Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue

Sulistio, Anthony; Kim, Kyong Hoon; Buyya, Rajkumar

doi:10.1109/ccgrid.2008.65

Cited by 52 publications

(31 citation statements)

References 16 publications

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“…We extend this work by combining the QoS ranges with several other policies, such as Price Discrimination. Another main difference between this paper and the work from Sulistio et al [22] and Dube et al [10] is that the main BLO of our work is the Client Classification instead of the Maximization of the Revenue.…”

Section: Related Workcontrasting

confidence: 44%

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Client Classification Policies for SLA Negotiation and Allocation in Shared Cloud Datacenters

Macías

Guitart

2012

Economics of Grids, Clouds, Systems, and Services

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Abstract. In Utility Computing business model, the owners of the computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) to be provided as well as the economic conditions are established in a Service-Level Agreement (SLA). There are situations in which providers must differentiate the SLAs in function of the type of Client that is willing to access the resources or the agreed QoS e.g. when the hardware resources are shared between users of the company that own the resources and external users. This paper proposes to consider the information of potential users when the SLA is under negotiation to allow providers to prioritize users (e.g. internal users over external users, or preferential users over common users). Two policies for negotiation are introduced: price discrimination and client-aware overselling of resources. The validity of the policies is demonstrated through exhaustive experiments.

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Section: Related Workcontrasting

confidence: 44%

“…Sulistio et al [22] propose overbooking strategies for mitigating the effects of cancellations and no-shows for increasing the revenue. The overbooking policies used in this paper consider in addition the possibility of under-usage of the reserved resources of the client.…”

Section: Related Workmentioning

confidence: 99%

Client Classification Policies for SLA Negotiation and Allocation in Shared Cloud Datacenters

Macías

Guitart

2012

Economics of Grids, Clouds, Systems, and Services

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“…All in all, the main challenge for overbooking is to decide how much excess capacity is appropriate to allocate to minimize the risk of performance degradation [2].…”

Section: Challengesmentioning

confidence: 99%

“…However, overbooking techniques always expose the infrastructure to a risk of resource congestion upon unexpected situations and consequently to SLA violations. The main challenge is how to decide the appropriate level of overbooking that can be achieved without impacting the performance of the cloud services [2]. Admission control techniques are thus needed to handle this trade-off between increase of resources utilization and risk of performance degradation, determining whether a new service should be admitted into the data center by evaluating the associated long term risk.…”

Section: Introductionmentioning

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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“…However, overbooking techniques always exposes the cloud provider to a risk of resource congestion and consequently of SLA violations as the future behavior of the workloads is not known. This is one of the main challenging issues when dealing with overbooking: how to decide the appropriate level of overbooking that can be achieved without impacting the performance under unexpected situations [5].…”

Section: Introductionmentioning

confidence: 99%

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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Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue

Cited by 52 publications

References 16 publications

Client Classification Policies for SLA Negotiation and Allocation in Shared Cloud Datacenters

Client Classification Policies for SLA Negotiation and Allocation in Shared Cloud Datacenters

An Autonomic Approach to Risk-Aware Data Center Overbooking

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

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