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Proceedings ArticleDOI

Resource usage control in multi-tenant applications

TLDR
This paper proposes an approach which applies resource demand estimation techniques in combination with a request based admission control to delay requests originating from tenants that exceed their allocated resource share.
Abstract
Multi-tenancy is an approach to share one application instance among multiple customers by providing each of them a dedicated view. This approach is commonly used by SaaS providers to reduce the costs for service provisioning. Tenants also expect to be isolated in terms of the performance they observe and the providers inability to offer performance guarantees is a major obstacle for potential cloud customers. To guarantee an isolated performance it is essential to control the resources used by a tenant. This is a challenge, because the layers of the execution environment, responsible for controlling resource usage (e.g., operating system), normally do not have knowledge about entities defined at the application level and thus they cannot distinguish between different tenants. Furthermore, it is hard to predict how tenant requests propagate through the multiple layers of the execution environment down to the physical resource layer. The intended abstraction of the application from the resource controlling layers does not allow to solely solving this problem in the application. In this paper, we propose an approach which applies resource demand estimation techniques in combination with a request based admission control. The resource demand estimation is used to determine resource consumption information for individual requests. The admission control mechanism uses this knowledge to delay requests originating from tenants that exceed their allocated resource share. The proposed method is validated by a widely accepted benchmark showing its applicability in a setup motivated by today's platform environments.

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Citations
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Proceedings ArticleDOI

Analyzing the impact of CPU pinning and partial CPU loads on performance and energy efficiency

TL;DR: It is found that less common CPU pinning configurations improve energy efficiency at partial background loads, indicating that systems hosting collocated workloads could benefit from dynamicCPU pinning based on CPU load and workload type.
Proceedings ArticleDOI

Ease.ml: towards multi-tenant resource sharing for machine learning workloads

TL;DR: A novel algorithm is developed that combines multi-armed bandits with Bayesian optimization and proves a regret bound under the multi-tenant setting, aiming for minimizing the total regret of all users running automatic model selection tasks.
Journal ArticleDOI

A systematic review of scheduling approaches on multi-tenancy cloud platforms

TL;DR: A systematic literature review of research studies related to multi-tenancy scheduling approaches on cloud platforms determine the primary scheduling approaches currently used and the challenges for addressing key multi-Tenancy scheduling issues.
Proceedings ArticleDOI

Platform-as-a-Service Architecture for Performance Isolated Multi-tenant Applications

TL;DR: A concrete PaaS enhancement is presented which enables application developers to realize isolation methods for their hosted SaaS application and a case study evaluated the applicability and effectiveness of the enhancement in different environments.
Posted Content

Quality of Experience (QoE) beyond Quality of Service (QoS) as its baseline: QoE at the Interface of Experience Domains

TL;DR: It is concluded that the proposedQoEaaB can bring a new perspective in QoE modeling and assessment toward a more enriched approach to improving the experience based on innovation and deep connectivity among actors.
References
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Book ChapterDOI

A New Approach to Linear Filtering and Prediction Problems

TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
ReportDOI

The NIST Definition of Cloud Computing

Peter Mell, +1 more
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Journal ArticleDOI

A view of cloud computing

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Book

The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling

TL;DR: The intended audience and the goals of the book are to provide computer professionals simple and straightforward performance analysis techniques in a comprehensive textbook.
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