Exponential Smoothing for Network-Aware Meta-scheduler in Advance in Grids

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

doi:10.1109/icppw.2010.52

Cited by 15 publications

(13 citation statements)

References 15 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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“…This has been actively researched in [11,53,54] by the study of different algorithms for resource behavior forecasting (such as exponential smoothing [55]) with interesting results. For instance, in [55] a comparison between using or not exponential smoothing prediction methods at the scheduling decisions is presented, highlighting their impact on the performance improvement. On the contrary, forecasting is not an exact science, so it can fail and may lead to undesired situations.…”

Section: Qos Mechanismsmentioning

confidence: 99%

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

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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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“…A survey of several prediction techniques is presented in [26]. Examples of techniques include adaptive methods [27], statespace models [28], exponential smoothing [6], and use of control schemes for self-tuning for improved forecasts [29]. As predictions cannot be totally accurate in all situations, recovery mechanisms are needed when problematic situations occur.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…The scheduler achieves safe overbooking by co-allocating CPU-bound applications with network-bound or memory-bound ones (resource multiplexing) and by performing safe time-sharing, i.e., ensuring that peaks and lows in the cohosted VMs do not coincide. The scheduler uses a worst-fit style algorithm that combines the future resource usage (estimated using triple exponential smoothing [6]) with profiling of the application. The selected host for a new VM is the one with the largest residual capacity after adding the new VM (all capacity dimensions weighted).…”

Section: A Experiments Frameworkmentioning

confidence: 99%

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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

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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.

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Exponential Smoothing for Network-Aware Meta-scheduler in Advance in Grids

Cited by 15 publications

References 15 publications

An Autonomic Approach to Risk-Aware Data Center Overbooking

An Autonomic Approach to Risk-Aware Data Center Overbooking

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

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

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