Editing Load balancing (computing) (section)

===Hardware architecture===
====Heterogeneous machines====
[[Parallel computing]] infrastructures are often composed of units of different [[computing power]], which should be taken into account for the load distribution.

For example, lower-powered units may receive requests that require a smaller amount of computation, or, in the case of homogeneous or unknown request sizes, receive fewer requests than larger units.

====Shared and distributed memory====
Parallel computers are often divided into two broad categories: those where all processors share a single common memory on which they read and write in parallel ([[Parallel random-access machine|PRAM]] model), and those where each computing unit has its own memory ([[distributed memory]] model), and where information is exchanged by messages.

For [[shared-memory]] computers, managing write conflicts greatly slows down the speed of individual execution of each computing unit. However, they can work perfectly well in parallel. Conversely, in the case of message exchange, each of the processors can work at full speed. On the other hand, when it comes to collective message exchange, all processors are forced to wait for the slowest processors to start the communication phase.
 
In reality, few systems fall into exactly one of the categories. In general, the processors each have an internal memory to store the data needed for the next calculations and are organized in successive [[Computer cluster|clusters]]. Often, these processing elements are then coordinated through [[distributed memory]] and [[message passing]]. Therefore, the load balancing algorithm should be uniquely adapted to a parallel architecture. Otherwise, there is a risk that the efficiency of parallel problem solving will be greatly reduced.

====Hierarchy====
Adapting to the hardware structures seen above, there are two main categories of load balancing algorithms. On the one hand, the one where tasks are assigned by “master” and executed by “workers” who keep the master informed of the progress of their work, and the master can then take charge of assigning or reassigning the workload in case of the dynamic algorithm. The literature refers to this as [[Master/slave (technology)|"Master-Worker"]] architecture. On the other hand, the control can be distributed between the different nodes. The load balancing algorithm is then executed on each of them and the responsibility for assigning tasks (as well as re-assigning and splitting as appropriate) is shared. The last category assumes a dynamic load balancing algorithm.

Since the design of each load balancing algorithm is unique, the previous distinction must be qualified. Thus, it is also possible to have an intermediate strategy, with, for example, "master" nodes for each sub-cluster, which are themselves subject to a global "master". There are also multi-level organizations, with an alternation between master-slave and distributed control strategies. The latter strategies quickly become complex and are rarely encountered. Designers prefer algorithms that are easier to control.

====Adaptation to larger architectures (scalability)====
In the context of algorithms that run over the very long term (servers, cloud...), the computer architecture evolves over time. However, it is preferable not to have to design a new algorithm each time.

An extremely important parameter of a load balancing algorithm is therefore its ability to adapt to scalable hardware architecture. This is called the [[scalability]] of the algorithm. An algorithm is called scalable for an input parameter when its performance remains relatively independent of the size of that parameter.

When the algorithm is capable of adapting to a varying number of computing units, but the number of computing units must be fixed before execution, it is called moldable. If, on the other hand, the algorithm is capable of dealing with a fluctuating amount of processors during its execution, the algorithm is said to be malleable. Most load balancing algorithms are at least moldable.<ref>{{cite book |last1=Asghar |first1=Sajjad |last2=Aubanel |first2=Eric |last3=Bremner |first3=David |title=2013 42nd International Conference on Parallel Processing |chapter=A Dynamic Moldable Job Scheduling Based Parallel SAT Solver |date=October 2013 |pages=110–119 |doi=10.1109/ICPP.2013.20 |isbn=978-0-7695-5117-3 |s2cid=15124201 }}</ref>