Next: Semantically Rich Information Up: A unified approach to Previous: Indexing Strategy

Performance

We have developed a prototype KEYNET system that runs on a network of sparcstations connected by a local area network. The sparcstations are part of the network of Unix workstations maintained by the College for a large community of faculty, students, staff and guests. We ran our tests late at night on relatively unused workstations. There was less activity on the network at these times, but there was some activity even at 3:00 AM, so our results exhibit a variance that reflects this.

The largest search engine we have used consisted of an 8 node network. On each node we index the content labels of 20,000 information objects, using 16 Mbytes of memory. Although the Sparcstations have anywhere from 64 to 128 Mbytes of memory, we found that attempting to allocate more than 16 Mbytes resulted in excessive paging activity. Presumably this is a result of the other activities, some of them in the background, going on at all times throughout the network.

The prototype is fully distributed, using a pure message-passing communication mechanism. All messages are one-way: no process ever waits for a reply to a message. The memory model is local, i.e., a ``shared nothing'' system. It is not a parallel processing algorithm, but we are investigating whether it can be ported to a parallel machine architecture.

The individual nodes are implemented as servers. Specifically, they are implemented as connectionless, multi-threaded, interrupt-driven, stateless servers. Each server is responsible for a fixed amount of memory, 16 Mbytes, which is small enough for page faulting to be rare so that, to a first approximation, the 16 Mbytes can be regarded as physical memory.

Messages between nodes are buffered and sent in groups to improve throughput at the expense of some response time. The amount of buffering can be adjusted so as to maximize throughput for a given response time requirement. In the test runs, the buffer size was adjusted for each configuration so that the frequency with which packets are being sent is approximately the same. Otherwise, when one varies the number of nodes, all one is measuring is the effect of the buffer size. We have a mechanism for flushing buffers when this is deemed to be appropriate. Load balancing is done by measuring the relative performance of the nodes at the beginning of each run, and then allocating tasks to the nodes using this measurement.

In figures 3 and 4, we show the median and 95 percentile response times, respectively, versus throughput for 2-, 4- and 8-node search engines.

The 2-node engine has the best response time for 200 queries/second, but for a larger throughput its response time goes off the scale. Increasing the number of nodes to 4 results in a slightly slower response at 200 queries per second, but now the throughput can be increased to 400 queries per second before the response time goes off the scale. The 8-node engine has the slowest response at low throughputs, but can sustain the highest throughput before the response time goes off the scale.

The prototype is running well enough to obtain throughput and response-time data on randomly generated databases and queries, and the user interface understands UMLS. However, these two components were not yet integrated at the time this paper was written.



Next: Semantically Rich Information Up: A unified approach to Previous: Indexing Strategy


kenb@ccs.neu.edu
Fri Jan 20 21:43:28 EST 1995