Underlying Computer System Research Articles

Interactive Cyber-Physical System Hacking

Cyber Security as an education discipline covers a variety of topics that can be challenging and complex for students who are new to the subject domain. With this in mind, it is crucial that new students are motivated by understanding both the technical aspects of computing and networking, and the real-world implications of compromising these systems. In this paper we approach this task to create an engaging outreach experience, on the concept of cyber-physical systems, using a Scalextric slot-car racetrack. In the activity, students seek to compromise the underlying computer system that is linked to the track and updates the scoreboard system, in order to inflate their own score and to sabotage their opponent. Our investigation with this technique shows high levels of engagement whilst providing an excellent platform for teaching basic concepts of enumeration, brute forcing, and privilege escalation. It also provokes discussion on how this activity relates to real-world cases of cyber-physical systems security in the sports domain and beyond.

Intrusion Detection System of IPv6 Network

Abstract: There are variations within the network of the differential computing system's underlying IPv6 network. There are different blind zones for security detection. It is thoroughly investigated how the automatic data processing system differs from the underlying IPv6 network. An enhanced underlying IPv6 network security detection module design approach is suggested for the multicomputer system based on this concept. One gets the comprehensive module design plan. The new technologies like receiving thread domain, object forwarding domain, and file buffer are taken into account by the network security detection system. It can alleviate the challenges with security detection brought on by IPv6 network variations in the underlying computing system. Thus, IPv6 networks have a significantly lower number of blind zones.

Towards Faster Execution of Ensemble ML Bootstrap Based Techniques.

Algorithms for ensemble methods (EM) based on bootstrap aggregation often perform copious amount of redundant computations (RC) thus limiting their practicality. Given this constraint, we propose a framework that views these algorithms as a collection of computational units (cu), a tightly coupled set of both mathematical operations and data. This view facilitates a reduction in RC (RRC), thereby allowing for faster execution plans. Inspired by the floor tiling approach in VLSI, we look to engineer solutions for RRC while possibly reconfiguring the underlying computing system's compiler technology stack. We start by showing that under the assumption that the computational system has unbounded but finite memory (i.e., the memory is large enough to hold all intermediate values) and that each cu has a uniform cost, our approach reduces to a well-studied directed bandwidth problem for the directed acyclic graphs (DAGs). Next, we consider a more realistic scenario where the computing system has limited memory and concurrent execution while still assuming a uniform cost. Using a new notion of (r,s) set cover of a DAG (nodes representing computational units and edges representing their interdependencies) we formulate the problem of reducing redundant computational steps in EM as a variation of a directed bandwidth problem. We show that the graph's minimum bandwidth is closely related to memory requirements for studying RRC. Finally, our preliminary experimental results are supportive of the proposed approach for RRC and promising that it can be applied to a broader set of algorithms in decision sciences.

FadingBF: A Bloom Filter With Consistent Guarantees for Online Applications

Bloom filter (BF), when used by an online application, experiences monotonically increasing false-positive errors. The decay of stale elements can control false-positives. Existing mechanisms for decay require unreasonable storage and computation. Inexpensive methods reset the BF periodically, resulting in inconsistent guarantees and performance issues in the underlying computing system. In this article, we propose Fading Bloom filter (FadingBF), which can provide inexpensive yet safe decay of elements. FadingBF neither requires additional storage nor computation to achieve this but instead exploits the underlying storage medium’s intrinsic properties, i.e., DRAM capacitor characteristics. We realize FadingBF by implementing the BF on a DRAM memory module with its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">periodic refresh disabled</i> . Consequently, the capacitors holding the data elements that are not accessed frequently will predictably lose charge and naturally decay. The retention time of capacitors guarantees against premature deletion. However, some capacitors may store information longer than required due to the FadingBF’s software and hardware variables. Using an analytical model of the FadingBF, we show that carefully tuning its parameters can minimize such cases. For a surveillance application, we demonstrate that FadingBF achieves better guarantees through graceful decay, consumes 57 percent lesser energy, and has a system load that is lesser than the standard BF.

