Macroeconomics based Grid resource allocation

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Abstract

Resource allocation is the key technology in Grid computing. While most of the existing literature of economics-based Grid resource allocation relies on the basic microeconomic principle, which concerns the behavior of individual agents in the Grid, this paper provides a novel approach based on macroeconomics, which concerns large aggregate behavior instead of individual actions. First, we propose a macroeconomics-based market framework that is well suited for a service-oriented Grid. Then, some macroeconomics-based resource allocation strategies, which can effectively improve the performance of the whole Grid market, are given. Simulation results prove good performance of the proposed method.

Introduction

With the proliferation of the Internet comes the possibility of aggregating vast collections of computers into large-scale service platforms. A new network paradigm known as the Grid [1] articulates a vision of distributed computing in which applications “plug” into a “power Grid” of service resources including computational ability, information, knowledge, etc., when they execute, dynamically drawing what they need from the global supply [2].

A great deal of existing research concerning software mechanism that will be necessary to bring Grid to fruition is underway [3], [4], [5], [6]. Thereby, we are able to move away from implementation details and focus on how to effectively allocate Grid resources.

Because of the dynamic, heterogeneous and autonomous nature of the Grid, resource allocation methods of traditional parallel and distributed computing cannot work. How to effectively allocate Grid resources remains a challenge. Since the core issue in economics is how to effectively allocate scarce resources of the real-world society [7], economics has recently become a research hotspot of Grid resource allocation. Buyya [2] proposed and developed a distributed computational economy-based framework, called Grid Architecture for Computational Economy (GRACE), for resource allocation and to regulate supply and demand of available resources. The proposed economics-based resource scheduling involves some optimization strategies of resource allocation. Wolski et al. [8] investigated G-commerce computational economies for controlling resource allocation in Computational Grid settings. They measured the efficiency of resource allocation under two different market conditions: commodity markets and auctions. Stuer et al. [9] continued the study based on the Wolski et al.’s [8] model and extended it to allow for trading and pricing of substitutable goods, which more closely modeled Grid markets. They also introduced improvements to the optimization algorithms used to compute equilibrium prices. Their contribution focused on commodity markets. In [10], Subramoniam et al. came up with a new algorithm for determining the price for a commodity market. The proposed algorithm for the tâtonnement process gives an efficient performance for different combinations of resources. Li et al. provided a price-directed proportional resource allocation algorithm for solving the grid task agent resource allocation problem in [11]. Huang et al. explored a market-based grid resource trading system from a cognitive computing perspective in [12]. They proposed a hybrid grid resource trading simulation framework and demonstrated a special trading case whereby user agents reserved resources by participating in sequential ascending auctions. The above works make Grid resource allocation more effective. However, they all only consider the basic microeconomic principle.

Economic theory is usually divided into microeconomics and macroeconomics [7]. Microeconomics, also known as price theory, concerns the behavior of individual agents and their interaction in the market, while macroeconomics concerns large aggregate behavior (group of agents), instead of individual actions (a single agent). Most of the existing literature of economics-based Grid resource allocation only relies on the basic microeconomic principle. However, coverage of the Grid service includes end users in different fields; therefore it may be regarded as a social problem. Due to the similarity of resource allocation in the Grid market and the real-world society, merely considering the microeconomic principle is not enough. Macroeconomic guidance and adjustment should also be introduced to maintain the efficiency and fair trading environment of the whole Grid market.

The focus of this paper is on macroeconomics-based resource allocation for a service-oriented Grid. Our solution is novel in the sense that we introduce a hierarchical Grid market model, which maintains the autonomy of Grid end users, but incorporates macro information analysis and guidance of the Grid Information Center (GIC) into resource allocation. Moreover, some realizations of resource allocation strategies driven by the macroeconomic principle are proposed, which can effectively improve the performance of the whole Grid market.

The rest of this paper is organized as follows. In the next section, we describe the design of macroeconomics-based Grid market architecture. Some realizations of macroeconomics-based resource allocation are given in Section 3. In Section 4, we present and discuss simulation experiments. Finally, Section 5 lists some conclusions and discusses some areas of future research.

Section snippets

Motivation and grid market architecture

How to effectively match Grid tasks with available Grid resources is a challenge for a Grid computing system because of the dynamic, heterogeneous and autonomous nature of the Grid. Grid does not own local resources and therefore does not have control over them. Furthermore, the Grid does not have control over the set of tasks submitted to it. The lack of ownership and control results in embarrassment for a traditional central-controlled Grid resource scheduler [13]. This calls for a

Macroeconomics based resource allocation method

There are many different methods of macroeconomics, which can improve the overall benefit of the economics group. In Grid markets, resource consumers are concerned with inquiry efficiency and the failure rate of Grid service requests, and service providers are concerned with their service profits, while as to the whole Grid market, good load balancing can avoid congestion of some local markets within it. In this section, some realizations of resource allocation strategies are proposed to

Simulation experiments

In this section, the effect of the proposed method, namely macroeconomics-based Grid resource allocation (ME-Based in short), is investigated using simulation. Section 4.1 describes the general simulation setup, including the simulated Grid environment and the metrices for evaluating the usefulness of our method. Section 4.2 shows the experiment results and gives brief analysis.

Conclusions

While most of the existing literature of economics-based Grid resource allocation only relies on the microeconomic principle, which concerns the behavior of individual agents in the Grid, this paper provides a novel approach based on macroeconomics, which concerns the overall benefit of the whole Grid market. In this paper, the macroeconomics-based market framework that is well suited for service-oriented Grid is proposed. Then, some realizations of resource allocation strategies driven by

Acknowledgements

This work is supported by National Natural Science Foundation of China (30230350) and Science and Technology Planning Project of Guangdong Province, China (A10202001, 2005B10101033, 2007A020300010).

Peijie Huang received the B.S. degree in computer science from South China University of Technology. He is currently a Ph.D. candidate in College of Computer Science and Engineering, South China University of Technology. His research interests include Grid computing and intelligent computing.

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    Peijie Huang received the B.S. degree in computer science from South China University of Technology. He is currently a Ph.D. candidate in College of Computer Science and Engineering, South China University of Technology. His research interests include Grid computing and intelligent computing.

    Hong Peng received the Ph.D. degree in computer science from Xi’an Jiaotong University and has done postdoctoral research at Zhejiang University. He is currently a Professor and Ph.D. tutor of computer science in South China University of Technology. He has presided over and participated in 15 projects supported by National Natural Science Foundation, 863 Program and Provincial High-tech Program of Guangdong etc. His research interests are in the areas of intelligent network computing, intelligent business and data mining. Professor Peng has published over 40 research papers.

    Piyuan Lin received the B.S. and M.S degrees both in Computer Science from University of Electronic Science and Technology of China (UESTC) respectively in 1984 and 1989 respectively. He is currently a senior member of China Computer Federation (CCF), a professor and the vice-dean of College of Informatics, South China Agricultural University (SCAU). He has presided over and participated in 21 projects supported by National Natural Science Foundation, 863 Program, Provincial High-tech Program of Guangdong, and some companies. His research interests include bioinformatics, data mining, and network & information security. Professor Lin has published more than 40 research papers and 22 books.

    Xuezhen Li received the B.S. degree in computer science from South China Normal University and the M.E. degree in computer science from South China University of Technology. She is currently a Ph.D. candidate in College of Computer and Information Engineering, HoHai University. She is now a lecturer of computer science in Guangdong Technical College of Water Resources and Electric Engineering. Her research interests include intelligent computing and hydro-information technology.

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