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Deterministic and non‐deterministic query optimization techniques in the cloud computing

Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP‐hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non‐deterministic methods are two main categories to study these techniques. The deterministic and non‐deterministic query optimization methods can be further divided into three subcategories, cost‐based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future.