Network-aware meta-scheduling in advance with autonomous self-tuning system

Tomás, Luis; Caminero, Agustín C.; Carrión, Carmen; Caminero, Blanca

doi:10.1016/j.future.2010.12.004

Cited by 31 publications

(20 citation statements)

References 33 publications

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“…A survey of several prediction techniques is presented in [36]. Examples of these techniques include adaptive methods [37], state-space models [38], exponential smoothing [39], and use of control schemes for self-tuning for improved forecasts [40].…”

Section: Prediction Methodsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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Abstract-Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time -changing the acceptable level of risk -depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50% increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

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Section: Prediction Methodsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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“…A survey of several prediction techniques is presented in [8]. Examples of techniques include adaptive methods [13], state-space models [14], exponential smoothing [24], and use of control schemes for self-tuning for improved forecasts [25].…”

Section: Related Workmentioning

confidence: 99%

Improving cloud infrastructure utilization through overbooking

Tomás

Tordsson

2013

Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

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Despite the potential given by the combination of multitenancy and virtualization, resource utilization in today's data centers is still low. We identify three key characteristics of cloud services and infrastructure as-a-service management practices: burstiness in service workloads, fluctuations in virtual machine resource usage over time, and virtual machines being limited to pre-defined sizes only. Based on these characteristics, we propose scheduling and admission control algorithms that incorporate resource overbooking to improve utilization. A combination of modeling, monitoring, and prediction techniques is used to avoid overpassing the total infrastructure capacity. A performance evaluation using a mixture of workload traces demonstrates the potential for significant improvements in resource utilization while still avoiding overpassing the total capacity.

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“…The support of MSA is addressed by the SA-Layer [18], which provides support for scheduled jobs in that way by using the GridWay metascheduler.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…On the contrary, forecasting is not an exact science, so it can fail and may lead to undesired situations. As a consequence, self-tuning mechanisms [18] appear as a solution in order to avoid as much as possible those unexpected situations and improve the forecasting, but introducing a general overhead. An algorithm to perform resource selection based on performance predictions, as well as though co-allocation of multiple resources is presented in [56] where already made reservations are displaced based on making co-allocation of jobs.…”

Section: Qos Mechanismsmentioning

confidence: 99%

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

Conejero

Caminero

Carrión

et al. 2014

Future Generation Computer Systems

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From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multigrid computing environments. Future generations computer systems, AbstractThe exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher re- source utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities. This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources' usage.

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Network-aware meta-scheduling in advance with autonomous self-tuning system

Cited by 31 publications

References 33 publications

An Autonomic Approach to Risk-Aware Data Center Overbooking

An Autonomic Approach to Risk-Aware Data Center Overbooking

Improving cloud infrastructure utilization through overbooking

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

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