ELISA: An Estimated Load Information Scheduling Algorithm for Distributed Computing Systems

In this paper, we present a decentralized dynamic load scheduling/balancing algorithm called ELISA (Estimated Load Information Scheduling Algorithm) for general purpose distributed computing systems. ELISA uses estimated state information based upon periodic exchange of exact state information between neighbouring nodes to perform load scheduling. The primary objective of the algorithm is to cut down on the communication and load transfer overheads by minimizing the frequency of status exchange and by restricting the load transfer and status exchange within the buddy set of a processor. It is shown that the resulting algorithm performs almost as well as a perfect information algorithm and is superior to other load balancing schemes based on the random sharing and Ni-Hwang algorithms. A sensitivity analysis to study the effect of various design parameters on the effectiveness of load balancing is also carried out. Finally, the algorithm's performance is tested on large dimensional hypercubes in the presence of time-varying load arrival process and is shown to perform well in comparison to other algorithms. This makes ELISA a viable and implementable load balancing algorithm for use in general purpose distributed computing systems.

Parallel optical negabinary signed-digit computing: algorithm and optical implementation

Negabinary is a component of the positional number system. A complete set of negabinary arithmetic operations are presented, including the basic addition/subtraction logic, the two-step carry-free addition/subtraction algorithm based on negabinary signed-digit (NSD) representation, parallel multiplication, and the fast conversion from NSD to the normal negabinary in the carry-look-ahead mode. All the arithmetic operations can be performed with binary logic. By programming the binary reference bits, addition and subtraction can be realized in parallel with the same binary logic functions. This offers a technique to perform space-variant arithmetic-logic functions with space-invariant instructions. Multiplication can be performed in the tree structure and it is simpler than the modified signed-digit (MSD) counterpart. The parallelism of the algorithms is very suitable for optical implementation. Correspondingly, a general-purpose optical logic system using an electron trapping device is suggested. Various complex logic functions can be performed by programming the illumination of the data arrays without additional temporal latency of the intermediate results. The system can be compact. These properties make the proposed negabinary arithmetic-logic system a strong candidate for future applications in digital optical computing with the development of smart pixel arrays. (C) 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99)00803-X].

Radio-aware service-level scheduling to minimize downlink traffic delay through Mobile Edge Computing

One of the most challenging problems in mobile broadband networks is how to assign the available radio resources among the different mobile users. Traditionally, research proposals are either speci c to some type of traffic or deal with computationally intensive algorithms aimed at optimizing the delivery of general purpose traffic. Consequently, commercial networks do not incorporate these mechanisms due to the limited hardware resources at the mobile edge. Emerging 5G architectures introduce cloud computing principles to add flexible computational resources to Radio Access Networks. This paper makes use of the Mobile Edge Computing concepts to introduce a new element, denoted as Mobile Edge Scheduler, aimed at minimizing the mean delay of general traffic flows in the LTE downlink. This element runs close to the eNodeB element and implements a novel flow-aware and channel-aware scheduling policy in order to accommodate the transmissions to the available channel quality of end users.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Tethered Monte Carlo: computing the effective potential without critical slowing down

We present Tethered Monte Carlo, a simple, general purpose method of computing the effective potential of the order parameter (Helmholtz free energy). This formalism is based on a new statistical ensemble, closely related to the micromagnetic one, but with an extended configuration space (through Creutz-like demons). Canonical averages for arbitrary values of the external magnetic field are computed without additional simulations. The method is put to work in the two-dimensional Ising model, where the existence of exact results enables us to perform high precision checks. A rather peculiar feature of our implementation, which employs a local Metropolis algorithm, is the total absence, within errors, of critical slowing down for magnetic observables. Indeed, high accuracy results are presented for lattices as large as L = 1024.

Tethered Monte Carlo: computing the effective potential without critical slowing down

We present Tethered Monte Carlo, a simple, general purpose method of computing the effective potential of the order parameter (Helmholtz free energy). This formalism is based on a new statistical ensemble, closely related to the micromagnetic one, but with an extended configuration space (through Creutz-like demons). Canonical averages for arbitrary values of the external magnetic field are computed without additional simulations. The method is put to work in the two-dimensional Ising model, where the existence of exact results enables us to perform high precision checks. A rather peculiar feature of our implementation, which employs a local Metropolis algorithm, is the total absence, within errors, of critical slowing down for magnetic observables. Indeed, high accuracy results are presented for lattices as large as L = 1024.

