Evolutionary Algorithm Optimization of Edge Delivery Sites in Next Generation Multi-Service Content Distribution Networks

Abstract. In the past decade or so we have been experiencing an extraordinary explosion of data volumes first in wireline networks and recently even in mobile wireless networks. Optimizing bandwidth utilization is critical for planning and deploying efficient networks that are capable of delivering new services like IPTV over cost-oriented implementations. Models of distributed content caching in the access network have been employed -for example -as analytical optimization tools in order to tackle associated problems. A modified capacitated quality-of-service network (QoS) model is proposed herein in order to optimize the placement of the sites of surrogate media servers (central offices-COs) on the access part of a content distribution network (CDN). The novelty of the proposed approach lies in the fact that capacitated quality-ofservice network optimization is cast as an optimization problem over two rather than one optimization variables-objectives. Implementation cost and link delay as determined by capacity/utilization requirements are the optimization functionals-objectives. Optimization of the network architecture is carried out via a multiobjective evolutionary algorithm that encodes all possible edges between the first level aggregation points of the access network. Proper priorities are assigned to different types of traffic according to class of service. Two main services are considered, namely live broadcast/IPTV and video on demand services (VoD). The media servers/COs are incorporated into the infrastructure of the access nodes in a step-by-step fashion modifying the traffic requirements between source and sink nodes of the optimal configurations of the access network. The evolution of the Pareto front is investigated in each case

Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

We have witnessed an increase in the availability of data from diverse sources over the past few years. Cloud computing, big data and Internet-of-Things (IoT) are distinctive cases of such an increase which demand novel approaches for data analytics in order to process and analyze huge volumes of data for security and business use. Cloud computing has been becoming popular for critical structure IT mainly due to cost savings and dynamic scalability. Current offerings, however, are not mature enough with respect to stringent security and resilience requirements. Mechanisms such as anomaly detection hybrid systems are required in order to protect against various challenges that include network based attacks, performance issues and operational anomalies. Such hybrid AI systems include Neural Networks, blackboard systems, belief (Bayesian) networks, case-based reasoning and rule-based systems and can be implemented in a variety of ways. Traffic in the cloud comes from multiple heterogeneous domains and changes rapidly due to the variety of operational characteristics of the tenants using the cloud and the elasticity of the provided services. The underlying detection mechanisms rely upon measurements drawn from multiple sources. However, the characteristics of the distribution of measurements within specific subspaces might be unknown. We argue in this paper that there is a need to cluster the observed data during normal network operation into multiple subspaces each one of them featuring specific local attributes, i.e. granules of information. Clustering is implemented by the inference engine of a model hybrid NN system. Several variations of the so-called value-difference metric (VDM) are investigated like local histograms and the Canberra distance for scalar attributes, the Jaccard distance for binary word attributes, rough sets as well as local histograms over an aggregate ordering distance and the Canberra measure for vectorial attributes. Low-dimensional subspace representations of each group of points (measurements) in the context of anomaly detection in critical cloud implementations is based upon VD metrics and can be either parametric or non-parametric. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate ordered sets of objects featuring VD metrics (as obtained from distributed network measurements) is proposed. Each node of the SOFM stands for a structured local distribution of such objects within the input space. The so-called Neighborhood-based Outlier Factor (NOOF) is defined for such reduced/aggregate ordered sets of objects as a value-difference metric of histogrammes. Measurements that do not belong to local distributions are detected as anomalies, i.e. outliers of the trained SOFM. Several methods of subspace clustering using Expectation-Maximization Gaussian Mixture Models (a parametric approach) as well as local data densities (a non-parametric approach) are outlined and compared against the proposed method using data that are obtained from our cloud testbed in emulated anomalous traffic conditions. The results—which are obtained from a model NN system—indicate that the proposed method performs well in comparison with conventional techniques