Weaving seams with data: Conceptualizing City APIs as elements of infrastructures

This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of smart cities and communities. On top of the built infrastructures in cities, application programming interfaces allow to weave new kinds of seams from static and dynamic data sources into the urban fabric. Contributing to debates about “urban informatics” and the governance of urban information infrastructures, this article provides a technically informed and critically grounded approach to evaluating APIs as crucial but often overlooked elements within these infrastructures. The conceptualization of what we term City APIs is informed by three perspectives: In the first part, we review established criticisms of proprietary social media APIs and their crucial function in current web architectures. In the second part, we discuss how the design process of APIs defines conventions of data exchanges that also reflect negotiations between API producers and API consumers about affordances and mental models of the underlying computer systems involved. In the third part, we present recent urban data innovation initiatives, especially CitySDK and OrganiCity, to underline the centrality of API design and governance for new kinds of civic and commercial services developed within and for cities. By bridging the fields of criticism, design, and implementation, we argue that City APIs as elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation between data science, technological development, urban management, and civic participation.

Rolex: resilience-oriented language extensions for extreme-scale systems

Future exascale high-performance computing (HPC) systems will be constructed from VLSI devices that will be less reliable than those used today, and faults will become the norm, not the exception. This will pose significant problems for system designers and programmers, who for half-a-century have enjoyed an execution model that assumed correct behaviour by the underlying computing system. The mean time to failure of the system scales inversely to the number of components in the system and, therefore, faults and resultant system level failures will increase, as systems scale in terms of the number of processor cores and memory modules used. However, every error detected need not cause catastrophic failure. Many HPC applications are inherently fault resilient. Yet it is the application programmers who have this knowledge but lack mechanisms to convey it to the system. In this paper, we present new Resilience Oriented Language Extensions (Rolex) which facilitate the incorporation of fault resilience as an intrinsic property of the application code. We describe the syntax and semantics of the language extensions as well as the implementation of the supporting compiler infrastructure and runtime system. Our experiments show that an approach that leverages the programmer’s insight to reason about the context and significance of faults to the application outcome significantly improves the probability that an application runs to a successful conclusion.

Virtual Shuffling for Efficient Data Movement in MapReduce

MapReduce is a popular parallel processing framework for large-scale data analytics. To keep up with the increasing volume of datasets, it requires efficient I/O capability from the underlying computer systems to process and analyze data in two phases (mapping and reducing). Between these phases, MapReduce requires a shuffling phase to globally exchange the intermediate data generated by the mapping phase. We reveal that data shuffling, by physically moving segments of intermediate data across disks, causes significant I/O contention and compounds the I/O problem. In this paper, we propose a novel virtual shuffling strategy to enable efficient data movement and reduce I/O for MapReduce shuffling, thereby reducing power consumption and conserving energy. Virtual shuffling is realized through a combination of three techniques including a three-level segment table, near-demand merging, and dynamic and balanced merging subtrees. Our experimental results show that virtual shuffling significantly speeds up data movement in MapReduce and achieves faster job execution. Particularly, its reduction in disk I/O accesses results in as much as 12% savings in power consumption for MapReduce programs.

Design and Evaluation of Network-Levitated Merge for Hadoop Acceleration

Hadoop is a popular open source implementation of the MapReduce programming model for cloud computing. However, it faces a number of issues to achieve the best performance from the underlying systems. These include a serialization barrier that delays the reduce phase, repetitive merges, and disk accesses, and the lack of portability to different interconnects. To keep up with the increasing volume of data sets, Hadoop also requires efficient I/O capability from the underlying computer systems to process and analyze data. We describe Hadoop-A, an acceleration framework that optimizes Hadoop with plug-in components for fast data movement, overcoming the existing limitations. A novel network-levitated merge algorithm is introduced to merge data without repetition and disk access. In addition, a full pipeline is designed to overlap the shuffle, merge, and reduce phases. Our experimental results show that Hadoop-A significantly speeds up data movement in MapReduce and doubles the throughput of Hadoop. In addition, Hadoop-A significantly reduces disk accesses caused by intermediate data.

Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee

Autonomic server provisioning for performance assurance is a critical issue in Internet services. It is challenging to guarantee that requests flowing through a multi-tier system will experience an acceptable distribution of delays. The difficulty is mainly due to highly dynamic workloads, the complexity of underlying computer systems, and the lack of accurate performance models. We propose a novel autonomic server provisioning approach based on a model-independent self-adaptive Neural Fuzzy Control (NFC). Existing model-independent fuzzy controllers are designed manually on a trial-and-error basis, and are often ineffective in the face of highly dynamic workloads. NFC is a hybrid of control-theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. We further enhance NFC to compensate for the effect of server switching delays. Extensive simulations demonstrate that, compared to a rule-based fuzzy controller and a Proportional-Integral controller, the NFC-based approach delivers superior performance assurance in the face of highly dynamic workloads. It is robust to variation in workload intensity, characteristics, delay target, and server switching delays. We demonstrate the feasibility and performance of the NFC-based approach with a testbed implementation in virtualized blade servers hosting a multi-tier online auction benchmark.

Improving the future by examining the past

During the last fifty years, the technology underlying computer systems has improved dramatically. As technology has evolved, designers have made a series of choices in the way it was applied in computers. In some cases, decisions that were made in the twentieth century make less sense in the twenty-first. Conversely, paths not taken might now be more attractive given the state of technology today, particularly in light of the limits the field is facing, such as the increasing gap between processor speed and storage access times and the difficulty of cooling today's computers. In this talk, I'll discuss some of these choices and suggest some possible changes that might make computing better in the twenty-first century.

L'interaction en réalité virtuelle. Etat de l'art

This article presents a survey of interaction techniques in virtual reality, with the goal to highlight that 3D interaction is a huge domain and difficult to apprehend. Virtual Reality is mainly a mean to interact with computer generated 3D world. The user would like to naviguate in the scene, manipulate its environment and act on the underlying computer system. That 3D interaction can rely on all sensorial modalities, by using a very large range of peripherals. We propose a non-exhaustive survey of existing techniques, based on previous taxonomies, and by adding a brief state of the art on the symbolic input task. We will see that Virtual Reality doesn 't only offer a work-around to the real world, but it also extends it to its creator's imagination borders.

Adaptive disk scheduling with workload-dependent anticipation intervals

Anticipatory scheduling (AS) of I/O requests has become a viable choice for block-device schedulers in open-source OS-kernels as prior work has established its superiority over traditional disk-scheduling policies. An AS-scheduler selectively stalls the block-device right after servicing a request in hope that a new request for a nearby sector will be soon posted. Clearly, this decision may introduce delays if the anticipated I/O does not arrive on time. In this paper, we build on the success of the AS and propose an approach that minimizes the overhead of unsuccessful anticipations. Our suggested approach termed workload-dependent anticipation scheduling (WAS), determines the length of every anticipation period in an on-line fashion in order to reduce penalties by taking into account the evolving spatio-temporal characteristics of running processes as well as properties of the underlying computing system. We harvest the spatio-temporal features of individual processes and employ a system-wide process classification scheme that is re-calibrated on the fly. The resulting classification enables the disk scheduler to make informed decisions and vary the anticipation interval accordingly, on a per-process basis. We have implemented and incorporated WAS into the current Linux kernel. Through experimentation with a wide range of diverse workloads, we demonstrate WAS benefits and establish reduction of penalties over other AS-scheduler implementations.

Programs for the approximation of real and imaginary single- and multi-valued functions by means of Hermite–Padé-approximants

