ClouNS - A Cloud-native Application Reference Model for Enterprise Architects

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies

Container-Managed ETL Applications for Integrating Data in Near Real-Time

As the analytical capabilities and applications of e-business systems expand, providing real-time access to critical business performance indicators to improve the speed and effectiveness of business operations has become crucial. The monitoring of business activities requires focused, yet incremental enterprise application integration (EAI) efforts and balancing information requirements in real-time with historical perspectives. The decision-making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in a timely manner. In this paper, we present an architecture for a container-based ETL (extraction, transformation, loading) environment, which supports a continual near real-time data integration with the aim of decreasing the time it takes to make business decisions and to attain minimized latency between the cause and effect of a business decision. Instead of using vendor proprietary ETL solutions, we use an ETL container for managing ETLets (pronounced “et-lets”) for the ETL processing tasks. The architecture takes full advantage of existing J2EE (Java 2 Platform, Enterprise Edition) technology and enables the implementation of a distributed, scalable, near real-time ETL environment. We have fully implemented the proposed architecture. Furthermore, we compare the ETL container to alternative continuous data integration approaches

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

AGOCS – Accurate Google Cloud Simulator Framework

This paper presents the Accurate Google Cloud Simulator (AGOCS) – a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detailed parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository

Leveraging Kubernetes in Edge-Native Cable Access Convergence

Public clouds provide infrastructure services and deployment frameworks for modern cloud-native applications. As the cloud-native paradigm has matured, containerization, orchestration and Kubernetes have become its fundamental building blocks. For the next step of cloud-native, an interest to extend it to the edge computing is emerging. Primary reasons for this are low-latency use cases and the desire to have uniformity in cloud-edge continuum. Cable access networks as specialized type of edge networks are not exception here. As the cable industry transitions to distributed architectures and plans the next steps to virtualize its on-premise network functions, there are opportunities to achieve synergy advantages from convergence of access technologies and services. Distributed cable networks deploy resource-constrained devices like RPDs and RMDs deep in the edge networks. These devices can be redesigned to support more than one access technology and to provide computing services for other edge tenants with MEC-like architectures. Both of these cases benefit from virtualization. It is here where cable access convergence and cloud-native transition to edge-native intersect. However, adapting cloud-native in the edge presents a challenge, since cloud-native container runtimes and native Kubernetes are not optimal solutions in diverse edge environments. Therefore, this thesis takes as its goal to describe current landscape of lightweight cloud-native runtimes and tools targeting the edge. While edge-native as a concept is taking its first steps, tools like KubeEdge, K3s and Virtual Kubelet can be seen as the most mature reference projects for edge-compatible solution types. Furthermore, as the container runtimes are not yet fully edge-ready, WebAssembly seems like a promising alternative runtime for lightweight, portable and secure Kubernetes compatible workloads

Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance