How does cloud architecture overcome the difficulties faced by traditional architecture? 

In a mapreduce job the master pings each worker periodically. In case a worker does not respond to that system then the system is marked as failed. Even completed tasks are rescheduled because the output was stored in a in a local disk of a worker which failed. Hence mapreduce is able to handle large-scale failures easily by simply restarting a task. The master node always saves itself at checkpoints and in case of any failure it simply restarts from that checkpoint.

The mapreduce framework contains most of the key architecture principles of cloud computing such as:

- Scale: The framework is able to expand itself in direct proportion to the number of machines available.

- Reliable: The framework is able to compensate for a lost node and restart the task on a different node.

- Affordable: A user can start small and over time can add more hardware.

MapReduce is a software framework that was created by Google. It`s prime focus was to aid in distributed computing, specifically large sets of data on a group of many computers. The frameworks took its inspiration from the map and reduce functions from functional programming.

- Multi-threading fetching in Amazon S3 is used to fetch the objects concurrently using the multiple threads and map the task so that fetching can be made simpler. 

- It is not a good practice to increase the threading for a particular object as every node on the server has some bandwidth constraints. 

- It provides user the ease with which they can upload the files and upload the threads in parallel. 

- It provides high speed of data transfer and easy maintenance of the sever as well.

Amazon S3 provides uploading of large files and retrieve small offsets for end-to-end transfer data rates. The large file gets stored into small files that are smaller in size. Amazon S3 stores multiple of files together in a bundle or in a compressed form, For example in .gzip or .gz format and then convert them into Amazon S3 objects. The files get uploaded on the Amazon server by the use of FTP or another protocol and then retrieved through the HTTP GET request. The request includes the defined parameters like URL, offset (byte-range) and size (length).

Every architecture rely on a database that is easy to maintain and gets easily configured Amazon uses the database by the name SimpleDB. This is the database that is used for cloud architecture to track the statuses of the components. The component of the system are asynchronous and discrete, it requires capturing the state of the system so that in any failure the user can easily revert back to its normal configuration. SimpleDB is schema-less database and there is no need to define the structure before the creation of any data. Every controller in the database defines their own structure and link the data to a job.

Amazon SQS is a message passing mechanism that is used for communication between different connectors that are connected with each other. It also acts as a communicator between various components of Amazon. It keeps all the different functional components together. This functionality helps different components to be loosely coupled, and provide an architecture that is more failure resilient system.