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Consider the medical imaging problem described in Sec 313 In this application, a point inside a model of the body is selected randomly, a radioactive decay is allowed to occur at this point, and the trajectory of the emitted particle is followed To create a statistically significant simulation, thousands, if not millions, of trajectories are followed It is natural to associate a task with each trajectory These tasks are particularly simple to manage concurrently because they are completely independent Furthermore, there are large numbers of trajectories, so there will be many tasks, making this decomposition suitable for a large range of computer systems, from a shared memory system with a small number of processing elements to a large cluster with hundreds of processing elements With the basic tasks defined, we now consider the corresponding data decomposition that is, we define the data associated with each task Each task needs to hold the information defining the trajectory But that is not all: The tasks need access to the model of the body as well Although it might not be apparent from our description of the problem, the body model can be extremely large Because it is a read only model, this is no problem if there is an effective shared memory system; each task can read data as needed If the target platform is based on a distributed memory architecture, however, the body model will need to be replicated on each PE This can be very time consuming and can waste a great deal of memory For systems with small memories per PE and/or with slow networks between PEs, a decomposition of the problem based on the body model might be more effective This is a common situation in parallel programming: Many problems can be decomposed primarily in terms of data or primarily in terms of tasks If a task based decomposition avoids the need to break up and distribute complex data structures, it will be a much simpler program to write and debug On the other hand, if memory and/or network bandwidth is a limiting factor, a decomposition that focuses on.

import javaxmail*; import javaxmailinternet*; import javautilProperties;

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One of the consequences of replicating database updates across multiple servers and data centers is the need to decide what kind of consistency guarantees will be maintainedA database running on a single server can easily maintain strong consistencyWith strong consistency, after an update occurs, every subsequent database access by every client reflects the change and the previous state of the database is never seen This can be a problem for a database cluster if the purpose of the cluster is to improve availability If there is a master database replicating updates to slave databases, strong consistency requires the slaves to accept the update at the same time as the master All access to the database would then be strongly consistent However, in the case of a problem preventing communication between the master and a slave, the master would be unable to accept updates because doing so out of sync with a slave would break the consistency guarantee If the database rejects updates during even simple problem scenarios, it defeats the availability In practice, replication is often not done this way A common solution to this problem is to allow only the master database to accept updates and do so without direct contact with any slave databases After the master commits each transaction, slaves are sent the update in near real-timeThis amounts to a relaxing of the consistency guarantee If clients only connect to the slave when the master goes down, then the weakened consistency only applies to this scenario SimpleDB sports the option of either eventual consistency or strong consistency for each read requestWith eventual consistency, when you submit an update to SimpleDB, the database server handling your request will forward the update to the other database servers where that domain is replicatedThe full update of all replicas does not happen before your update request returnsThe replication continues in the background while other requests are handledThe period of time it takes for all replicas to be updated is called the eventual consistency windowThe eventual consistency window is usually smallAWS does not offer any guarantees about this window, but it is frequently less than one second A couple things can make the consistency window larger One is a high request load If the servers hosting a given SimpleDB domain are under heavy load, the time it takes for full replication is increasedAdditionally a network or server failure can block replication until it is resolved Consider a network outage between data centers hosting your data If the SimpleDB load-balancer is able to successfully route your requests to both data centers, your updates will be accepted at both locations However, replication will fail between the two locationsThe data you fetch from one will not be consistent with updates you have applied to the other Once the problem is fixed, SimpleDB will complete the replication automatically Using a consistent read eliminates the consistency window for that requestThe results of a consistent read will reflect all previous writes In the normal case, a consistent read is no slower than an eventually consistent read However, it is possible for consistent read requests to display higher latency and lower bandwidth on occasion.

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