Scalability

This paper presents the design philosophy and initial design decisions of Herald: a highly scalable global event notification system that is being designed and built at Microsoft Research. Herald is a distributed system designed to transparently scale in all respects, including numbers of subscribers and publishers, numbers of event subscription points, and event delivery rates. Event delivery can occur within a single machine, within a local network or Intranet, and throughout the Internet. Herald tries to take into account the lessons learned from the successes of both the Internet and the Web. Most notably, Herald is being designed, like the Internet, to operate correctly in the presence of numerous broken and disconnected components. The Herald service will be constructed as a set of protocols governing a federation of machines within cooperating but mutually suspicious domains of trust. Like the Web, Herald will try to avoid, to the extent possible, the maintenance of globally consistent state and will make failures part of the clientvisible interface.

The problem of mobile robot navigation has received a noticeable attention over last few years. Several different approaches were presented, each having major limitations. In this paper a new, agentbased solution the problem of mobile robots navigation is proposed. It is based on a novel representation of the environment, that divides it into a number of distinct regions, and assigns autonomous software Space Agents to supervise it. Space Agents create a graph, that represents a high-level structure of the entire environment. The graph is used as a virtual space, that robot controlling agents work in. The most important features of the approach are: path planning for multiple robots based on most recent data available in the system, automated collision avoidance, simple localization of a "lost robot" and unrestricted scalability.

In this paper, a novel approach for building synopses is proposed by using a service and message-oriented architecture. The Sain-tEtiQ summarization system initially designed for very large stored databases, by its intrinsic features, is capable of dealing with the requirements inherent to the data stream environment. Its incremental maintenance of the output summaries and its scalability allows it to be a serious challenger to existing techniques. The resulting summaries present on the one hand the incoming data in a less precise form but is still on the other hand very informative on the actual content. We expose a novel way of exploiting this semantically rich information for query answering with an approach midway between blunt query answering and midway between data mining.

Topic detection with large and noisy data collections such as social media must address both scalability and accuracy challenges. KeyGraph is an efficient method that improves on current solutions by considering keyword cooccurrence. We show that KeyGraph has similar accuracy when compared to state-of-the-art approaches on small, well-annotated collections, and it can successfully filter irrelevant documents and identify events in large and noisy social media collections. An extensive evaluation using Amazon’s Mechanical Turk demonstrated the increased accuracy and high precision of KeyGraph, as well as superior runtime performance compared to other solutions.

The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

Overlay management systems face the challenges of increased complexity and heterogeneity because of the many elements involved in providing overlay services. In dynamic networks, the challenge and complexity is increased. Service composition allows simple services to be dynamically combined into more complex services in order to provide new services. In this paper, we deal with the problem of composing multiple autonomic elements to achieve system wide goals. Using a selforganizing approach, autonomic entities are dynamically and seamlessly composed into service-specific overlay networks. The paper describes and discusses the details of the composition approach. Results of extensive simulation are presented.

We present a study of multithreaded implementations of Thorup's algorithm for solving the Single Source Shortest Path (SSSP) problem for undirected graphs. Our implementations leverage the fledgling MultiThreaded Graph Library (MTGL) to perform operations such as finding connected components and extracting induced subgraphs. To achieve good parallel performance from this algorithm, we deviate from several theoretically optimal algorithmic steps. In this paper, we present simplifications that perform better in practice, and we describe details of the multithreaded implementation that were necessary for scalability. We study synthetic graphs that model unstructured networks, such as social networks and economic transaction networks. Most of the recent progress in shortest path algorithms relies on structure that these networks do not have. In this work, we take a step back and explore the synergy between an elegant theoretical algorithm and an elegant computer architecture. Finally, we conclude with a prediction that this work will become relevant to shortest path computation on structured networks.

