Application of Saas to Interactive Media Marketing in Public Transportation

How to render the benefit of advertisement is a question difficult to be answered in the out-of-home advertising industry. With the advance of information technology this question can be answered efficiently and effectively. Moreover, with the innovation of gamification [5], a brand vendor can interact with its consumers directly. For media companies, they not only have to provide innovative applications to attract the attention of consumers but also to serve multiple advertisers simultaneously in the same time. In this paper, we introduce a “software as a service (SaaS)” architecture [1] of interactive marketing media to solve the problem of providing interaction between brand vendors and consumers simultaneously in the cloud computing environment. According to prior research [3], advertisers can benefit from their scalability and foci on core competencies with a cloud service. In the proposed system architecture, a series of interactive activities can be designed by an advertiser with high flexibility. The activities can be initialized with any traditional or digital signage when the attention of a consumer is caught. An instruction of online interaction with any smartphone can be provided in the signage. The consumer can follow the instructions to interact with the advertisers or to place orders online immediately. Since the whole system was designed as an interactive SoLoMo application, various multimedia content databases can be plugged in as data sources, including geographic, game, and video stream databases. The system was designed with three layers (presentation, domain, and data) distinctively according to Fowler [2]. We follow the seven guidelines suggested by Hevner et. al. [4] in the paradigm of design science research. These guidelines, including design as an artifact, problem relevant, design as a search process, research rigor, design evaluation, research contributions, and communication of research, provide us a rigorous process when developing a brand new information system or solving an existing problem with a new way [4]. We evaluated the proposed SaaS architecture with three scenarios and a benchmark comparison. The contribution of our work lies in that we provide a systematic framework of value co-creation among media owners, advertisers, and consumers. The advertising activity can be interesting, economic, and flexible on scale with the help of cloud computing

Representing variant calling format as directed acyclic graphs to enable the use of cloud computing for efficient and cost effective genome analysis

Ever since the completion of the Human Genome Project in 2003, the human genome has been represented as a linear sequence of 3.2 billion base pairs and is referred to as the "Reference Genome". Since then it has become easier to sequence genomes of individuals due to rapid advancements in technology, which in turn has created a need to represent the new information using a different representation. Several attempts have been made to represent the genome sequence as a graph albeit for different purposes. Here we take a look at the Variant Calling Format (VCF) file which carries information about variations within genomes and is the primary format of choice for genome analysis tools. This short paper aims to motivate work in representing the VCF file as Directed Acyclic Graphs (DAGs) to run on a cloud in order to exploit the high performance capabilities provided by cloud computing.N/

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

With increasing demands for High Performance Computing (HPC), new ideas and methods are emerged to utilize computing resources more efficiently. Cloud Computing appears to provide benefits such as resource pooling, broad network access and cost efficiency for the HPC applications. However, moving the HPC applications to the cloud can face several key challenges, primarily, the virtualization overhead, multi-tenancy and network latency. Software-Defined Networking (SDN) as an emerging technology appears to pave the road and provide dynamic manipulation of cloud networking such as topology, routing, and bandwidth allocation. This paper presents a new scheme called ASETS which targets dynamic configuration and monitoring of cloud networking using SDN to improve the performance of HPC applications and in particular task scheduling for HPC as a service on the cloud (HPCaaS). Further, SETSA, (SDN-Empowered Task Scheduler Algorithm) is proposed as a novel task scheduling algorithm for the offered ASETS architecture. SETSA monitors the network bandwidth to take advantage of its changes when submitting tasks to the virtual machines. Empirical analysis of the algorithm in different case scenarios show that SETSA has significant potentials to improve the performance of HPCaaS platforms by increasing the bandwidth efficiency and decreasing task turnaround time. In addition, SETSAW, (SETSA Window) is proposed as an improvement of the SETSA algorithm

RFaaS: RDMA-Enabled FaaS Platform for Serverless High-Performance Computing

The rigid MPI programming model and batch scheduling dominate high-performance computing. While clouds brought new levels of elasticity into the world of computing, supercomputers still suffer from low resource utilization rates. To enhance supercomputing clusters with the benefits of serverless computing, a modern cloud programming paradigm for pay-as-you-go execution of stateless functions, we present rFaaS, the first RDMA-aware Function-as-a-Service (FaaS) platform. With hot invocations and decentralized function placement, we overcome the major performance limitations of FaaS systems and provide low-latency remote invocations in multi-tenant environments. We evaluate the new serverless system through a series of microbenchmarks and show that remote functions execute with negligible performance overheads. We demonstrate how serverless computing can bring elastic resource management into MPI-based high-performance applications. Overall, our results show that MPI applications can benefit from modern cloud programming paradigms to guarantee high performance at lower resource costs

Proliferating Cloud Density through Big Data Ecosystem, Novel XCLOUDX Classification and Emergence of as-a-Service Era

Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X} classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed over the last few years

Deep language models for software testing and optimisation

Developing software is difficult. A challenging part of production development is ensuring programs are correct and fast, two properties satisfied with software testing and optimisation. While both tasks still rely on manual effort and expertise, the recent surge in software applications has led them to become tedious and time-consuming. Under this fast-pace environment, manual testing and optimisation hinders productivity significantly and leads to error-prone or sub-optimal programs that waste energy and lead users to frustration. In this thesis, we propose three novel approaches to automate software testing and optimisation with modern language models based on deep learning. In contrast to our methods, existing few techniques in these two domains have limited scalability and struggle when they face real-world applications. Our first contribution lies in the field of software testing and aims to automate the test oracle problem, which is the procedure of determining the correctness of test executions. The test oracle is still largely manual, relying on human experts. Automating the oracle is a non-trivial task that requires software specifications or derived information that are often too difficult to extract. We present the first application of deep language models over program execution traces to predict runtime correctness. Our technique classifies test executions of large-scale codebases used in production as “pass” or “fail”. Our proposed approach reduces by 86% the amount of test inputs an expert has to label by training only on 14% and classifying the rest automatically. Our next two contributions improve the effectiveness of compiler optimisation. Compilers optimise programs by applying heuristic-based transformations constructed by compiler engineers. Selecting the right transformations requires extensive knowledge of the compiler, the subject program and the target architecture. Predictive models have been successfully used to automate heuristics construction but their performance is hindered by a shortage of training benchmarks in quantity and feature diversity. Our next contributions address the scarcity of compiler benchmarks by generating human-likely synthetic programs to improve the performance of predictive models. Our second contribution is BENCHPRESS, the first steerable deep learning synthesizer for executable compiler benchmarks. BENCHPRESS produces human-like programs that compile at a rate of 87%. It targets parts of the feature space previously unreachable by other synthesizers, addressing the scarcity of high-quality training data for compilers. BENCHPRESS improves the performance of a device mapping predictive model by 50% when it introduces synthetic benchmarks into its training data. BENCHPRESS is restricted by a feature-agnostic synthesizer that requires thou sands of random inferences to select a few that target the desired features. Our third contribution addresses this inefficiency. We develop BENCHDIRECT, a directed language model for compiler benchmark generation. BENCHDIRECT synthesizes programs by jointly observing the source code context and the compiler features that are targeted. This enables efficient steerable generation on large scale tasks. Compared to BENCHPRESS, BENCHDIRECT matches successfully 1.8× more Rodinia target benchmarks, while it is up to 36% more accurate and up to 72% faster in targeting three different feature spaces for compilers. All three contributions demonstrate the exciting potential of deep learning and language models to simplify the testing of programs and the construction of better optimi sation heuristics for compilers. The outcomes of this thesis provides developers with tools to keep up with the rapidly evolving landscape of software engineering