An Autonomic Framework for Mobile Cloud Computing

This thesis presents the formal definition of a novel Mobile Cloud Computing (MCC) extension of the Networked Autonomic Machine (NAM) framework, a general-purpose conceptual tool which describes large-scale distributed autonomic systems. The introduction of autonomic policies in the MCC paradigm has proved to be an effective technique to increase the robustness and flexibility of MCC systems. In particular, autonomic policies based on continuous resource and connectivity monitoring help automate context-aware decisions for computation offloading. We have also provided NAM with a formalization in terms of a transformational operational semantics in order to fill the gap between its existing Java implementation NAM4J and its conceptual definition. Moreover, we have extended NAM4J by adding several components with the purpose of managing large scale autonomic distributed environments. In particular, the middleware allows for the implementation of peer-to-peer (P2P) networks of NAM nodes. Moreover, NAM mobility actions have been implemented to enable the migration of code, execution state and data. Within NAM4J, we have designed and developed a component, denoted as context bus, which is particularly useful in collaborative applications in that, if replicated on each peer, it instantiates a virtual shared channel allowing nodes to notify and get notified about context events. Regarding the autonomic policies management, we have provided NAM4J with a rule engine, whose purpose is to allow a system to autonomously determine when offloading is convenient. We have also provided NAM4J with trust and reputation management mechanisms to make the middleware suitable for applications in which such aspects are of great interest. To this purpose, we have designed and implemented a distributed framework, denoted as DARTSense, where no central server is required, as reputation values are stored and updated by participants in a subjective fashion. We have also investigated the literature regarding MCC systems. The analysis pointed out that all MCC models focus on mobile devices, and consider the Cloud as a system with unlimited resources. To contribute in filling this gap, we defined a modeling and simulation framework for the design and analysis of MCC systems, encompassing both their sides. We have also implemented a modular and reusable simulator of the model. We have applied the NAM principles to two different application scenarios. First, we have defined a hybrid P2P/cloud approach where components and protocols are autonomically configured according to specific target goals, such as cost-effectiveness, reliability and availability. Merging P2P and cloud paradigms brings together the advantages of both: high availability, provided by the Cloud presence, and low cost, by exploiting inexpensive peers resources. As an example, we have shown how the proposed approach can be used to design NAM-based collaborative storage systems based on an autonomic policy to decide how to distribute data chunks among peers and Cloud, according to cost minimization and data availability goals. As a second application, we have defined an autonomic architecture for decentralized urban participatory sensing (UPS) which bridges sensor networks and mobile systems to improve effectiveness and efficiency. The developed application allows users to retrieve and publish different types of sensed information by using the features provided by NAM4J's context bus. Trust and reputation is managed through the application of DARTSense mechanisms. Also, the application includes an autonomic policy that detects areas characterized by few contributors, and tries to recruit new providers by migrating code necessary to sensing, through NAM mobility actions.

A framework for reasoning about inherent parallelism in modern object-oriented languages

With the emergence of multi-core processors into the mainstream, parallel programming is no longer the specialized domain it once was. There is a growing need for systems to allow programmers to more easily reason about data dependencies and inherent parallelism in general purpose programs. Many of these programs are written in popular imperative programming languages like Java and C]. In this thesis I present a system for reasoning about side-effects of evaluation in an abstract and composable manner that is suitable for use by both programmers and automated tools such as compilers. The goal of developing such a system is to both facilitate the automatic exploitation of the inherent parallelism present in imperative programs and to allow programmers to reason about dependencies which may be limiting the parallelism available for exploitation in their applications. Previous work on languages and type systems for parallel computing has tended to focus on providing the programmer with tools to facilitate the manual parallelization of programs; programmers must decide when and where it is safe to employ parallelism without the assistance of the compiler or other automated tools. None of the existing systems combine abstraction and composition with parallelization and correctness checking to produce a framework which helps both programmers and automated tools to reason about inherent parallelism. In this work I present a system for abstractly reasoning about side-effects and data dependencies in modern, imperative, object-oriented languages using a type and effect system based on ideas from Ownership Types. I have developed sufficient conditions for the safe, automated detection and exploitation of a number task, data and loop parallelism patterns in terms of ownership relationships. To validate my work, I have applied my ideas to the C] version 3.0 language to produce a language extension called Zal. I have implemented a compiler for the Zal language as an extension of the GPC] research compiler as a proof of concept of my system. I have used it to parallelize a number of real-world applications to demonstrate the feasibility of my proposed approach. In addition to this empirical validation, I present an argument for the correctness of the type system and language semantics I have proposed as well as sketches of proofs for the correctness of the sufficient conditions for parallelization proposed.

Application-level simulation for network security

NeSSi (network security simulator) is a novel network simulation tool which incorporates a variety of features relevant to network security distinguishing it from general-purpose network simulators. Its capabilities such as profile-based automated attack generation, traffic analysis and support for detection algorithm plug-ins allow it to be used for security research and evaluation purposes. NeSSi has been successfully used for testing intrusion detection algorithms, conducting network security analysis and developing overlay security frameworks. NeSSi is built upon the agent framework JIAC, resulting in a distributed and extensible architecture. In this paper, we provide an overview of the NeSSi architecture as well as its distinguishing features and briefly demonstrate its application to current security research projects.

