Middleware Support Research Articles

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

SummaryThere is a growing need for next‐generation science gateways to increase the accessibility of emerging large‐scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID‐19‐related challenges. Such science gateways that enable access to distributed computing resources for large‐scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio‐technical approach for developing a next‐generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID‐19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence‐based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug‐in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot‐assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks.

CAMID: architectural support of middleware for multiple-domain ubiquitous computing and IoT

CAMID: architectural support of middleware for multiple-domain ubiquitous computing and IoT

DIFFUSE: A DIstributed and decentralized platForm enabling Function composition in Serverless Environments

Serverless computing is an emerging proposition in the cloud offering landscape that promotes a higher level of abstraction, further decoupling software operations from the underlying hardware. Often recognized as an economically driven computational approach, the serverless model relies on the execution of short-lived stateless functions, enabling a fine-grained accounting and control of resources. In this context, function composition represents an appealing feature, allowing the composition of two or more functions to create tailored processing pipelines, incentivizing modularity and reusability of functions, while paving the way to application-specific run-time optimizations. This work presents DIFFUSE: a DIstributed and decentralized platForm enabling Function composition in Serverless Environments. DIFFUSE embodies an innovative infrastructural support, enabling the efficient and transparent composition of functions by relying on pluggable middleware support, serving as a conveyor of messages among the platform components. Broadening the deployment spectrum of our proposal, we assess different middleware solutions, each presenting distinct delivery profiles, evidencing the tradeoffs that emerge.

The Role of Mixed Criticality Technology in Industry 4.0

Embedded systems used in critical systems, such as aeronautics, have undergone continuous evolution in recent years. In this evolution, many of the functionalities offered by these systems have been adapted through the introduction of network services that achieve high levels of interconnectivity. The high availability of access to communications networks has enabled the development of new applications that introduce control functions with higher levels of intelligence and adaptation. In these applications, it is necessary to manage different components of an application according to their levels of criticality. The concept of “Industry 4.0” has recently emerged to describe high levels of automation and flexibility in production. The digitization and extensive use of information technologies has become the key to industrial systems. Due to their growing importance and social impact, industrial systems have become part of the systems that are considered critical. This evolution of industrial systems forces the appearance of new technical requirements for software architectures that enable the consolidation of multiple applications in common hardware platforms—including those of different criticality levels. These enabling technologies, together with use of reference models and standardization facilitate the effective transition to this approach. This article analyses the structure of Industry 4.0 systems providing a comprehensive review of existing techniques. The levels and mechanisms of interaction between components are analyzed while considering the impact that the handling of multiple levels of criticality has on the architecture itself—and on the functionalities of the support middleware. Finally, this paper outcomes some of the challenges from a technological and research point of view that the authors identify as crucial for the successful development of these technologies.

Automated Configuration of NoSQL Performance and Scalability Tactics for Data-Intensive Applications

This paper presents the architecture, implementation and evaluation of a middleware support layer for NoSQL storage systems. Our middleware automatically selects performance and scalability tactics in terms of application specific workloads. Enterprises are turning to NoSQL storage technologies for their data-intensive computing and analytics applications. Comprehensive benchmarks of different Big Data platforms can help drive decisions which solutions to adopt. However, selecting the best performing technology, configuring the deployment for scalability and tuning parameters at runtime for an optimal service delivery remain challenging tasks, especially when application workloads evolve over time. Our middleware solves this problem at runtime by monitoring the data growth, changes in the read-write-query mix at run-time, as well as other system metrics that are indicative of sub-optimal performance. Our middleware employs supervised machine learning on historic and current monitoring information and corresponding configurations to select the best combinations of high-level tactics and adapt NoSQL systems to evolving workloads. This work has been driven by two real world case studies with different QoS requirements. The evaluation demonstrates that our middleware can adapt to unseen workloads of data-intensive applications, and automate the configuration of different families of NoSQL systems at runtime to optimize the performance and scalability of such applications.

Deploying Fog Computing in Industrial Internet of Things and Industry 4.0

Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT.

Decentralized decision making in adaptive multi-robot teams

Abstract We present our decision support middleware PROViDE that facilitates decentralized decision making in multi-robot teams operating in highly dynamic environments with potentially unreliable communication channels and noisy sensors. Achieving an adaptive team behavior in such an environment is a challenge because the specific conditions require a fully decentralized decision process. The design of PROViDE borrows inspiration from human decision making processes. PROViDE supports replication of proposals, conflict resolution, and final team-decision making. For each of these steps a choice of methods is offered to the developer to provide flexibility for different application requirements and characteristics of execution environments. PROViDE is integrated into a comprehensive modeling framework for multi-robot systems. The main contributions of this paper are twofold: For the development of adaptive multi-robot teams we discuss requirements for a middleware that supports decentralized decision making in dynamic and adverse environments, and we demonstrate the effective and coherent integration of a set of domain-dependent decision support protocols into a middleware framework.

