25,129 research outputs found
Markets are Dead, Long Live Markets
Researchers have long proposed using economic approaches to resource
allocation in computer systems. However, few of these proposals became
operational, let alone commercial. Questions persist about the economic
approach regarding its assumptions, value, applicability, and relevance to
system design. The goal of this paper is to answer these questions. We find
that market-based resource allocation is useful, and more importantly, that
mechanism design and system design should be integrated to produce systems that
are both economically and computationally efficient.Comment: Fix rotation of figure
A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters
Research interest in Grid computing has grown significantly over the past
five years. Management of distributed resources is one of the key issues in
Grid computing. Central to management of resources is the effectiveness of
resource allocation as it determines the overall utility of the system. The
current approaches to superscheduling in a grid environment are non-coordinated
since application level schedulers or brokers make scheduling decisions
independently of the others in the system. Clearly, this can exacerbate the
load sharing and utilization problems of distributed resources due to
suboptimal schedules that are likely to occur. To overcome these limitations,
we propose a mechanism for coordinated sharing of distributed clusters based on
computational economy. The resulting environment, called
\emph{Grid-Federation}, allows the transparent use of resources from the
federation when local resources are insufficient to meet its users'
requirements. The use of computational economy methodology in coordinating
resource allocation not only facilitates the QoS based scheduling, but also
enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE
Cluster'05, Boston, M
Libra: An Economy driven Job Scheduling System for Clusters
Clusters of computers have emerged as mainstream parallel and distributed
platforms for high-performance, high-throughput and high-availability
computing. To enable effective resource management on clusters, numerous
cluster managements systems and schedulers have been designed. However, their
focus has essentially been on maximizing CPU performance, but not on improving
the value of utility delivered to the user and quality of services. This paper
presents a new computational economy driven scheduling system called Libra,
which has been designed to support allocation of resources based on the users?
quality of service (QoS) requirements. It is intended to work as an add-on to
the existing queuing and resource management system. The first version has been
implemented as a plugin scheduler to the PBS (Portable Batch System) system.
The scheduler offers market-based economy driven service for managing batch
jobs on clusters by scheduling CPU time according to user utility as determined
by their budget and deadline rather than system performance considerations. The
Libra scheduler ensures that both these constraints are met within an O(n)
run-time. The Libra scheduler has been simulated using the GridSim toolkit to
carry out a detailed performance analysis. Results show that the deadline and
budget based proportional resource allocation strategy improves the utility of
the system and user satisfaction as compared to system-centric scheduling
strategies.Comment: 13 page
Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?
In order to meet the performance/privacy requirements of future
data-intensive mobile applications, e.g., self-driving cars, mobile data
analytics, and AR/VR, service providers are expected to draw on shared
storage/computation/connectivity resources at the network "edge". To be
cost-effective, a key functional requirement for such infrastructure is
enabling the sharing of heterogeneous resources amongst tenants/service
providers supporting spatially varying and dynamic user demands. This paper
proposes a resource allocation criterion, namely, Share Constrained Slicing
(SCS), for slices allocated predefined shares of the network's resources, which
extends the traditional alpha-fairness criterion, by striking a balance among
inter- and intra-slice fairness vs. overall efficiency. We show that SCS has
several desirable properties including slice-level protection, envyfreeness,
and load driven elasticity. In practice, mobile users' dynamics could make the
cost of implementing SCS high, so we discuss the feasibility of using a simpler
(dynamically) weighted max-min as a surrogate resource allocation scheme. For a
setting with stochastic loads and elastic user requirements, we establish a
sufficient condition for the stability of the associated coupled network
system. Finally, and perhaps surprisingly, we show via extensive simulations
that while SCS (and/or the surrogate weighted max-min allocation) provides
inter-slice protection, they can achieve improved job delay and/or perceived
throughput, as compared to other weighted max-min based allocation schemes
whose intra-slice weight allocation is not share-constrained, e.g., traditional
max-min or discriminatory processor sharing
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
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