In this paper we present the Chelonia storage cloud middleware. It was designed to fill the requirements gap between those of large, sophisticated scientific collaborations which have adopted the grid paradigm for their distributed storage needs, and of corporate business communities which are gravitating towards the cloud paradigm. The similarities to and differences between Chelonia and several well-known grid- and cloud-based storage solutions are commented. The design of Chelonia has been chosen to optimize high reliability and scalability of an integrated system of heterogeneous, geographically dispersed storage sites and the ability to easily expand the system dynamically. The architecture and implementation in term of web-services running inside the Advanced Resource Connector Hosting Environment Dameon (ARC HED) are described. We present results of tests in both local-area and wide-area networks that demonstrate the fault-tolerance, stability and scalability of Chelonia.
Unlike Chelonia, iRODS @cite_4 does not provide any storage itself but is more an interface to other, third-party storage systems. Based on the client-server model, iRODS provides a flexible data grid management system. It allows uniform access to heterogeneous storage resources over a wide area network. Its functionality, with a uniform namespace for several Data Grid Managers and file systems, is quite similar to the functionality offered by our gateway module. However, iRODS uses a database system for maintaining the attributes and states of data and operations. This is not needed with Chelonia's gateway modules.
@cite_4
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Data Grids are used for managing massive amounts of data (Peta scale) that are distributed across heterogeneous storage systems. As such they are complex in nature and deal with multiple operations in the life-cycle of a data set from creation to usage to preservation to final disposition. Administering a data grid can be very challenging (not only for system administrators, but also for data providers and user communities). Data grids are reactive systems that handle events based on contextual information. They also maintain transactional capabilities in order to ensure consistency across distributed storage systems. We are developing a data grid system called integrated Rule Oriented Data Systems (iRODS) manage the phases of the data life-cycle using ECA-type rules. Such a system not only captures the complex operational policies of a data grid but also provides a declarative semantics for describing event processing based on a side effects ontology and context information stored in the data grid. In this paper we describe the event management and processing being implemented in iRODS and how a distributed rule engine is used to handle actions in a data grid. The iRODs data grid can be viewed as a complex, distributed event processing system providing data life-cycle management capabilities using a rule-oriented architecture.