Abstract:We propose a new approach to MPLS that uses the standard MPLS data plane and an OpenFlow based simpler and extensible control plane. We demonstrate this approach using a prototype system for MPLS Traffic Engineering.

We demonstrate MPLS Traffic Engineering (MPLS-TE) and MPLS-based Virtual Private Networks (MPLS VPNs) using OpenFlow [1] and NOX [6]. The demonstration is the outcome of an engineering experiment to answer the following questions: How hard is it to implement a complex control plane on top of a network controller such as NOX? Does the global vantage point in NOX make the implementation easier than the traditional method of implementing it on every switch, embedded in the data plane?We implemented every major feature of MPLS-TE and MPLS-VPN in just 2,000 lines of code, compared to much larger lines of code in the more traditional approach, such as Quagga-MPLS. Because NOX maintains a consistent, up-to-date topology map, the MPLS control plane features are quite simple to implement. And its simplicity makes it easy to extend: We have easily added several new features; something a network operator could do to customize their network to meet their customers' needs.The demo consists of two parts: MPLS-TE services and then MPLS VPN driven by a GUI. Categories and Subject SCIENTIFIC RATIONALEWe claim that while the MPLS data plane is fairly simple, the control planes associated with MPLS-TE and MPLS VPNs are rather complicated. For instance, in a typical traffic engineered MPLS network, one needs to run OSPF, LDP, RSVP-TE, I-BGP, and MP-BGP to name a few protocols. The distributed nature of these protocols results in excessive traffic of update messages when there are frequent changes in the network. This, in turn causes the routers to spend a lot of CPU time recalculating routing information. Hence, CPU message queues may get filled leading to incoming hello messages getting dropped. This leads to false link-state information being distributed throughout the network. The described vicious cycle causes large convergence times for the above protocols, meaning excessive control traffic on the network and stale information on the routers.In SDN, the Network Operating System (NOS) is responsible for constructing and presenting a logically centralized map of the network. Instead of a set of distributed protocols implemented on each router, we implement these functionalities as simple software modules that work on the network map in NOS. Implementation of these functions on a logical map of the network is very simple. Hence, by pushing the control plane functionality to NOS, we benefit from not only simplicity of implementation, but also the fact that maintaining and updating applications are easy as well. This is because new features are no longer tied to multiple protocols that would normally have to be changed. In fact, with the controller in charge of the control plane, there is no need for any distributed protocol running in the routers as the NOS has complete knowledge of the network. ARCHITECTUREThe architecture of our system is given in Figure 1. Our test-bed consists of several software and physical switches. The software switches are instances of Open vSwitch [2] which are hosted within the Mininet environm...

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An estimation of change-proneness of parts of a software system is an active topic in the area of software engineering. Such estimates can be used to predict changes to different classes of a system from one release to the next. They can also be used to estimate and possibly reduce the effort required during the development and maintenance phase by balancing the amount of developers’ time assigned to each part of a software system. This research work proposes a novel approach to predict changes in an object-oriented software system. The rationale behind this approach is that in a well-designed software system, feature enhancement or corrective maintenance should affect a limited amount of existing code. Our goal is to quantify this aspect of quality by assessing the probability that each class will change in a future generation. Our proposed probabilistic approach uses the dependencies obtained from the UML diagrams, as well as other code metrics extracted from source code of several releases of a software system using reverse engineering techniques. These measures, combined with the change log of the software system and the expected time of next release, are used in an automated manner to predict whether a class will change in the next release of the software system. The proposed systematic approach has been evaluated on a multiversion medium sized open source project namely JFlex, the Fast Scanner Generator for Java. The obtained results indicate the simplicity and accuracy of our approach in the comparison with existing methods referred in the literature.

Emergency Department (ED) crowding is a major public health challenge since it can seriously impact patient outcomes; and accurate prediction of patient flow in EDs is essential for improving operational efficiency and quality of care. We present a deep learning framework to predict patient flow rates in EDs, namely the rates of arrival, treatment, and discharge for patients across all triage levels. Our model detects short-term and long-term temporal dependencies within the time-series data of a given patientflow variable, as well as dependencies between time-series data of different patient-flow variables. We implement this framework as a convolutional neural network, which we call PatientFlowNet. Our proposed model learns simultaneously from multiple flow variables over a long temporal window and predicts future values of arrival, treatment, and discharge rates in the ED. We benchmark our model against state-of-the-art methods on data from EDs in three different hospitals. Results show that PatientFlowNet achieves superior prediction accuracy, compared to the baseline methods, and yields a mean absolute error that is 4.8% lower than the leading baseline. Furthermore, we provide a visual and interpretable representation of the learned dependencies by our model, between patient-flow variables in EDs. INDEX TERMS Health information management, machine learning, neural networks, public healthcare, supervised learning.