Remote Server

The functionality available in MedChem Explorer is broken down into a list of available computational experiments, including activity prediction, align/ pharmacophore, overlay molecules, conformer generation, property calculation, and database access. Within each experiment, the Web system walks the user through a series of questions that must be answered sequentially. The task is then submitted to a remote server, where it is performed. The user can view the progress of the work in their Web browser at any time. Once complete, the results of the calculation are stored on the server. The user can then run subsequent experiments starting with those results. The Web interface includes links to help pages at every step of the process. 

Integration of Distributed Systems with Remote Servers over the Internet... 

Once a mass spectrum has been obtained, it is possible to perform a library search, in different databases installed in the local computer or in remote servers through the Internet that can help in identification of unknowns. 

Java Runtime Environment including Java Web-Start. ACT is implemented using the Java programming language, and requires a Java Runtime Environment (JRE), vl.4 or newer, to be installed on the users computer. Java Web Start is a technology that permits Java programs from a remote server to be run on the local machine. Java is available from http //www.java.com, with instructions on installation. 

A Pipeline Pilot deployment includes a number of integration components that provide facilities to create a new component to capture a simple command line, or by coding up new functionality in Perl or Python scripts or in Java code, using a component API. To utilize network resources, the user can also configure telnet, ftp, or SOAP components. SOAP components can execute in a multithreaded fashion to parallelize the use of remote servers. 

The stub and the ORB then cooperate to transmit the request to the remote server object. 

The existence of URIs makes it possible to address every document on the Internet. HTTP provides a mechanism whereby if a URI is given, the document represented by that URI can be obtained from a remote server. 

The repository can be on remote servers, or kept locally, enabling the user to maintain their own version history. 

While wireless connectivity has improved the ability to communicate, it has also introduced some serious privacy and security issues. Wireless data is easily intercepted and unless encrypted, it is easy for hackers to read and misuse the data. Mobile computing also supports storing data in the cloud on remote servers, and even when the data is encrypted, issues are raised with tlie owners as to its safety. 

In our approach, we do not parallelize the deconvolution algorithm itself to increase the deconvolution speed of each stack. Instead, we distribute independent tasks, at the image stack level. Thus, our parallel calculations are coarsegrained , the computational element being a single stack. Basically, processing jobs are queued into a pipeline. At its endpoint, job consumer threads (A-N) con-cinrently invoke the deconvolution of queued stacks on remote servers (remote computers A-N) (Fig. 19). 

Fig. 20 Top Measured speedup factor using one to eight remote similar computers in parallel, for 2 different algorithms MGold with 7 iterations and MLE with 50 iterations. Computation was achieved using 120 stacks of 1.4 Mb each, and computers connected via a 1 Gb/s LAN. Bottom Simulation of the speedup factor as a function of the ratio TJTp for two to ten remote servers...

A Stack of 500x400 pixels x 9 planes was acquired every second over a 2-niin period with only 50 ms exposure time per plane. The acquisition of each stack in z-streaming mode took less than 500 ms, allowing more than 500 ms for fluorescence relaxation. Figure 20 shows the measured speedup factor as a fimction of the number of remote servers. The speedup factor is defined as the time required to restore all the stacks in sequential mode, divided by the time measured to restore all the stacks in parallel mode. The only difference between the algorithms is the computing time per stack. 

In the fastest case, it took only 1 min 45 s to process all the data using eight remote servers plus the chent computer, rather than about 9 min in sequential mode, giving a speedup factor greater than 5. 

This use of JavaScript (Scheme 2, line 10) is starting to replace the older mechanism for achieving this effect, which was to initiate a call to a script or program resident on a remote server, which would then assemble an appropriate document for display and return it to the browser. Thus line 8 in Scheme 2 could be replaced with the following ... 

A common form of distributed computing in which execution of a task is shared between a central, often remote, server and local clients communicating via a common protocol. Requests are sent from the client to the server (e.g., database searches), and the results are then sent back to the client which presents them to the user in appropriate form. This mode of operation allows fiexible processing in a network, and combinations of different hardware/operating systems. 

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