The ever-changing nature of workloads and the need for effective resource allocation make cloud data center resource optimization an essential concern. Keeping energy usage low while maintaining Quality of Service (QoS) is a common challenge for existing optimization methods. The Salp Swarm Optimization (SSO) framework recommends the most effective resource allocation policy for resource distribution in cloud architecture. This is necessary due to the continuously fluctuating demands for resources and the complexities of the cloud environment. CPU use, power consumption, and infrastructure efficiency are considered during layer allocation. The framework's efficiency is evaluated using data obtained by PlanetLab Virtualized Research to determine how effective the resource allocation is. The utility of resource allocation is demonstrated by increasing PUE and CPU utilization. CloudSim oversees resource allocation, and when it does so, it does so while considering the energy consumption of the host. A high degree of data center efficiency ultimately leads to developing a green data center, which is achieved through cloud resource allocation in conjunction with SSO. The model performance is evaluated by simulating it on the cloud and comparing the results to several other performance factors, including make span, delay, and throughput. The proposed SSA model increases the sustainability ratio of 92. 07%, resource optimization ratio of 98. 54%, space optimization ratio of 90. 15%, time efficiency ratio of 92. 20%, and performance ratio of 93. 29% compared to other existing models.

The 21st century can be considered the world century of water and the main war of countries over the possession of world water resources. Because the Middle East and Africa region are one of the arid and water-scarce regions of the world, the countries of this region are facing a water shortage crisis. A crisis from which not coming out can cause many problems for nations. Increasing world population and increasing demand for water and food products are other reasons for the need to pay attention to water resources management activities. Macro water resources management policies, in fact, determine the relationship between development and how to use these resources with national development goals. The first step in formulating macro-policies for water resources management is to propose different options according to the limitations and comprehensive goals of water resources development and management. The principles that have been considered in the current structure of water resources management organizations, considered water resources as the main element of land management and sustainable development of the basin, which should be managed by a single organization in the natural boundaries of the catchment people's participation and cooperation is perhaps the most effective factor in the evolution of water resources management system. This partnership can be mainly considered in the two areas of investment in water resources development projects and policy impact.

Introduction: Due to the variation of rainfall and inflow regimes in different years, it is necessary to adopt a suitable management model for reservoir operation. Water requirement may differ at different demand sites and therefore it can be satisfied with one reservoir or a network of reservoirs. Different methodologies are adopted for optimal operation of a system of reservoirs. These may be classified in different groups, Stochastic Dynamic Programming (Braga et al., 1991, Ghahraman and Sepaskhah, 2002), Fuzzy Rule Based Programming (Faye et al., 1991), Linear Programming (Mohan and Raipure, 1992) Deterministic Dynamic Programming- Regression Based (Karamouz et al., 1992), Mixed Integer Non-Linear Programming (Teegavarapu and Simonovic, 2000), and Dynamic Programming And Artificial Neural Network (Chnddramouli and Raman, 2001),. Since nearly all dams in Iran regulate water for agricultural uses, a wise discipline should be adopted for best allocation of limited water (especially in droughts) considering the uncertain future river yield.

Calculating total cast of bank resources procurement methods which include current -free loan deposit, saving interest-free loan deposit, regular and net short-term investment deposit, long-term investment deposit and surety bond cash deposit and presenting their optimal integration require precise scientific studies. Hence, this study is an attempt to know which methods are the best optimal integration banking resources. Linear and ideal planning techniques are used to find an optimal solution according to existing mathematical models. We use three algorithms to construct mathematical models. In the suggested mathematical models, linear planning has 6 variables and 2 constraints in getting no information algorithm and 6 variables and 7 constraints in getting information algorithm and the problem is solved by WINQSB software. But, 6 variables and 8 constraints are solved by LINGO8 software. The results of the study show that presenting an optimal integration of resources procurement methods by using mathematical models is possible and is applicable with regard to determining rational and suitable constraints and ideals for all resource procurement methods. In addition, with regard to the calculation and investigation of procurement cost in financial procurement methods, it is found out that total cost (i.e. real operational cost plus nonoperational cost) is the basis of judgment for studying resource procurement cost. Further, as with the total cost of resource procurement methods, current interest-free loan deposit and long-term investment deposit are the most expensive methods while surety bond cash deposit is the cheapest resource procurement method and other methods fall in between.