Why do we need to Introduce Temporal Behavior in Both Modern Science and Modern Computing, with an Outlook to Researching Modern Effects/Materials and Technologies

Classic science seemed to be completed more than a century ago, facing only a few (but growing number of!) unexplained issues. Introducing time-dependence into classic science explained those issues, and its consequent use led to the birth of a series of modern sciences, including relativistic and quantum physics. Classic computing is based on the paradigm proposed by von Neumann for vacuum tubes only, which seems to be completed in the same sense. Von Neumann warned, however, that implementing computers under more advanced technological conditions, using the paradigm without considering the transfer time (and especially attempting to imitate neural operation), would be unsound. However, classic computing science persists in neglecting the transfer time and is facing a few (but growing number of!) unexplained issues, and its development stalled in most of its fields. Introducing time-dependence into the classic computing science explains those issues and discovers the reasons for its experienced stalling. It can lead to a revolution in computing, resulting in a modern computing science, in the same way, as it resulted in modern science's birth

A Novel Cost Based Model for Energy Consumption in Cloud Computing

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment

A Novel Cost Based Model for Energy Consumption in Cloud Computing

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment

Hardware Implementation of Real-Time Operating System’s Thread Context Switch

Increasingly, embedded real-time applications use multi-threading. The benefits of multi-threading include greater throughput, improved responsiveness, and ease of development and maintenance. However, there are costs and pitfalls associated with multi-threading. In some of hard real-time applications, with very precise timing requirements, multi-threading itself becomes an overhead cost mainly due to scheduling and contextswitching components of the real-time operating system (RTOS). Different scheduling algorithms have been suggested to improve the overall system performance. However, context-switching still consumes much of the processor’s time and becomes a major overhead cost especially for hard real-time embedded systems. A typical RTOS context switch consumes 50 to 80 processor clock cycles (depending on processor architecture and context size) to store and restore the thread context. If a real-time application needs to respond to an event repeatedly less than this time, then the overall system performance may not be acceptable. The suggested approach in this thesis improves the context-switching time drastically. This technique has been implemented in hardware, as part of the processor state along with new central processing unit (CPU) instructions to take care of the context-switching process without interacting with external memory. With the suggested approach, the thread contextswitch can be achieved in 4 CPU clock cycles independent of context size. This is a significant improvement to thread context switching