MapReduce performance discussion: Performance Refinement: Actually, the MapReduce had several works done for improvements of either Map or Reduce, yet, we just discussed about locality which is most related to the network bandwidth in the datacenter. Network bandwidth is a relatively scarce resource in this computing environment. MapReduce conserves network band- width by taking advantage of the fact that the input data is stored on the local disks of the machines that make up our cluster. GFS divides each file into 64 MB blocks, and stores several copies of each block on different machines. The MapReduce master takes the location information of the input files into account and attempts to schedule a map task on a machine that contains a replica of the corresponding input data. Failing that, it attempts to schedule a map task near a replica of that task's input data. That is to say, the master will do its best to ensure that a worker machine that is on the same network switch as the machine containing the data. Therefore, in the map phase, most input data is read locally and consumes no network bandwidth. Experiment: Google had done the performance test using the TeraSort benchmark  which is the typical sorting task with the large data sets. The programs were executed on a cluster that consists of approximately 1800 machines. Each machine has two 2GHz Intel Xeon processors, 4GB of memory, two 160GB IDE disks, and a gigabit Ethernet link. The machines were arranged in a two-level tree-shaped switched network which is the most common structure built for the datacenter. Also, its tree structure has approximately 100-200 Gbps of aggregate band-width available at the root. Result: This is the result comes from the above task, the x-axis represents the seconds elapsing from the MapReduce’s start, and the y-axis represents the throughput measured by MB/s. This sketch has three sub pictures that respectively demonstrate the three phases: Map, Shuffle, and Reduce. Also, we can notice that some shuffle task starts even though input tasks hadn’t finished; and the output task also start in the same way. In fact, this is caused by the pipeline implementation in the MapReduce. It is obviously that input rate is higher than the shuffle rate and the output rate because of our locality optimization most data is read from a local disk and bypasses our relatively bandwidth constrained network. The shuffle rate is higher than the output rate because the output phase writes two copies of the sorted data (MapReduce make two replicas of the output for reliability and availability reasons). Observation: Based on the fact that the input tasks can consume less network bandwidth with locality strategy that shuffle and reduce task can’t use, the throughput gap between input and shuffle or input and output may results from the intensive network usage and remote data access. In fact, network resource in accessing data across machines is the mainly problem. Also, in the MapReduce tasks, as the previous section (which introduce the operations in MapReduce) said, the general operations like the propagation of the program to all worker machines and the delay interaction with GFS to open the large set of input files heavily pose a great overhead to the network usage among many nodes. Summary: Many services in Google need to process large data sets every day. MapReduce is a programming model and an associated implementation for processing and generating large data sets. Programs written in MapReduce are automatically parallelized and executed on a large cluster. The model is easy to use, even for programmer without experience with parallel and distributed system as it hides the details of parallelization, fault-tolerance, locality optimization, and load balancing. The computation takes a set of input key/value pairs, and produces a set of output key/value pairs. Two functions Map and Reduce are used to express this computation. Map takes an input pair and produces a set of intermediate key/value pairs. Reduce accepts an intermediate key and a set of value for that key. It merges together these values to form a possibly smaller set of values. However, behind this powerful service, it still faces the intensive network usage problem like other datacenters. Thus, in the next section, we are going to discuss about DCTCP which is published to solve such serious problem in the large datacenter.