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Grid computing [Gri04, FKT01] is about sharing and aggregation of distributed resources such as processing time, storage, and information. A Grid consists of multiple computers linked to one system. Grid computing makes extensive use of remote communication, as the distributed resources need to be managed [KJ04] and computing requests and results need to be transmitted. Distributed object middleware plays an important role here. To find distributed resources, which are typically represented as remote objects, Grids use LOOKUP. Depending on the Grid, the role of the lookup service in the system is often referred to as resource broker , resource manager , or lookup service . The architectures of available Grid computing projects [BBL02] and toolkits vary, but some of the underlying concepts are the same in most of them. In fact, large parts of their architectures are very similar to those of ad hoc networks and P2P system see the discussion above. Code mobility and agents As we discuss in several places in this book, remote invocations can exhibit a variety of limitations and drawbacks when compared to local invocations, for example in performance, configurability, and scalability. Developers cannot easily get around these problems, because they are often faced with applications that inherently need distribution. Consider a simple example: a client requests data from remote objects and evaluates this data locally. Even though the client logic might be quite simple, it cannot be placed on the server, because different clients need different information processing, and the server application developers cannot foresee all possible requirements. As a consequence, the client needs to fetch the data from the server and process it on the client side. The client therefore must send out a network request every time it needs to access data located on the server and the data needs to be sent across the network. This can become slow and consume much network bandwidth, especially if the data volume is large. Code mobility resolves such problems by providing the ability to move code between the nodes of a network. The client code can then migrate to the server, execute within the server context, and return with the result to the client. The custom client code thus executes at the
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Related Concepts, Technologies, and Patterns same site at which the data is located, removing the need for costly data transfer. The ability to relocate code is a powerful concept, but the field is still immature. Fuggetta, Picco, and Vigna provide a conceptual framework for understanding code mobility [FPV98]. They distinguish different code mobility paradigms: Remote evaluation. Code is sent to the server and executed in the server context using the data located at the server. Code on demand. Code is downloaded by a client and executed in the client context using data located at the client. Mobile agents. Code and associated data, and possibly also the execution state, migrates from host A to host B and executes in the context of host B. These code mobility paradigms extend the client/server paradigm used in distributed object middleware concepts. Similarly to other high-level remoting paradigms, code mobility can be realized on top of almost any existing distributed object middleware. For example, in many existing mobile code systems distributed object middleware, such as CORBA or RMI, or their protocols, such as IIOP, are used actually to send the code across the network. The ability of mobile code to change its location means that it is hard to locate the mobile code with a location-dependent ABSOLUTE OBJECT REFERENCE. LOOKUP and LOCATION FORWARDER therefore play a central role in mobile code scenarios, because some entity is needed to inform a client of the new location of mobile code. An extension to the MARSHALLER is required for all code mobility approaches, because the mobile code must be transferred with the messages in a format appropriate for network transmission. In some approaches native machine-code is transferred. This has the disadvantage that the code can only execute on the platform for which it is compiled. As an alternative, byte-code such as compiled Java classes can be transferred, which can execute on any platform for which the byte-code s virtual machine (such as the Java Virtual Machine) is implemented. These variants machine-code or byte-code are often used in statically-compiled languages. In dynamic languages those that allow for behavioral modifications at runtime a Serializer
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