The implementation and performance of the multidimensional Fast Fourier Transform (FFT) on a distributed memory Beowulf cluster is examined. We focus on the three-dimensional (3D) real transform, an essential computational component of Galerkin and pseudospectral codes. The approach studied is a 1D domain decomposition algorithm that relies on communication-intensive transpose operation involving P processors. Communication is based upon the standard portable message passing interface (MPI). We show that 1=P scaling for execution time at ®xed problem size N 3 (i.e., linear speedup) can be obtained provided that (1) the transpose algorithm is optimized for simultaneous block communication by all processors; and (2) communication is arranged for non-overlapping pairwise communication between processors, thus eliminating blocking when standard fast ethernet interconnects are employed. This method provides the basis for implementation of scalable and ecient spectral method computations of hydrodynamic and magneto-hydrodynamic turbulence on Beowulf clusters assembled from standard commodity components. An example is presented using a 3D passive scalar code.

The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field, code mobility is generating a growing body of scientific literature and industrial developments. Nevertheless, the field is still characterized by the lack of a sound and comprehensive body of concepts and terms. As a consequence, it is rather difficult to understand, assess, and compare the existing approaches. In turn, this limits our ability to fully exploit them in practice, and to further promote the research work on mobile code. Indeed, a significant symptom of this situation is the lack of a commonly accepted and sound definition of the term "mobile code" itself.

The authors present a predictive model for the negative bias temperature instability (NBTI) of PMOS under both short term and long term operation. On the basis of the reaction-diffusion mechanism, this model accurately captures the dependence of NBTI on the oxide thickness (t ox), the diffusing species (H or H 2) and other key transistor and design parameters. In addition, a closed form expression for the threshold voltage change (DV th) under multiple cycle dynamic operation is derived. Model accuracy and efficiency were verified with 180, 130 and 90 nm silicon data. The impact of NBTI on the delay degradation of a ring oscillator and the various metrics of the SRAM such as its data retention voltage, read and hold margins, as well as read and write delay, is also investigated.

We present two designs (I and II) for IEEE 754 double precision floating point matrix multiplication, optimized for implementation on high-end FPGAs. It forms the kernel in many important tile-based BLAS algorithms, making an excellent candidate for acceleration. The designs, both based on the rank-1 update scheme, can handle arbitrary matrix sizes, and are able to sustain their peak performance

Over the last 21 years, we’ve spent almost a person-millenium producing Cyc, an axiomatization of general human knowledge. Though still far from complete, Cyc contains over three million axioms. The need to express the range of things a person knows has led us to ever more expressive representation languages – currently we use an nth order predicate calculus with an overlay of contexts which are themselves first class objects. These pressures and others (e.g., elaboration tolerance) have driven us against numerous sorts of ”scaling up” problems. In this talk I will briefly describe Cyc, the processes by which new axioms are added and deleted, applications of it, etc., but I will focus on some of these scaling up issues and approaches we have taken – and plan to take – to keep inference fast enough and to keep contradictions from being more than a nuisance.

Sensor networks for environmental data acquisition will play an important role in the development of future large data acquisition systems, particularly in oceanographic observation and operational oceanography. Within these scenarios, many challenges will be overcome by using emergent networking technologies which have been successfully tested in other fields where distributed systems are used.

A new image compression algorithm is proposed, based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich set of features, including resolution and SNR scalability together with a "random access" property. The algorithm has modest complexity and is suitable for applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.

A number of parallel formulations of dense matrix multiplication algorithm have been developed. For arbitrarily large number of processors, any of these algorithms or their variants can provide near linear speedup for sufficiently large matrix sizes and none of the algorithms can be clearly claimed to be superior than the others. In this paper we analyze the performance and scalability of a number of parallel formulations of the matrix multiplication algorithm and predict the conditions under which each formulation is better than the others.

Interconnects based on Networks-on-Chip are an appealing solution to address future microprocessor designs where, very likely, hundreds of cores will be connected on a single chip. A fundamental role in highly parallelized applications running on many-core architectures will be played by barrier primitives used to synchronize the execution of parallel processes. This paper focuses on the analysis of the efficiency and scalability of different barrier implementations in manycore architectures based on NoCs. Several message passing barrier implementations based on four algorithms (all-to-all, master-slave, butterfly and tree) have been implemented and evaluated for a single-chip target architecture composed of a variable number of cores (from 4 to 128) and different network topologies (mesh, torus, ring, clustered-ring and fattree). Using a cycle-accurate simulator, we show the scalability of each barrier for every NoC topology, analyzing and comparing theoretical with real behaviors. We observed that some barrier algorithms, when implemented in hardware or software, show a different scaling behavior with respect to those theoretically expected. We evaluate the efficiency of each combination topology-barrier, demonstrating that, in many cases, simple network topologies can be more efficient than complex and highly connected topologies.

