No abstract

In response to the need for high bandwidth and power efficient data center interconnection networks, different interconnects have been proposed based on the optical technology used: microelectromechanical system (MEMS), optical cross connects (OXCs), arrayed waveguide grating routers (AWGRs) and semiconductor optical amplifier (SOAs). MEMS switches are based on mature technology, have low insertion loss and cross-talk, and are data rate independent. They are also the most scalable and the cheapest class of optical switches. However, the reconfiguration time of these switches is of the order of tens of milliseconds while fast optical switches have switching time in the range of a few nanoseconds. Fast optical switches can be based on AWGRs in conjunction with tunable wavelength converters or tunable lasers or they are based on SOAs in broadcast-and-select architecture. In this paper, we propose an optical interconnect architecture for the large scale data centers. The proposed interconnect: Hybrid Optical Switch Architecture (HOSA) is a hybrid design that features slow and fast optical switches. The hybrid design leverages strengths of both types of optical switches. To reduce complexity, we employ a single stage core topology that can be easily scaled up (in capacity) and scaled out (in the number of racks) without requiring major re-cabling and network reconfiguration. We investigate the scalability of the HOSA and show that by using a single stage core topology, it can be scaled to a hundreds of thousands of servers. We also investigate a trade-off between cost and power consumption of our design by comparing it with other well-known interconnects by using analytical modelling. We demonstrate power efficiency as compared to other conventional interconnects on account of upfront CAPEX but the additional CAPEX incurred in deploying our solution instead of traditional architecture is mitigated to some extent by reduced OPEX, due to its greater energy efficiency. We evaluate the performance of the system using network-level simulation by considering diverse workload communication patterns and system design parameters. Our results show low latency and high throughput with different workload communication patterns. 1.1.2. Scalability Large cloud computing data centers owned by Amazon, Microsoft and Google have tens of thousands of servers. With the expected growth in data center traffic, the number of servers in data centers is destined to increase which poses a significant challenge to the data center interconnection network. 1.1.3. Traffic locality The projection of traffic growth in data centers according to the Cisco cloud index [3] is shown in Fig. 2. Observe that during the period from 2013 to 2018, the majority of data center traffic will remain within the data center while only a small portion of the traffic will go to the external network. Some of the traffic will also be exchanged between data centers for distributed and replicated services between databases in different data centers. Due to this high traff...

High intensity particle beams from accelerators induce high energy density states in bulk matter. Due to the specific nature of the ion-matter interaction a volume of matter is heated uniformly with low gradients of temperature and pressure in the initial phase, depending on the pulse structure of the beam with respect to space and time. We present an overview on recent results and developments of beam plasma, and beam matter interaction experiments with heavy ion and laser beams.

No abstract

To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).