A prediction method for job runtimes on shared processors: Survey, statistical analysis and new avenues

IEEE Trans. Cloud Comput.

2014

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.

Section: Prediction Methodsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

IEEE Trans. Cloud Comput.

2014

“…The literature on resource behavior prediction within highly distributed systems such as Grids or clouds is very rich. 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

Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

2013

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.

“…The literature on resource behavior prediction within highly distributed systems such as grids and clouds is very rich. 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].…”

Section: Related Workmentioning

confidence: 99%

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

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

2013

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.