Mathematical Models Sample Clauses
Mathematical Models. The mathematical model is designed for SLA violation detection, power consumption, efficient VM placement and migration techniques in cloud data centers. To reduce power consumption, DVFS technique plays a significant role in all electronic devices. It has in-built frequency controller to supply appropriate voltage based on the requirement of VMs. Unpredicted load of cloud data centers consume more energy, thus increasing operational cost. However, energy-efficient resource management technique helps to minimize resource allocation effectively. Over usage or under usage of cloud data centers accidentally increases cost of energy. Many users access same host for different needs; therefore, unbalanced load of data centers consumes more energy and violate SLA procedures. Moreover, dynamic placement of VMs and on-demand resource allocation on those VMs sometimes failed to satisfy user requirements. While meeting SLA procedure, cloud users always expect quality of services from the cloud providers. Reducing energy consumption and SLA violation, we move on to efficient resource allocation techniques for minimizing such problems. To ensure the expected quality of services, SLA-aware MBFD and EMPMU algorithms are used for power-aware resource provisioning without violating SLA procedures.
Mathematical Models. Computer models based upon established mathematical criteria were undertaken to produce local distant currant movements. Actual measurements were used to validate these models. This work had the purpose of examining the possibilities of unusual water movements, which might bring contaminants from the more populated areas to the North. No unusual conditions emerged from this analysis. However, a mathematical study of the maximum shut-down surge affecting the intake chamber, did suggest that provision should be made for a surge spillway to avoid possible damage to the chamber roof. Vertical temperature and salinity profiles showed a characteristic density stratification for the area. At the intake location, the thickness of the upper homogenous layer varied around 5m. The density difference between the upper and lower layer was 1.5 to 1.9 kg/m3. Figure 1 is typical of these profiles and the data assisted in fixing the depth of the offshore terminal.
Mathematical Models. Empirical models for the temperature dependent thermal conductivity of suitable types of thermal insulation will be developed as required for simulating the thermal conductiv- ity of encapsulations used for enhancing the temperature range of ▇▇▇ sensors. The material flow model in WP5 could also extended to include information from this process in order to improve the customer order to delivery chain.
Mathematical Models. The Contractor shall deliver, prior to the Alphasat CDR, the following engineering models, with associated support documentation: • Satellite Structural Mathematical Model (from equipment to system level) • Satellite Thermal Mathematical Model (from equipment to system level)
Mathematical Models. Sub-contractor acknowledges that the mathematical models, data files, design files and computer programmers, specified in Appendix 2 shall be delivered to the r specified in Article 5, ▇▇▇▇▇▇ 5, Sub-Clause 5.1 a) of the Contract, not later than:
Mathematical Models. A solid understanding of the underlying mathematical model is necessary to guide the development of solution methods. In this dissertation three mathematical frameworks are considered.