Brownout: building more robust cloud applications

Klein, Cristian; Maggio, Martina; Årzén, Karl-Erik; Hernández-Rodríguez, Francisco

doi:10.1145/2568225.2568227

Cited by 120 publications

(135 citation statements)

References 70 publications

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“…In these cases, the induced sudden lack of resources can result in poor performance. However, we focus only on load-balancing solutions, since brownout applications are already taking care of the potential lack of resources [21].…”

Section: Related Workmentioning

confidence: 99%

“…Special to our case is the presence of a controller within each replica [21]. This controller takes care of adjusting the percentage of requests θ i served with the optional components enabled, based on the measured response time t i of the requests served by the replica.…”

Section: Problem Statementmentioning

confidence: 99%

“…Cloud services therefore greatly benefit from selfadaptation techniques [35], such as brownout [21,25]. A brownout service adapts itself by reducing the amount of computations it executes to serve a request, so as to maintain response time around a given setpoint.…”

Section: Introductionmentioning

confidence: 99%

“…Note that executing optional code directly translates into a better service for the end-user and more revenue for the service provider. This approach has proved to be successful for dealing with unexpected events [21]. However, there, brownout services were composed of a single replica, i.e., a single copy of the application, running inside a single VM.…”

Section: Introductionmentioning

confidence: 99%

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Control-theoretical load-balancing for cloud applications with brownout

Dürango

Dellkrantz

Maggio

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -copies of the applications having the same functionalities -for redundancy and adding a load-balancer to direct incoming traffic.However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a selfadaptive application simulator, together with some state-of-theart solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control-theoretical load-balancing for cloud applications with brownout

Dürango

Dellkrantz

Maggio

et al. 2014

53rd IEEE Conference on Decision and Control

Self Cite

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“…For instance, to provide timely response to service requests, a news website with self-adaptive capabilities can react to high response latencies by activating more servers, or reducing the fidelity of contents being served [6,12].…”

Section: Introductionmentioning

confidence: 99%

Impact Models for Architecture-Based Self-adaptive Systems

Moreno

Lopes

Garlan

et al. 2015

Formal Aspects of Component Software

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Abstract. Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decisionmaking in the Rainbow framework for architecture-based self-adaptation.

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Mitigating impact of short‐term overload on multi‐cloud web applications through geographical load balancing

Calheiros

Buyya

2017

Concurrency and Computation

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Managed by an auto-scaler in the clouds, applications may still be overloaded by sudden flash crowds or resource failures as the auto-scaler takes time to make scaling decisions and provision resources. With more cloud providers building geographically dispersed data centers, applications are commonly deployed in multiple data centers to better serve customers worldwide. In this case, instead of sufficiently over-provisioning each data center to prepare for occasional overloads, it is more cost-efficient to over-provision each data center a small amount of capacity and to balance the extra load among them when resources in any data center are suddenly saturated.In this paper, we present a decentralized system that timely detects short-term overload situations and autonomously handles them using geographical load balancing and admission control to minimize the resulted performance degradation. Our approach also includes a new algorithm that optimally distributes the excessive load to remote data centers causing minimum increase of overall response times. We developed a prototype and evaluated it on Amazon Web Services.The results show that our approach is able to maintain acceptable quality of service while greatly increase the number of requests served during overloading periods.

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Brownout: building more robust cloud applications

Cited by 120 publications

References 70 publications

Control-theoretical load-balancing for cloud applications with brownout

Control-theoretical load-balancing for cloud applications with brownout

Impact Models for Architecture-Based Self-adaptive Systems

Mitigating impact of short‐term overload on multi‐cloud web applications through geographical load balancing

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