Cloud computing is an emerging technology that is still unclear to many security problems and user authentication, access control, and ensuring the security of stored data in cloud servers are the most challenging issues in cloud-based environment. Accordingly, this paper offers an efficient and scalable user authentication scheme for cloud computing environment. It the suggested model, various tools and techniques have been introduced and used by using the concept of agent. Therefore, a client-based user authentication agent has been introduced to confirm identity of the user in client-side. Furthermore, a cloud-based software-as-a-service application has been used to confirm the process of authentication for unregistered devices. Moreover, there are two separate servers for storing authentication and cryptography resources from main servers to decrease the dependency of user authentication and encryption processes from main server. Cryptography agent was also introduced to encrypt resources before storing on cloud servers. In overall, the theoretical analysis of the suggested scheme shows that, designing this user authentication and access control model will enhance the reliability and rate of trust in cloud computing environments as an emerging and powerful technology in various industries.

Abstract: ASP .NET web applications typically employ server controls to provide dynamic web pages, and data-bound server controls to display and maintain database data. Most developers use default properties of ASP .NET server controls when developing web applications, which allows for rapid development of workable applications. However, creating a high-performance, multi-user, and scalable web application requires enhancement of server controls using custom-made code. In this empirical study we ...

Re-occurrence of the same problem is very common in many large software products. By matching the symptoms of a new problem to those in a database of known problems, automated diagnosis and even selfhealing for re-occurrences can be (partially) realized. This paper exploits function call stacks as highly structured symptoms of a certain class of problems, including crashes, hangs, and traps. We propose and evaluate algorithms for efficiently and accurately matching call stacks by a weighted metric of the similarity of their function names, after first removing redundant recursion and uninformative (poor discriminator) functions from those stacks. We also describe a new indexing scheme to speed queries to the repository of known problems, without compromising the quality of matches returned. Experiments conducted using call stacks from actual product problem reports demonstrate the improved accuracy (both precision and recall) resulting from our new stack-matching algorithms and removal of uninformative or redundant function names, as well as the performance and scalability improvements realized by indexing call stacks. We also discuss how call-stack matching can be used in both self-managing (or autonomic systems) and human "help desk" applications.

A crucial challenge for scientific workflow management systems is to support the efficient and scalable storage and querying of large provenance datasets that record the history of in silico experiments. As new provenance management systems are being developed, it is important to have benchmarks that can evaluate these systems and provide an unbiased comparison. In this paper, based on the requirements for scientific workflow provenance systems, we design an extensible benchmark that features a collection of techniques and tools for workload generation, query selection, performance measurement, and experimental result interpretation.

Safety critical software requires integrating verification techniques in software development methods. Software architectures must guarantee that developed systems will meet safety requirements and safety analyses are frequently used in the assessment. Safety engineers and software architects must reach a common understanding on an optimal architecture from both perspectives. Currently both groups of engineers apply different modelling techniques and languages: safety analysis models and software modelling languages. The solutions proposed seek to integrate both domains coupling the languages of each domain. It constitutes a sound example of the use of language engineering to improve efficiency in a software-related domain. A model-driven development approach and the use of a platform-independent language are used to bridge the gap between safety analyses (failure mode effects and criticality analysis and fault tree analysis) and software development languages (e.g. unified modelling language). Language abstract syntaxes (metamodels), profiles, language mappings (model transformations) and language refinements, support the direct application of safety analysis to software architectures for the verification of safety requirements. Model consistency and the possibility of automation are found among the benefits.

For distributed applications requiring collaboration, responsive and transparent interactivity is highly desired. Though such interactivity can be achieved with optimistic replication, maintaining replica consistency is difficult. To support efficient implementations of collaborative applications, this paper extends a few representative abstract data types (ADTs), such as arrays, hash tables, and growable arrays (or linked lists), into replicated abstract data types (RADTs). In RADTs, a shared ADT is replicated and modified with optimistic operations. Operation commutativity and precedence transitivity are two principles enabling RADTs to maintain consistency despite different execution orders. Especially, replicated growable arrays (RGAs) support insertion/deletion/update operations. Over previous approaches to the optimistic insertion and deletion, RGAs show significant improvement in performance, scalability, and reliability.

