This document discusses load balancing and load sharing approaches for process scheduling. It describes static versus dynamic and deterministic versus probabilistic load balancing algorithms. Centralized algorithms make efficient decisions but have lower fault tolerance, while distributed algorithms avoid bottlenecks but react faster. Issues in designing load balancing algorithms include load estimation policies, process transfer policies, state information exchange policies, location policies, and priority assignment policies. The document provides examples of load balancing in cloud computing and references several papers on load balancing techniques.