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The type of service field requests that a specific level of service be offered to the datagram Some applications may require quick responses to reduce network delays, greater reliability, or higher throughput The total length field states the total length of the datagram (including both header and data) A maximum value of 65,536 bytes is usually imposed, but many networks may only support smaller sizes All networks are guaranteed to support a minimum of 576 bytes The identification field allows datagrams that are part of a sequence to be uniquely identified This field can be thought of as a sequence number, allowing ordering of datagrams that arrive out of sequence Sometimes when packets are sent between network gateways, one gate-way will support only smaller packets The flags field controls whether these datagrams may be fragmented (sent as smaller pieces and later reassembled) Fields marked "do not fragment" are discarded and are undeliverable As datagrams are routed across the Internet, congestion throughout the network or faults in intermediate gateways may cause a datagram to be routed through long and winding paths So that datagrams don't get caught in infinite loops and congest the network even further, the time-to-live counter (TTL) field is included The value of this field is decremented every time it is routed by a gateway, and when it reaches zero the datagram is discarded It can be thought of as a self-destruct mechanism to prevent network overload The protocol type field identifies the transport level protocol that is using a datagram for information transmission Higher-level transport protocols rely on IP for sending messages across a network Each transport protocol has a unique protocol number, defined in RFC 790 For example, if TCP is used, the protocol field will have a value of 6 To safeguard against incorrect transmission of a datagram, a header checksum is used to detect whether data has been scrambled If any of the bits within the header have been modified in transit, the checksum is designed to detect this, and the datagram is discarded Not only can datagrams become lost if their TTL reaches zero, they can also fail to reach their destination if an error occurs in transmission The next two fields contain addressing information The source IP address field and destination IP address fields are stored as two separate 32-bit values Note that there is no authentication mechanism to prove that a datagram originated from the specified source address Though not common, it is possible to use the technique of "IP spoofing" to make it appear that a datagram originated from a specific address, such as a trusted host The final field within the datagram header is an optional field that is not always present The datagram options field is of variable length, and contains flags to control security settings, routing information, and time stamping of individual datagrams The length of the options field must be a multiple of 32 if not, extra bits are added as padding IP Address The addressing of IP datagrams is an important issue, as applications require a way to deliver packets to specific machines and to identify the sender Each host machine under the Internet Protocol has a unique address, the IP address The IP address is a four-byte (32-bit) address, which is usually expressed in dotted decimal format (eg, 19216806) Although a physical address will normally be issued to a machine, once outside the local network in which it resides, the physical address is not very useful Even if somehow every machine could be located by its physical address, if the address changed for any.

You have a few options when it comes to how to store the mapping between the import file and SimpleDB attributes It could just be parameters to the import tool stored in a script or you could make a separate file format for it In more complex cases, having a separate file capable of representing relationships (like XML or JSON) can be valuable These complex cases include times where data is bound for different domains, different regions, and even different accounts In this scenario, you may need a way to store a hierarchy of parameters in your mapping configurationThis might include cases where separate accounts are used to support various deployment scenarios like development, testing, staging, and production

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Problem Suppose the application can be decomposed into groups of semi independent tasks interacting in an irregular fashion The interaction is determined by the flow of data between them which implies ordering constraints between the tasks How can these tasks and their interaction be implemented so they can execute concurrently Context Some problems are most naturally represented as a collection of semi independent entities interacting in an irregular way What this means is perhaps cleare st if we compare this pattern with the Pipeline pattern In the Pipeline pattern, the entities form a linear pipeline, each entity interacts only with the entities to either side, the flow of data is one way, and interaction occurs at fairly regular and predictable intervals In the Event Based Coordination pattern, in contrast, there is no restriction to a linear structure, no restriction that the flow of data be one way, and the interaction takes place at irregular and sometimes unpredictable intervals As a real world analogy, consider a newsroom, with reporters, editors, fact checkers, and other employees collaborating on stories As reporters finish stories, they send them to the appropriate editors; an editor can decide to send the story to a fact checker (who would then eventually send it back) or back to the reporter for further revision Each employee is a semi independent entity, and their interaction (for example, a reporter sending a story to an editor) is irregular Many other examples can be found in the field of discrete event simulation, that is, simulation of a physical system consisting of a collection of objects whose interaction is represented by a sequence of discrete "events" An example of such a system is the car wash facility described in [Mis86]: The facility has two car wash machines and an attendant Cars arrive at random times at the attendant Each car is directed by the attendant to a nonbusy car wash machine if one exists, or queued if both machines are busy Each car wash machine processes one car at a time The goal is to compute, for a given distribution or arrival times, the average time a car spends in the system (time being washed plus any time waiting for a nonbusy machine) and the average length of the queue that builds up at the attendant The "events" in this system include cars arriving at the attendant, cars being directed to the car wash machines, and cars leaving the machines Fig 431 sketches this example Notice that it includes "source" and "sink" objects to make it easier to model cars arriving and leaving the facility Notice also that the attendant must be notified when cars leave the car wash machines so that it knows whether the machines are busy.

For small deployments, the time requirement to process the import might not be a concern For larger data sets that will take hours to import, the percentage of import completion is valuable informationAs you design and implement your tool, consider ways to display the import progress or status

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