Autonomous Resource Selection for Decentralized Utility Computing

Costa, Paolo; Napper, Jeff; Pierre, Guillaume; Steen, Maarten van

doi:10.1109/icdcs.2009.70

Cited by 29 publications

(36 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our approach relies on autonomous decisions taken by each participants of the protocol, without any central coordinator. The autonomous resource selection presented in [5] organizes nodes in a peer-to-peer system over a multi-dimensional space, upon which lookup queries are issued to select nodes matching the specified requirements. Similarly to ours, this protocol has built-in support for multi-dimensional attributes.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Multi-dimensional Slicing for Large-Scale Distributed Systems

Pasquet

Maia

Rivière

et al. 2014

Distributed Applications and Interoperable Systems

View full text Add to dashboard Cite

Abstract. Slicing is a distributed systems primitive that allows to autonomously partition a large set of nodes based on node-local attributes. Slicing is decisive for automatically provisioning system resources for different services, based on their requirements or importance. One of the main limitations of existing slicing protocols is that only single dimension attributes are considered for partitioning. In practical settings, it is often necessary to consider best compromises for an ensemble of metrics.In this paper we propose an extension of the slicing primitive that allows multi-attribute distributed systems slicing . Our protocol employs a gossip-based approach that does not require centralized knowledge and allows self-organization. It leverages the notion of domination between nodes, forming a partial order between multi-dimensional points, in a similar way to SkyLine queries for databases. We evaluate and demonstrate the interest of our approach using large-scale simulations.

show abstract

Section: Related Workmentioning

confidence: 99%

Autonomous Multi-dimensional Slicing for Large-Scale Distributed Systems

Pasquet

Maia

Rivière

et al. 2014

Distributed Applications and Interoperable Systems

View full text Add to dashboard Cite

show abstract

“…Lumezanu et al examined the problem of optimal resource allocation for event-driven distributed infrastructures and proposed a scalable distributed algorithm to maximize the total system utility [15]. The work in [5] presented a fully decentralized resource selection algorithm by which resources autonomously select themselves in large-scale utility computing infrastructures. In this paper, we propose a combination of multi-objective optimization with a stressstrain curving method for automated and efficient server provisioning.…”

Section: Related Workmentioning

confidence: 99%

“…Autonomic resource provisioning in data center servers has been an active and important research area [1], [5], [6], [16], [17], [18], [19], [21], [23]. Many of previous efforts applied the utility computing paradigm to achieve multiple objectives of a data center.…”

Section: Introductionmentioning

confidence: 99%

aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

Lama

Zhou

2011

2011 International Conference on Parallel Processing

View full text Add to dashboard Cite

Abstract-A modern data center built upon virtualized server clusters for hosting Internet applications has multiple correlated and conflicting objectives. Utility-based approaches are often used for optimizing multiple objectives. However, it is difficult to define a local utility function to suitably represent one objective and to apply different weights on multiple local utility functions. Furthermore, choosing weights statically may not be effective in the face of highly dynamic workloads. In this paper, we propose an automated multi-objective server provisioning with stress-strain curving approach (aMOSS). First, we formulate a multi-objective optimization problem that is to minimize the number of physical machines used, the average response time and the total number of virtual servers allocated for multi-tier applications. Second, we propose a novel stress-strain curving method to automatically select the most efficient solution from a Pareto-optimal set that is obtained as the result of a nondominated sorting based optimization technique. Third, we enhance the method to reduce server switching cost and improve the utilization of physical machines. Simulation results demonstrate that compared to utility-based approaches, aMOSS automatically achieves the most efficient tradeoff between performance and resource allocation efficiency. We implement aMOSS in a testbed of virtualized blade servers and demonstrate that it outperforms a representative dynamic server provisioning approach in achieving the average response time guarantee and in resource allocation efficiency for a multi-tier Internet service. aMOSS provides a unique perspective to tackle the challenging autonomic server provisioning problem.

show abstract

“…We proved using simulations and actual deployments that our approach scales well with the number of nodes as well as the number of supported attributes. For more information we refer the reader to [2].…”

Section: Resource Selection Based On Static Attributesmentioning

confidence: 99%