What is the difference between E-Science Infrastructures and Distributed Computing Infrastructures?
Both eScience infrastructures and grid computing are based on distributed computing, which is related to loosely-coupled applications. There are several models including client-server, peer-to-peer etc. The issues arising in distributed computing, such as security, failures, network partition etc must be taken into account at design time, since in this context failures are the rule and not the exception. Many large-scale scientific endeavors rely on distributed computing, such as for instance SETI@HOME, the projects using the BOINC middleware etc. However, these projects are not examples of grid computing.

Grid computing refers to the coordinated sharing of resources installed at different administrative domains, usually HPC resources. Each domain is called a Virtual Organization.
In contrast, the resources shared in projects such as SETI@HOME are usually common PC and laptops. 

The grid computing paradigm emerged as a new field distinguished from traditional distributed computing because of its focus on large-scale resource sharing and innovative high-performance applications such as:


distributed supercomputing; 
on demand computing; 
high throughput computing; 
data-intensive computing;


Distributed supercomputing applications require multiple supercomputers to solve problems otherwise too large or whose execution is divided on different components that can benefit from execution on different architectures. This class of applications present a number of challenges to be faced, like resource discovery and scheduling, coordinated startup, configuration at multiple sites, wide area message passing and fault tolerance. An example of such application is SF-Express, a distributed interactive simulation of a military battle. On demand computing refers to the possibility of dynamically acquiring online instruments (e.g. microscopes, satellite sensors and telescopes) connected by high-speed networks to gather and process the data generated. An experiment of microtomography at photon sources in 1999 demonstrated the feasibility of on demand computing. The aim of high throughput computing (HTC) is to schedule many independent jobs for parametric studies or data analysis; in this case a measure of efficiency is the number of jobs processed per unit of time. The two most important tools for HTC, namely Condor and Nimrod are now grid-aware. Data-intensive applications extract new knowledge from geographically distributed data archives or digital libraries; issues related to this class of applications include scheduling and configuration of multiple data flows through several hierarchy levels.

In the past, eScience infrastructures were almost always based on grid computing. Right now, cloud computing is also emerging as a viable alternative in some cases.