We present programs for the calculation and evaluation of special type Hermite–Padé-approximations. They allow the user to either numerically approximate multi-valued functions represented by a formal series expansion or to compute explicit approximants for them. The approximation scheme is based on Hermite–Padé polynomials and includes both Padé and algebraic approximants as limiting cases. The algorithm for the computation of the Hermite–Padé polynomials is based on a set of recursive equations which were derived from a generalization of continued fractions. The approximations retain their validity even on the cuts of the complex Riemann surface which allows for example the calculation of resonances in quantum mechanical problems. The programs also allow for the construction of multi-series approximations which can be more powerful than most summation methods. Program summary Title of program: hp.sr Catalogue identifier: ADSO Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADSO Program obtainable from: CPC Program Library, Queen's University Belfast, Northern Ireland Licensing provisions: Persons requesting the program must sign the standard CPC non-profit use license Computer: Sun Ultra 10 Installation: Computing Center, University of Regensburg, Germany Operating System: Sun Solaris 7.0 Program language used: MapleV.5 Distribution format: tar gzip file Memory required to execute with typical data: 32 MB; the program itself needs only about 20 kB Number of bits in a word: 32 No. of processors used: 1 Has the code been vectorized?: no No. of bytes in distributed program, including test data etc.: 38194 No. of lines in distributed program, including test data, etc.: 4258 Nature of physical problem: Many physical and chemical quantum systems lead to the problem of evaluating a function for which only a limited series expansion is known. These functions can be numerically approximated by summation methods even if the corresponding series is only asymptotic. With the help of Hermite–Padé-approximants many different approximation schemes can be realized. Padé and algebraic approximants are just well-known examples. Hermite–Padé-approximants combine the advantages of highly accurate numerical results with the additional advantage of being able to sum complex multi-valued functions. Method of solution: Special type Hermite–Padé polynomials are calculated for a set of divergent series. These polynomials are then used to implicitly define approximants for one of the functions of this set. This approximant can be numerically evaluated at any point of the Riemann surface of this function. For an approximation order not greater than 3 the approximants can alternatively be expressed in closed form and then be used to approximate the desired function on its complete Riemann surface. Restriction on the complexity of the problem: In principle, the algorithm is only limited by the available memory and speed of the underlying computer system. Furthermore the achievable accuracy of the approximation only depends on the number of known series coefficients of the function to be approximated assuming of course that these coefficients are known with enough accuracy. Typical running time: 10 minutes with parameters comparable to the testruns Unusual features of the program: none

Something Third, Other

Something Third, Other

On the symbiosis of memory and communication in the programming of parallel applications

Interoperability in non-sequential applications requires communication to exchange information using either the shared-memory or message-passing paradigm. In the past, the communication paradigm in use was determined through the architecture of the underlying computing platform. Shared-memory computing systems were programmed to use shared-memory communication, whereas distributed-memory architectures were running applications communicating via message-passing. Current trends in the architecture of parallel machines are based on shared-memory and distributed-memory. For scalable parallel applications, in order to maintain transparency and efficiency, both communication paradigms have to coexist. Users should not be obliged to know when to use which of the two paradigms. On the other hand, the user should be able to exploit either of the paradigms directly in order to achieve the best possible solution. The paper presents the VOTE communication support system. VOTE provides coexistent implementations of shared-memory and message-passing communication. Applications can change the communication paradigm dynamically at runtime, thus we are able to employ the underlying computing system in the most convenient and application-oriented way. The presented case study and detailed performance analysis underpins the applicability of the VOTE concepts for high-performance parallel computing. By having implemented a symbiosis of memory and communication at the level of virtual shared memory, the end-user message-passing performance was significantly improved.

On dynamically updating a computer program: From concept to prototype

An approach to dynamically updating a computer program, i.e., updating while it is executing, is presented. Dynamic updating is crucial in applications where the cost of stopping and restarting the program makes doing so impractical. The presented system works with programs written in procedural languages such as Pascal and C. It is assumed that computer programs are written in a top-down manner consistent with good software engineering practices. Also assumed is that the underlying computer system logically provides a network-wide sparse virtual address space. Using these assumptions, it is possible to update computer programs with minimum interruption to the running program. By partitioning the address space into a number of version spaces, the handling of multiple simultaneous updates is possible. This allows one update to begin before previous updates complete. Via appropriate mapping mechanisms, old versions of procedures may call new procedures and maintain consistency. An overview of the design and implementation of a working prototype updating system is discussed and a sample updating session is illustrated.

Problems in modelling tasks and task views

We present the rudiments of a new theory of tasks and task views. The motivating idea is essentially as follows: in an environment in which several organizational agents cooperate to accomplish a common task, each of the agents need only know its own part of the task — this is the agent's task view. The underlying computer system may take responsibility for coordinating the actions among the different views. We discuss what task views are and why they are interesting. We also consider consistent task specification based on partial orderings of task actions and the problems involved. The major issues are the expressiveness of action conditions, tractable satisfaction of action conditions, and tractable testing for task consistency. Our notion of task consistency leads us to consider, among other things, the “expressiveness versus tractability” tradeoff encountered in knowledge representation, planning work, and a recent proposal for specification and refinement of office procedures.