Adaptive admission control of distributed cloud services

Leontiou, Nikolaos; Dechouniotis, Dimitrios; Denazis, Spyros

doi:10.1109/cnsm.2010.5691214

Cited by 22 publications

(11 citation statements)

References 7 publications

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“…For instance, Leontiou et al [28] propose an adaptive feedback scheme with an application queue model to prevent overload of cloud services. Kjaer et al [29] use an online feed-forward control system for web server resource allocation avoiding performance disturbances rejecting individual requests.…”

Section: Admission Controlmentioning

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: Admission Controlmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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“…It used Lyapunov Optimization to design an online admission control, routing, and resource allocation algorithm for a virtualized data center. In [11], an adaptive feedback control scheme alongside with a queue model of the application was employed. Cloud service, modeled using queuing theory, and controlled through adaptive proactive controllers that estimate whether services need some of the resources in the near future or not, was discussed in [1].…”

Section: Introductionmentioning

confidence: 99%

Task admission control for cloud server pools

Khojasteh

Mišić

2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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This paper provides a model for task admission control and resource allocation in the cloud infrastructure. We have examined the performance of resource allocation in this solution through different test scenarios. Also, we have presented two algorithms to control the admission of incoming tasks. These algorithms are based upon establishing thresholds for task arrival rate and task blocking probability. Our experiments indicate that these simple admission control algorithms can vastly improve system performance.

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“…We subsequently removed all problem models with a load > 3. In these cases, it would be preferable to filter applications using admission policies, such as those described in [37], in the management system, ensuring some applications are not accepted by the system.…”

Section: Load-based Evaluationmentioning

confidence: 99%

Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud

et al. 2013

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Cloud computing technologies can be used to more flexibly provision application resources. By exploiting multi-tenancy, instances can be shared between users, lowering the cost of providing applications. A weakness of current cloud offerings however, is the difficulty of creating customizable applications that retain these advantages. In this article, we define a feature-based cloud resource management model, making use of Software Product Line Engineering (SPLE) techniques, where applications are composed of feature instances using a service-oriented architecture. We focus on how resources can be allocated in a cost-effective way within this model, a problem which we refer to as the feature placement problem. A formal description of this problem, that can be used to allocate resources in a cost-effective way, is provided. We take both the cost of failure to place features, and the cost of using servers into account, making it possible to take energy costs or the cost of public cloud infrastructure into consideration during the placement calculation. Four algorithms that can be used to solve the feature placement problem are defined. We evaluate the algorithm solutions, comparing them with the optimal solution determined using an integer linear problem solver, and evaluating the execution times of the algorithms, making use of both generated inputs and a use case based on three applications. We show that, using our approach a higher degree of multi-tenancy can be achieved, and that for the considered scenarios, taking the relationships between features into account and using application-oriented placement performs 25% to 40% better than a purely feature-oriented placement.

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Adaptive admission control of distributed cloud services

Cited by 22 publications

References 7 publications

An Autonomic Approach to Risk-Aware Data Center Overbooking

An Autonomic Approach to Risk-Aware Data Center Overbooking

Task admission control for cloud server pools

Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud

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