On the Simulation of Grid Market Coordination Approaches

Abstract

Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multiattribute n:m allocation decision problem, which is often solved using a central resource broker. However, decentralized approaches to this service allocation problem represent a much more flexible alternative, thus promising improvements in the efficiency of the resulting negotiations and W. Streitberger (B) · S. Hudert · T. Eymann service allocations. This paper compares centralized and decentralized service allocation mechanisms in Grid market scenarios according to a defined set of metrics.

Enabling Usage Control Through Reputation Objects: A Discussion on e-Commerce and the Internet of Services Environments

“…To match providers and consumers, an efficient allocation mechanism between service demand and supply is needed: a market [47]. But introducing a market will lead to other problems, e.g.…”

Services Grids in Industry – On-Demand Provisioning and Allocation of Grid-Based Business Services

“…The above-mentioned heterogeneous requirements of the potential Grid market finally lead to a two-tier market approach which contains a resource market for allocating resources and raw services and a service market for complex services (Streitberger et al 2008). Fig.…”

“…While in small scale settings bids and offers on resources or services might be processed by a central resource broker (centralized market), this approach underperforms or even fails in larger scenarios (Streitberger et al 2008;Buss et al 2008). Therefore, decentralized market approaches based on the "Catallaxy" market paradigm proposed by Hayek (1945) establish a matching of supply and demand in a Peer-2-Peer (P2P) manner and thus overcome the bottleneck found in a centralized market.…”

“…In this concept autonomous agents maximize their own utility despite incomplete information by constantly processing and exchanging price information with agents in their proximity (Eymann et al 2003, pp. 491-495;Streitberger et al 2008). Beck et al (2008) assess the cost differences in three scenarios (product pricing, unit pricing, market pricing) each reflecting a real-world scenario.…”

“…Risk in this context subsumes the probability of violating a SLA accidentally or on purpose. The situation is made more hazardous by the asymmetrical distribution of information between service providers and customers, leading to a principal agent problem (Eymann et al 2008). In the case of a principal agent problem, an agent may try to behave in an opportunistic manner by exploiting its superior access to information.…”