The Joint Photographic Experts Group (JPEG) committee is a joint working group of the International Standardization Organization (ISO) and the International Electrotechnical Commission (IEC). The word "Joint" in JPEG however does not refer to the joint efforts of ISO and IEC, but to the fact that the JPEG activities are the result of an additional collaboration with the International Telecommunication Union (ITU). Inspired by technology and market evolutions, i.e. the advent of wavelet technology and need for additional functionality such as scalability, the JPEG committee launched in 1997 a new standardization process that would result in 2000 in a new standard: JPEG 2000. JPEG 2000 is a collection of standard parts, which together shape the complete toolset. Currently, the JPEG 2000 standard is composed out of 13 parts. In this paper, we review these parts and additionally address recent standardization initiatives within the JPEG committee such as JPSearch, JPEG-XR and AIC.

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area.

First version of the latest video coding standard, High Efficiency Video Coding (HEVC), only supports coding of video in YUV 4:2:0 chroma format. An extension of the standard that will support other chroma formats is currently under development, however, version 1 decoders will not be able to handle the bitstreams created using this extension. In this paper, we propose a novel method to create scalable bitstreams that involve a backward compatible base layer in 4:2:0 format that can be handled by HEVC version 1 decoders and code additional layers to enhance the chroma resolution. The proposal codes 4:2:0 video in the base layer and the high resolution chroma components as auxiliary pictures as separate enhancement layers. The high resolution chroma components could optionally be predicted from the upsampled 4:2:0 chroma components of base layer. The simulations show that the proposed method achieves scalability with 9.5% coding efficiency penalty on average compared to single layer coding of 4:4:4 video. When compared to simulcast of 4:2:0 and 4:4:4 video, proposed method provides 38% gain on average. Proposed method makes services using high chroma fidelity easier to be deployed, due to the backwards compatibility to existing HEVC implementations with high coding efficiency.

Wireless broadcast transmission enables Inter-vehicle or Vehicle-to-Vehicle (V2V) communication among nearby vehicles. This communication supports latency-critical applications for improved safety and maybe optimized traffic. However, V2V communication is vulnerable to cyber attacks involving message manipulation. Mechanisms are required to ensure both authenticity and integrity of broadcast data, while maintaining drivers privacy against surveillance. Considering the limited computational resources of vehicles and the possibility of high traffic density scenarios, authentication processes should have low computational overhead. Prior research has produced multiple authentication protocol proposals based on digital signatures, hash functions, or Message Authentication Codes (MACs). To date, there is no computationally efficient secure broadcast authentication scheme tolerable by the vehicles resource-constrained On-Board Units (OBUs) for latency-critical applications in heavy traffic conditions. This paper provides a new secure, efficient, and privacy-preserving scheme proposing Anonymous Lightweight Inter-vehicle (ALI) broadcast authentication with encryption. ALI provides a high level of anonymity by combining a message authentication scheme with beacon encryption. The cryptographic overhead for V2V communication in the ALI scheme is only 149 bytes, and can handle authentication of approximately 700 broadcast messages every 100 milliseconds (ms) on a 2.10 Gigahertz (GHz) Intel Core 2 Duo Processor. This demonstrates the suitability of the ALI scheme in heavy traffic scenarios. We show the security and efficiency of our proposal by conducting both a formal security proof and extensive performance analysis.

We propose a framework for a secure RSU-to-OBU message broadcasting in VANETs using proxy signatures. We consider a VANET infrastructure for which the network assumptions are similar to the current standards of VANET communications. The main purpose of our scheme is to provide message integrity, authenticate the broadcast messages, and authenticate the RSU to the OBUs. We set the appropriate liabilities to the network components enabling the OBU to verify the received message for its validity and integrity. Our contribution includes modification of the proxy signature scheme in order to fulfill the security requirements of a VANET's message broadcast. The security analysis of our scheme strongly supports the applicability of our proposed framework.

