TURBO: an autonomous execution environment with scalability and load balancing features

Parallel Problem Solving From Nature – PPSN X

Yao

2008

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Section: Related Workmentioning

confidence: 99%

Evolutionary Market Agents for Resource Allocation in Decentralised Systems

Lewis

Parallel Problem Solving From Nature – PPSN X

Yao

2008

“…Scalability, however, is a critical factor in load-balancing systems, and though decentralised load-balancing mechanisms have been proposed (e.g. [6]), these rely upon cooperation between nodes within the system; something which we may not be able to assume.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary market agents and heterogeneous service providers: Achieving desired resource allocations

Lewis¹,

2009 IEEE Congress on Evolutionary Computation

Yao³

2009

Abstract-In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential. We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently.We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation.Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.

“…In the field of grid computing, examples include the hierarchical agent-based approach found in Cao et al [9], and TURBO [1]. In the latter, allocations are achieved through the reliance on altruistic behaviour between cooperating peers, which collaborate in order to reach a global objective.…”

Section: Decentralised Approachesmentioning

confidence: 99%

“…The outcome resource allocation occurs when the system is at equilibrium. Figure 1a illustrates the payoff function for s 1 …”

Section: Simple Casementioning

confidence: 99%

“…-the requirement that the environment remain static while the central coordinator is calculating the optimal resource allocation, -that the coordinator has global knowledge of the system and all nodes within it, -that all coordination messages must route through the central point, counteracting the benefit from having resources distributed about the network, reducing scalability [1] and creating a fundamentally brittle system [3]. This brittleness may be mitigated against to a certain degree, with backup coordinator nodes, however even in these cases the wider system is reliant upon the existence and performance of a small number of key nodes.…”

Section: Decentralised Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Resource allocation in decentralised computational systems: an evolutionary market-based approach

Lewis

Auton Agent Multi-Agent Syst

Yao

2009

We present a novel market-based method, inspired by retail markets, for resource allocation in fully decentralised systems where agents are self-interested. Our market mechanism requires no coordinating node or complex negotiation. The stability of outcome allocations, those at equilibrium, is analysed and compared for three buyer behaviour models. In order to capture the interaction between self-interested agents, we propose the use of competitive coevolution. Our approach is both highly scalable and may be tuned to achieve specified outcome resource allocations. We demonstrate the behaviour of our approach in simulation, where evolutionary market agents act on behalf of service providing nodes to adaptively price their resources over time, in response to market conditions. We show that this leads the system to the predicted outcome resource allocation. Furthermore, the system remains stable in the presence of small changes in price, when buyers' decision functions degrade gracefully.