Abstract
AbstractThe accelerated development in peer‐to‐peer and Grid computing has positioned them as promising next‐generation computing platforms. They enable the creation of virtual enterprises for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply‐and‐demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod‐G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker, with deadline and cost constrained scheduling for two different optimization strategies, on the World‐Wide Grid testbed that has resources distributed across five continents. Copyright © 2002 John Wiley & Sons, Ltd.
Highlights
Computational Grids and peer-to-peer (P2P) computing systems are emerging as a new paradigm for solving large-scale problems in science, engineering and commerce [1,2]
It supports budget-based query processing and storage management. It supports storage objects based on bank accounts from which rent is collected for the storage occupied by objects. It supports deadline and budget constrained scheduling algorithms for executing task-farming applications on distributed resources depending on their cost, power and availability and users quality of service requirements Popcorn API-based parallel applications need to specify a budget for processing each of its modules
The brokers can be enabled to issue bids depending on the budget, deadline, job complexity, scheduling strategy and resource characteristics requirements, and Grid service providers (GSPs) can issue asks depending on current load and perceived demand, and price constraints
Summary
Computational Grids and peer-to-peer (P2P) computing systems are emerging as a new paradigm for solving large-scale problems in science, engineering and commerce [1,2] They enable the creation of virtual enterprises (VEs) for sharing and aggregation of millions of resources (e.g. SETI@Home [3]) geographically distributed across organizations and administrative domains. Most of the related work in Grid computing dedicated to resource management and scheduling problems adopt a conventional style where a scheduling component decides which jobs are to be executed at which site based on certain cost functions (Legion [8], Condor [9], AppLeS [10], Netsolve [11], Punch [12]) Such cost functions are often driven by system-centric parameters that enhance system throughput and utilization rather than improving the utility of application processing. Resource consumers certainly prefer to use economic-driven schedulers to effectively utilize their tokens by using lightly loaded cheaper resources
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