Different trust models have been developed for dealing with possible dishonest behavior and attacks from malicious peer Intrusion Detection Systems (IDSs) in a collaborative Intrusion Detection Network (IDN). For evaluating and comparing these models, this paper introduces a simulation framework that incorporates different components namely expertise model, deception model, attack model, and evaluation metrics. The proposed framework offers flexibility for users to adjust the simulation parameters according to their needs. We then compare three existing trust models in this domain to demonstrate the effectiveness of our framework when used in analyzing their efficiency, robustness and scalability.

Simultaneous multithreading (SMT) increases CPU utilization and application performance in many circumstances, but it can be detrimental when performance is limited by application scalability or when there is significant contention for CPU resources. This paper describes an SMT-selection metric that predicts the change in application performance when the SMT level and number of application threads are varied. This metric is obtained online through hardware performance counters with little overhead, and allows the application or operating system to dynamically choose the best SMT level. We have validated the SMT-selection metric using a variety of benchmarks that capture various application characteristics on two different processor architectures. Our results show that the SMT-selection metric is capable of predicting the best SMT level for a given workload in 90% of the cases. The paper also shows that such a metric can be used with a scheduler or application optimizer to help guide its optimization decisions.

S e p t e m b e r / O c t o b e r 2 0 0 3 I E E E S O F T W A R E 1 0 3 BOOKSHELF cal inference principles, and various optimizing techniques from operations research. Otherwise, Quality Software Project Management is an excellent reference for both new and seasoned software program managers. Robert C. Larrabee works for ARINC Engineering LLC. Contact him at [email protected].

Data mining is an active field of research and development aiming to automatically extract "knowledge" from analyzing data sets. Knowledge can be defined in different ways such as discovering (structured, frequent, approximate, etc.) patterns in data, grouping/clustering/bi-clustering data according to one or more criteria, finding association rules, etc. Such knowledge is then fed-back to decision support systems enabling end-users (actors) to make more informed decisions, which in economic terms could lead to advantages as compared to traditional decision support systems. It should be noted however, that data mining algorithms and frameworks have been proposed prior to the "Big Data" explosion. While data mining algorithms have considered efficiency and computational complexity as an important requirement, they did not take into account features of Big Data such as very large size, velocity with which data is generated, variety, etc. On the other hand, these features are indeed posing issues and challenges to data mining algorithms and frameworks. In this paper we analyse some of the issues in mining large data sets such as scalability and in-memory needs. We also show some computational results pointing out to such issues.

In petabyte-scale distributed file systems that decouple read and write from metadata operations, behavior of the metadata server cluster will be critical to overall system performance and scalability. We present a dynamic subtree partitioning and adaptive metadata management system designed to efficiently manage hierarchical metadata workloads that evolve over time. We examine the relative merits of our approach in the context of traditional workload partitioning strategies, and demonstrate the performance, scalability and adaptability advantages in a simulation environment.

Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.

Computational methods for protein structure prediction allow us to determine a three-dimensional structure of a protein based on its pure amino acid sequence. These methods are a very important alternative to costly and slow experimental methods, like X-ray crystallography or Nuclear Magnetic Resonance. However, conventional calculations of protein structure are time-consuming and require ample computational resources, especially when carried out with the use of ab initio methods that rely on physical forces and interactions between atoms in a protein. Fortunately, at the present stage of the development of computer science, such huge computational resources are available from public cloud providers on a pay-as-you-go basis. We have designed and developed a scalable and extensible system, called Cloud4PSP, which enables predictions of 3D protein structures in the Microsoft Azure commercial cloud. The system makes use of the Warecki-Znamirowski method as a sample procedure for protein structure prediction , and this prediction method was used to test the scalability of the system. The results of the efficiency tests performed proved good acceleration of predictions when scaling the system vertically and horizontally. In the paper, we show the system architecture that allowed us to achieve such good results, the Cloud4PSP processing model, and the results of the scalability tests. At the end of the paper, we try to answer which of the scaling techniques, scaling out or scaling up, is better for solving such computational problems with the use of Cloud computing.

