof ile <= T C + α and OBF > OBF threshold then …”

mentioning

confidence: 99%

See 4 more Smart Citations

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

Tomás

Tordsson

2013

2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing

Self Cite

View full text Add to dashboard Cite

Abstract-Elasticity is key for the cloud paradigm, where the pay-per use nature provides great flexibility for end-users. However, elasticity complicates long-term capacity planning for cloud providers as the exact amount of resources required over time becomes uncertain. Admission control techniques are thus needed to handle the trade-off between resource utilization and potential overload. We define a set of admission control algorithms that combine risk assessment methods with a fuzzy aggregation framework. An experimental evaluation using a mixture of bursty and steady applications demonstrate that our algorithms can increase resource utilization by a factor of two while limiting overload problems to a few percent.

show abstract

“…The values for F ree and U nReq are predicted using exponential smoothing [4], [6]. Req is either obtained from previous knowledge of the service or specified by the cloud user.…”

Section: Admission Control With Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The main steps of the simple admission control method implemented in [4] are outlined in Algorithm 2. This algorithm uses a overbooking threshold to decide on admission.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

Tomás

Tordsson

2013

2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing

Self Cite

View full text Add to dashboard Cite

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

Task admission control policy in cloud server pools based on task arrival dynamics

Khojasteh

Mišić

2016

Wireless Communications

View full text Add to dashboard Cite

In this paper, we propose two task admission control algorithms that utilize random task filtering: a lightweight algorithm based on long-term estimates of average utilization and offered load, and a more complex algorithm based on instantaneous utilization. Detailed performance evaluation confirms that both algorithms are able to ensure that the system remains in the stable operating region. We have also found that more aggressive filtering tends to decrease task blocking rate and delay.

show abstract

Improving cloud infrastructure utilization through overbooking

Cited by 77 publications

References 23 publications

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

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

Task admission control policy in cloud server pools based on task arrival dynamics

Contact Info

Product

Resources

About