22,917 research outputs found
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
A Novel Workload Allocation Strategy for Batch Jobs
The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach
Adaptive Electricity Scheduling in Microgrids
Microgrid (MG) is a promising component for future smart grid (SG)
deployment. The balance of supply and demand of electric energy is one of the
most important requirements of MG management. In this paper, we present a novel
framework for smart energy management based on the concept of
quality-of-service in electricity (QoSE). Specifically, the resident
electricity demand is classified into basic usage and quality usage. The basic
usage is always guaranteed by the MG, while the quality usage is controlled
based on the MG state. The microgrid control center (MGCC) aims to minimize the
MG operation cost and maintain the outage probability of quality usage, i.e.,
QoSE, below a target value, by scheduling electricity among renewable energy
resources, energy storage systems, and macrogrid. The problem is formulated as
a constrained stochastic programming problem. The Lyapunov optimization
technique is then applied to derive an adaptive electricity scheduling
algorithm by introducing the QoSE virtual queues and energy storage virtual
queues. The proposed algorithm is an online algorithm since it does not require
any statistics and future knowledge of the electricity supply, demand and price
processes. We derive several "hard" performance bounds for the proposed
algorithm, and evaluate its performance with trace-driven simulations. The
simulation results demonstrate the efficacy of the proposed electricity
scheduling algorithm.Comment: 12 pages, extended technical repor
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
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Managing Uncertainty: A Case for Probabilistic Grid Scheduling
The Grid technology is evolving into a global, service-orientated
architecture, a universal platform for delivering future high demand
computational services. Strong adoption of the Grid and the utility computing
concept is leading to an increasing number of Grid installations running a wide
range of applications of different size and complexity. In this paper we
address the problem of elivering deadline/economy based scheduling in a
heterogeneous application environment using statistical properties of job
historical executions and its associated meta-data. This approach is motivated
by a study of six-month computational load generated by Grid applications in a
multi-purpose Grid cluster serving a community of twenty e-Science projects.
The observed job statistics, resource utilisation and user behaviour is
discussed in the context of management approaches and models most suitable for
supporting a probabilistic and autonomous scheduling architecture
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