We present a novel dynamic analysis technique that finds real deadlocks in multi-threaded programs. Our technique runs in two stages. In the first stage, we use an imprecise dynamic analysis technique to find potential deadlocks in a multi-threaded program by observing an execution of the program. In the second stage, we control a random thread scheduler to create the potential deadlocks with high probability. Unlike other dynamic analysis techniques, our approach has the advantage that it does not give any false warnings. We have implemented the technique in a prototype tool for Java, and have experimented on a number of large multi-threaded Java programs. We report a number of previously known and unknown real deadlocks that were found in these benchmarks.

Cloud computing is a set of Information Technology services offered to users over the web on a rented base. Such services enable the organizations to scale-up or scale-down their in-house foundations. Generally, cloud services are provided by a third-party supplier who possesses the arrangement. Cloud computing has many advantages such as flexibility, efficiency, scalability, integration, and capital reduction. Moreover, it provides an advanced virtual space for organizations to deploy their applications or run their operations. With disregard to the possible benefits of cloud computing services, the organizations are reluctant to invest in cloud computing mainly due to security concerns. Security is one of the main challenges that hinder the growth of cloud computing. At the same time, service providers strive to reduce the risks over the clouds and increase their reliability in order to build mutual trust between them and the cloud customers. Various security issues and challenges are discussed in this research, and possible opportunities are stated.

This paper provides a performance analysis of the Scalable Video Coding (SVC) extension of H.264/AVC. A short overview presenting the main functionalities of SVC is given and main issues in encoder control and bit stream extraction are outlined. Some aspects of rate-distortion optimization in the context of SVC are discussed and strategies for derivation of optimized configurations relative to the investigated scalability scenarios are presented. Based on these methods, rate-distortion results for several SVC configurations are presented and compared to rate-distortion optimized H.264/AVC single layer coding. For reference, a comparison to rate-distortion optimized MPEG-4 Visual (Advanced Simple Profile) coding results is provided. The results show that the performance gap between single layer coding and scalable video coding can be very small and that SVC clearly outperforms previous video coding technology such as MPEG-4 ASP.

Multi-degree implementations of the reconfigurable optical add-drop multiplexer (ROADM) are analyzed in terms of in-band crosstalk performance with an emphasis on the reduction in the number of wavelength selective switches used in the module. It is shown experimentally that an 8-degree ROADM using the wavelength selective switches for channel adding and optical splitters for channel dropping performs closely to one that uses switches for both operations. A new modular architecture based on the wavelength selective switches is proposed for implementations applicable to large degree nodes. Performance analysis of this architecture indicates node scalability to 256 degree with crosstalk-induced power penalties below 0.5 dB.

Recommender Systems (RS) are software tools which can be used in making useful predictions of items to users. RS has been an important research area since the mid-1990s, and there are a lot of RS tools built since then to improve user satisfaction. While building the RS tools, researchers face a lot of problems in the form of data sparsity, information overload, cold-start, scalability, lack of resources and time. These factors may reduce the accuracy of predictions. To overcome these problems, researchers model the rating data as graphs. Through graphs, we can explore the transitive associations and hidden information in our dataset. Particularly, this work focuses only on the graph-based collaborative filtering (GBCF) techniques introduced in the recommendation systems. We have studied and analyzed various GBCF articles published in the most popular online digital libraries during the last two decades. These approaches have been categorized into three broader categories: user-item, user-user, and item-item based on the model of the graph, which is used for the recommendation purpose. This survey provides an understanding of how graph-based CF has helped researchers and developers built a more efficient RS model in terms of different RS goals, such as accuracy.

Wireless Sensor Networks (WSN) are formed by a large number of networked sensing nodes. It is rather complex, or even unfeasible, to model analytically a WSN and it usually leads to oversimplified analysis with limited confidence. Besides, deploying test-beds supposes a huge effort. Therefore, simulation is essential to study WSN. However, it requires a suitable model based on solid assumptions and an appropriate framework to ease implementation. In addition, simulation results rely on the particular scenario under study (environment), hardware and physical layer assumptions, which are not usually accurate enough to capture the real behavior of a WSN, thus, jeopardizing the credibility of results. However, detailed models yields to scalability and performance issues, due to the large number of nodes, that depending on application, have to be simulated. Therefore, the tradeoff between scalability and accuracy becomes a major issue when simulating WSN.