Sensors for missions

An onboard payload may be seen in most instances as the “Raison d’Etre” for a UAV. It will define its capabilities, usability and hence market value. Large and medium UAV payloads exhibit significant differences in size and computing capability when compared with small UAVs. The latter have stringent size, weight, and power requirements, typically referred as SWaP, while the former still exhibit endless appetite for compute capability. The tendency for this type of UAVs (Global Hawk, Hunter, Fire Scout, etc.) is to increase payload density and hence processing capability. An example of this approach is the Northrop Grumman MQ-8 Fire Scout helicopter, which has a modular payload architecture that incorporates off-the-shelf components. Regardless of the UAV size and capabilities, advances in miniaturization of electronics are enabling the replacement of multiprocessing, power-hungry general-purpose processors for more integrated and compact electronics (e.g., FPGAs). Payloads play a significant role in the quality of ISR (intelligent, surveillance, and reconnaissance) data, and also in how quick that information can be delivered to the end user. At a high level, payloads are important enablers of greater mission autonomy, which is the ultimate aim in every UAV. This section describes common payload sensors and introduces two examples cases in which onboard payloads were used to solve real-world problems. A collision avoidance payload based on electro optical (EO) sensors is first introduced, followed by a remote sensing application for power line inspection and vegetation management.

Medical free-text to concept mapping as an information retrieval problem

Concept mapping involves determining relevant concepts from a free-text input, where concepts are defined in an external reference ontology. This is an important process that underpins many applications for clinical information reporting, derivation of phenotypic descriptions, and a number of state-of-the-art medical information retrieval methods. Concept mapping can be cast into an information retrieval (IR) problem: free-text mentions are treated as queries and concepts from a reference ontology as the documents to be indexed and retrieved. This paper presents an empirical investigation applying general-purpose IR techniques for concept mapping in the medical domain. A dataset used for evaluating medical information extraction is adapted to measure the effectiveness of the considered IR approaches. Standard IR approaches used here are contrasted with the effectiveness of two established benchmark methods specifically developed for medical concept mapping. The empirical findings show that the IR approaches are comparable with one benchmark method but well below the best benchmark.

Synergistic Execution of Stream Programs on Multicores with Accelerators

The StreamIt programming model has been proposed to exploit parallelism in streaming applications oil general purpose multicore architectures. The StreamIt graphs describe task, data and pipeline parallelism which can be exploited on accelerators such as Graphics Processing Units (GPUs) or CellBE which support abundant parallelism in hardware. In this paper, we describe a novel method to orchestrate the execution of if StreamIt program oil a multicore platform equipped with an accelerator. The proposed approach identifies, using profiling, the relative benefits of executing a task oil the superscalar CPU cores and the accelerator. We formulate the problem of partitioning the work between the CPU cores and the GPU, taking into account the latencies for data transfers and the required buffer layout transformations associated with the partitioning, as all integrated Integer Linear Program (ILP) which can then be solved by an ILP solver. We also propose an efficient heuristic algorithm for the work-partitioning between the CPU and the GPU, which provides solutions which are within 9.05% of the optimal solution on an average across the benchmark Suite. The partitioned tasks are then software pipelined to execute oil the multiple CPU cores and the Streaming Multiprocessors (SMs) of the GPU. The software pipelining algorithm orchestrates the execution between CPU cores and the GPU by emitting the code for the CPU and the GPU, and the code for the required data transfers. Our experiments on a platform with 8 CPU cores and a GeForce 8800 GTS 512 GPU show a geometric mean speedup of 6.94X with it maximum of 51.96X over it single threaded CPU execution across the StreamIt benchmarks. This is a 18.9% improvement over it partitioning strategy that maps only the filters that cannot be executed oil the GPU - the filters with state that is persistent across firings - onto the CPU.

A network forensics tool for precise data packet capture and replay in cyber-physical systems

Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and security-related anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.

Conformal geometry processing

This thesis introduces fundamental equations and numerical methods for manipulating surfaces in three dimensions via conformal transformations. Conformal transformations are valuable in applications because they naturally preserve the integrity of geometric data. To date, however, there has been no clearly stated and consistent theory of conformal transformations that can be used to develop general-purpose geometry processing algorithms: previous methods for computing conformal maps have been restricted to the flat two-dimensional plane, or other spaces of constant curvature. In contrast, our formulation can be used to produce---for the first time---general surface deformations that are perfectly conformal in the limit of refinement. It is for this reason that we commandeer the title Conformal Geometry Processing.

The main contribution of this thesis is analysis and discretization of a certain time-independent Dirac equation, which plays a central role in our theory. Given an immersed surface, we wish to construct new immersions that (i) induce a conformally equivalent metric and (ii) exhibit a prescribed change in extrinsic curvature. Curvature determines the potential in the Dirac equation; the solution of this equation determines the geometry of the new surface. We derive the precise conditions under which curvature is allowed to evolve, and develop efficient numerical algorithms for solving the Dirac equation on triangulated surfaces.

From a practical perspective, this theory has a variety of benefits: conformal maps are desirable in geometry processing because they do not exhibit shear, and therefore preserve textures as well as the quality of the mesh itself. Our discretization yields a sparse linear system that is simple to build and can be used to efficiently edit surfaces by manipulating curvature and boundary data, as demonstrated via several mesh processing applications. We also present a formulation of Willmore flow for triangulated surfaces that permits extraordinarily large time steps and apply this algorithm to surface fairing, geometric modeling, and construction of constant mean curvature (CMC) surfaces.