Procuring Spontaneous Session-Level Resource Guarantees for Real-Time Applications: An Auction Approach

Harishankar, Madhumitha; Pilaka, Sireesha; Sharma, Pragya; Srinivasan, Nagarjun; Joe‐Wong, Carlee; Tague, Patrick

doi:10.1109/jsac.2019.2916487

Cited by 6 publications

(1 citation statement)

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“…By using pre-defined patterns, a request can be classified as a simple or a complex task and thereby resource allocation was decided for the application. For a model-free approach in [26], authors present a framework to address the rigidness issues in admission control procedures. A learning-based admission control solution was presented in [27] which use the user-experience to grant access to required resources.…”

Section: Related Workmentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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Cloud computing is a paradigm that ensures the flexible, convenient and on-demand provisioning of a shared pool of configurable network and computing resources. Its services can be offered by either private or public infrastructures, depending on who owns the operational infrastructure. Much research has been conducted to improve a cloud’s resource provisioning techniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in resource shortages affecting both providers and consumers. This uncertainty of resource demands by users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted service requests. In this paper, we present Bouncer—a workload admission control scheme for cloud services. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructure’s threshold capacity. By adopting an application-aware approach, we implemented Bouncer on software-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to evaluate our framework’s performance. Our evaluation shows that Bouncer significantly outperforms the conventional service admission control schemes, which are still state of the art.

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Section: Related Workmentioning

confidence: 99%