Abstract

he paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.

Abstract

Systems engineering is widely perceived as an empirical discipline, with a need for theoretical foundations that can facilitate reasoning about practice. This is an attempt to help build such foundations by systems‐theoretic inquiry into the nature of the relationship between knowledge and engineering. We conceptualize this relationship in terms of four worlds: the real world, the world of systems models, a world of aspect knowledge, and a world of wholes knowledge: knowledge that indicates how aspects come together and also how wholes relate to each other. This leads us to a generative understanding of systems engineering: synthesizing aspects to develop blocks; and generating the network of blocks that form a system, through recursive performance of three activities: decomposition, dependency closure and refinement. The problem of systems engineering practice involves augmenting this core with the …

Abstract

In many application scenarios, data consumed by real-time tasks are required to meet a maximum age, or freshness, guarantee. In this paper, we consider the end-to-end freshness constraint of data that is passed along a chain of tasks in a uniprocessor setting. We do so with few assumptions regarding the scheduling algorithm used. We present a method for selecting the periods of tasks in chains of length two and three such that the end-to-end freshness requirement is satisfied, and then extend our method to arbitrary chains. We perform evaluations of both methods using parameters from an embedded benchmark suite (E3S) and several schedulers to support our

Abstract

Executing multiple applications on a single MPSoC brings the major challenge of satisfying multiple quality requirements regarding real-time, energy, and so on. Hybrid application mapping denotes the combination of design-time analysis with run-time application mapping. In this article, we present such a methodology, which comprises a design space exploration coupled with a formal performance analysis. This results in several resource reservation configurations, optimized for multiple objectives, with verified real-time guarantees for each individual application. The Pareto-optimal configurations are handed over to run-time management, which searches for a suitable mapping according to this information. To provide any real-time guarantees, the performance analysis needs to be composable and the influence of the applications on each other has to be bounded. We achieve this either by spatial or a novel temporal isolation for tasks and by exploiting composable networks-on-chip (NoCs). With the proposed temporal isolation, tasks of different applications can be mapped to the same resource, while, with spatial isolation, one computing resource can be exclusively used by only one application. The experiments reveal that the success rate in finding feasible application mappings can be increased by the proposed temporal isolation by up to 30% and energy consumption can be reduced compared to spatial isolation.

Abstract

Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.

Abstract

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.

Abstract

High-level synthesis (HLS) has received significant attention in recent years, improving programmability for FPGAs. PolyMage is a domain-specific language (DSL) for image processing pipelines that also has a HLS backend to translate the input DSL into an equivalent circuit that can be synthesized on FPGAs, while leveraging an HLS suite. The data at each stage of a pipeline is stored using a fixed-point data type (alpha,beta) where alpha and beta denote the number of integral and fractional bits. The power and area savings while performing arithmetic operations on fixed-point data type is known to be significant over using floating point. In this paper, we first propose an interval-arithmetic based range analysis (alpha-analysis) algorithm to estimate the number of bits required to store the integral part of the data at each stage of an image processing pipeline. The analysis algorithm uses the homogeneity of pixel signals at each stage to cluster them and perform a combined range analysis. Secondly, we propose a software architecture for easily deploying any kind of interval/affine arithmetic based range analyses in the DSL compiler. Thirdly, we propose a new range analysis technique using Satisfiability Modulo Theory (SMT) solvers, and show that the range estimates obtained through it are very close to the lower bounds obtained through profile-driven analysis.We evaluated our bitwidth analysis algorithms on four image processing benchmarks listed in the order of increasing complexity: Unsharp Mask, Down-Up Sampling, Harris Corner Detection and Horn-Schunck Optical Flow. For example, on Optical Flow, the interval analysis based approach showed an 1.4x and 1.14x improvement on area and power metrics over floating-point representation respectively; whereas the SMT solver based approach showed 2.49x and 1.58x improvement on area and power metrics when compared to interval analysis.

Abstract

Fast-computable and accurate leakage models for state of the art CMOS digital standard cells is one of the most critical issues in present and future nano-scale technology nodes. It is further interesting if such model can calculate leakage currents not only at initial circuit life but also over the years based on Bias Temperature Instability (BTI) aging mechanism, which increases the threshold voltage over the years – thus mitigating leakage – but in turn degrades circuit speed. A reliable quantification of such aging-induced leakage mitigation opens the way to effective trade-off techniques for compensating speed degradation while maintaining leakage within specification bounds. The presented logic level leakage characterization and estimation technique, currently implemented as VHDL packages, shows more than 103 speed-ups over HSPICE circuit simulation and exhibits less than 1% error over HSPICE. We report BTI aging aware leakage current estimation for ten years at 25 °C and 90 °C in 16 nm CMOS technology, and we analyze how such leakage reduction trend can be traded off to improve the degraded circuit speed over time.

Abstract

In this paper, we propose a multi-cloud marketplace model for Infrastructure-as-a-Service (IaaS) layer with multiple cloud providers, intermediate brokers and end users. The brokers service end users subscribed to them by aggregating resources (virtual machines) from cloud providers while maximizing their profits. Similarly cloud providers (producers) allocate their supply of virtual machines to brokers (consumers) so as to maximize their profits. We define the notion of social welfare in this market structure and study two trading schemes. The first scheme involves centralized control which aims at maximizing social welfare but may contain unstable producer-consumer pairs who have an incentive to deviate from the current allocation. The second scheme eliminates such unstable pairs by using a generalization of stable matching algorithm but may lead to sub-optimal social welfare. The stable matching algorithm we proposed in this paper is a particular way of generalizing the original Gale-Shapley algorithm

Abstract

An authentication scheme handling multiple servers offers a feasible environment to users to conveniently access the rightful services from various servers using one-time registration. The practical realization of distribution of online services efficiently and transparently in multiple-server systems has come true by virtue of multi-server user authentication schemes. Due to distinguished properties like, difficulty to forge or copy, in-feasibility to lose or guess or forget, etc., biometrics have been widely preferred as a third authenticating factor in password and smart card based user authentication protocols. In this paper, we design a new biometrics-based multi-server authentication scheme based on trusted multiple-servers. We harness the concept of fuzzy extractor to provide the proper matching of biometric patterns. We evaluate our scheme through informal discussions on performance and also using Burrows-Abadi-Needham logic (BAN-logic) & random oracle model for formal security analysis. We also compose a comparative assessment of our scheme and the related ones. Outcome of the analysis and assessment shows our scheme an edge above many related and contemporary schemes.