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Selection strategy is an essential part of an adaptive routing algorithm that influences the performance of the networks-on-chip (NoC). A selection strategy is used for selecting the best output channel from the available channels according to the network status. This study presents a new output selection strategy called destination intensity and congestion aware (DICA) that uses both local and regional congestion information from adjacent and two hops away neighbours on the path to destination based on the channel and switch information. Also, the proposed output selection strategy uses a new global congestion-aware scheme based on destination node called destination congestion awareness method to distribute traffic more equally over the network. The simulation results show that DICA strategy consistently improves the performance in both throughput and average latency with minimal overhead in terms of area consumption for various synthetic and real application traffic patterns. In addition, the microarchitecture of NoC routers is also presented in this study and it shows that the proposed output selection strategy can be combined with any adaptive routing algorithms. The experimental results show the average delay improvements of DICA to the Bufferlevel, neighbours-on-path, and regional congestion awareness are 87, 57, and 24%, respectively.

Cloud computing is a new technology and Due to its economical and operational benefits it is the most popular concept in IT today. Clouds consist of a collection of virtualized resources that can be provisioned on demand, depending on the clients' needs. In this technology almost everything like hardware, software and platform are provided as a service. In cloud computing one Host is a component that represents a physical computing and a Datacenter is composed by a set of hosts, which are responsible for managing Virtual Machines during their life cycles. An important issue in cloud computing is the scheduling of virtual machines requests, so that the requested tasks can be completed in a minimum time according to the user defined time. This paper presents and evaluate a new scheduling algorithm that is an efficient technique for scheduling virtual machines between datacenters. The simulation results show the effectiveness of our algorithm by comparing it with the two scheduling techniques First Fit (FCFS) and Round Robin (RR). The results depict that the proposed scheduling algorithm can be effectively deployed on Clouds.