Fluid dynamics in a two dimensional fluidized bed of large particles

Observation shows that bubbles in large particle fluidized beds behave quite differently from beds of fine particles. They expand and can grow faster than they rise. Bubbles in fine particle beds seem to grow primarily by coalescence, while in large particle beds they are observed to grow mainly without coalescence. The aim of this study was to gain a better insight into bubble behavior in fluidized beds of intermediate and large particles (particle diameter greater than 0.4 mm), and understand the cause of the observed bubble growth. In the theoretical part of this work it is shown that emulsion voidage invariability, incompressible viscous gas flow and potential solids flow assumed by existing theoretical two-phase models cannot account for the observed gas accumulation within bubbles. A simplified theoretical form for the gas accumulation rate in a cylindrical bubble is developed. It is based on different areas of gas inflow and outflow at the bubble boundary, and on the fact that the emulsion voidages above and below the bubble are different. It was shown that voidage variations in the emulsion phase around a bubble are important to bubble growth considerations. The experimental cases analyzed for single growing two dimensional bubbles indicate a higher voidage at the wake region, than the voidage at the nose of the bubble. It was found that a relatively small difference in emulsion voidage above and below the bubble can cause significant gas accumulation within the bubble reaching values comparable to the gas throughflow. Finally the movement of bubbles in the vicinity of an immersed tube and pressure-time curves representing the slow bubble and rapid bubble growth regime, and the transition range are analyzed in order to identify important bubble behavior characteristics. An experimental set up was developed making possible simultaneous permanent recording of the two-dimensional bubble behavior and instantaneous pressure data acquisition at multiple locations in an area under study. Lapsed cinematography and a special circuit activating the data acquisition system was used. The experimental set up enabled the collection of a large amount of data which gave valuable information about fluid dynamic behavior in the vicinity of an immersed tube. Experiments were performed at atmospheric pressure and room temperature in a two-dimensional fluidized bed with an immersed 50.8.mm (2 in) in diameter internal located at a height 0.35 m above the distributor plate. Glass, sand and dolomite particles with mean surface particle diameters of 0.65 to 2.6 mm were used. Superficial air velocities used spanned a region from well below minimum fluidization conditions to 2.82 m/s

Evaluation of Anomaly Detection Techniques for SCADA Communication Resilience

Attacks on critical infrastructures’ Supervisory Control and Data Acquisition (SCADA) systems are beginning to increase. They are often initiated by highly skilled attackers, who are capable of deploying sophisticated attacks to exfiltrate data or even to cause physical damage. In this paper, we rehearse the rationale for protecting against cyber attacks and evaluate a set of Anomaly Detection (AD) techniques in detecting attacks by analysing traffic captured in a SCADA network. For this purpose, we have implemented a tool chain with a reference implementation of various state-of-the-art AD techniques to detect attacks, which manifest themselves as anomalies. Specifically, in order to evaluate the AD techniques, we apply our tool chain on a dataset created from a gas pipeline SCADA system in Mississippi State University’s lab, which include artefacts of both normal operations and cyber attack scenarios. Our evaluation elaborate on several performance metrics of the examined AD techniques such as precision; recall; accuracy; F-score and G-score. The results indicate that detection rate may change significantly when considering various attack types and different detections modes (i.e., supervised and unsupervised), and also provide indications that there is a need for a robust, and preferably real-time AD technique to introduce resilience in critical infrastructures

Excavations at Azoria, 2003-2004, Part 1: The Archaic Civic Complex

This article constitutes the first of two reports on fieldwork conducted at Azoria in eastern Crete during the 2003 and 2004 excavation seasons. The focus of excavation was on the South Acropolis, where buildings of Archaic date (7th-early 5th century b.c.) suggesting public or civic functions have come to light. The complex includes a possible andreion on the west slope, a cult building on the terrace south of the peak, and storerooms and kitchens associated with a monumental public building on the southwest terrace. A 3rd-century b.c. dump on the southeast slope provides important information about the limited reoccupation of the site in the Hellenistic period

Multimedia Content Distribution over Next-Generation Heterogeneous Networks Featuring a Service Architecture of Sliced Resources

Part 5: First Intelligent Innovative Ways for Video-to-Video Communication in Modern Smart Cities Workshop (IIVC 2012)International audienceRecent advancements of IP networks pave the way for Over-the-Top (OTT) applications. Evolved telecom platforms provide revenue potentials via Service Gateways (APIs) on top of VoIP/RCS (IMS), Machine Type Communication (MTC) and Smart Bit pipe approaches. QoS is achieved through over-provisioning in today’s access and core networks since there are no flexible mechanisms that are available for end-users to influence the QoS level. Processes for user-demanded and operator-controlled QoS management as well as mechanisms for applications signaling their requirements on the data path into the network are far from being adequate. Novel approaches regarding end-to-end inter-domain flow-control architectures, i.e. network slicing, as well as machine-to-machine (M2M) virtualization platforms that handle such functions as device/communication management, session management and bearer and charging management are emerging promising enhanced multimedia communications and efficient utilization of network resources. They promote cloud services and they integrate the computer word into next generation telecommunications