XWARE—A customizable interoperability framework for pervasive computing systems

Middleware support for Pervasive Computing has been extensively researched in the past. Different approaches for, e.g., discovery, communication, or interaction, have been explored and successfully tested in different lab environments. Although this works well in lab environments, where one middleware manages all resources, this will not suffice in real-world deployments, where more than one system software is present and services as well as other resources are only accessible by those.In this paper, we present XWARE, an interoperability framework that allows to integrate discovery and interaction of different middleware platforms. The flexible design allows to configure and extend the interoperability framework in order to add new platforms and tailor it for the use in different domains. Furthermore, XWARE instances can communicate with each other enabling interoperability in smart environments as well as in smart peer groups. The feasibility of our approach is discussed and evaluated based on the integration of five different platforms.

Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay

The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

NoMISHAP: A Novel Middleware Support for High Availability in Multicloud PaaS

The availability of multiple Platform as a Service (PaaS) environments, characterized by different actors, supported service models, and available components ease the engineering of new applications and facilitate their fast composition and elastic delivery. However, as demonstrated by the several outages that affected all main cloud providers along the last decade, when these applications come to a production stage there are still several open issues, especially for applications that must meet high availability requirements. This paper proposes NoMISHAP, a Novel Middleware Support for High Availability in multicloud PaaS. NoMISHAP provides the needed middleware support to develop of a new class of highly available services over multiple PaaS environments: it includes service component adapters to ease service migration between different PaaS and supports all needed monitoring, brokerage, and binding/deployment operations to enable transparent transitions between PaaS environments as failures occur at runtime. We deeply assessed NoMISHAP in a realistic scenario over Heroku, Bluemix, and Openshift PaaS platforms. Collected results show both the effectiveness of our solution and quantify the overhead, with the goal of demonstrating the suitability of middleware-based multicloud PaaS solutions to grant high availability.

REU Site: Bio-Grid Initiatives for interdisciplinary research and education

Recently more and more universities have been incorporating HPC (High Performance Computing) in their computing curriculum. The Bio-Grid REU (Research Experience for Undergraduates) Site offers undergraduate students interested or experienced in HPC a summer research opportunity to participate in projects that apply HPC in various life-science disciplines. The projects are associated with the Bio-Grid Initiatives conducted at the University of Connecticut. Training seminars are designed to equip students with background knowledge such as basic parallel programming, large-scale data analytics, and middleware support, etc., as well as some ongoing projects using these computing methods. Students participate in several collaborative projects supported by a campus-wide computational and data grid. The REU project introduces such interdisciplinary research work to students in the early stage of their academic career to spark their interest. The project aims at preparing future software engineers to formalize and solve emerging life-science problems, as well as life-science researchers with a strong background in high-performance computing. The Bio-Grid REU Site was supported by the National Science Foundation from 08–10 and 12–14, with a website located at http://biogrid.engr.uconn.edu/REU.

Use and advances in the Active Grammar-based Modeling architecture

The choice of using a robotic architecture and one of its possible implementations is one of the most crucial design decisions when developing robots. Such decision affects the whole development process, the limitations of the robot, and changing minds can be prohibitively time consuming. This paper presents the redesign and the most relevant implementation issues of the Active Grammar-based Modeling architecture (AGM), as well as the latest developments thereof. AGM is flexible, modular and designed with computation distribution and scalability in mind. In addition to a continuous refactoring of the API library and planner, the most relevant improvements are an enhanced mission specification syntax, support for representations combining symbolic and metric properties, redesigned communication patterns, and extended middleware support. A few use examples are presented to demonstrate successful application of the architecture and why some of its features were needed

Transparent and Optimized Distributed Processing on GPUs

DistributedCL is a middleware which enables transparent parallel processing on distributed GPUs. With the support of the DistributedCL middleware an application designed to use the OpenCL API can run in a distributed manner and transparently use remote GPUs without having to change or rebuild the code. The proposed architecture for the DistributedCL middleware is modular, with well-defined layers. A prototype was built according to the architecture, which considered various optimization points, including sending data in batches, network asynchronous communication and asynchronous request to the OpenCL API. The prototype was evaluated using available benchmarks and a specific benchmark, the CLBench, was developed to facilitate the evaluations according to the amount of processed data. The prototype presented good performance, higher when compared to similar proposals, which also consider transparent use of remote GPUs. The data size to be transmitted over the network was the major limiting factor.

QoS Aware Middleware Support for Dynamically Reconfigurable Component Based IoT Applications

Sensor web systems, cyber-physical systems, and the so-called Internet of Things are concepts that share a set of common characteristics. The nature of such systems is highly dynamic and very heterogeneous and issues such as interoperability, energy consumption, or resource management must be properly managed to ensure the operation of the applications within the required quality of service level. In this context, base technologies such as component based software engineering or Service Oriented Architecture can play a central role. Model driven development and middleware technologies also aid in the design, development, and operation of such systems. This paper presents a middleware solution that provides runtime support for the complete lifecycle management of a system consisting of several concurrent applications running over a set of distributed infrastructure nodes. The middleware builds up on top of a general purpose component model and is driven by a quality of service aware self-configuration algorithm that provides stateful reconfiguration capabilities in face of both internal (application triggered) and external (application unaware) reconfiguration events. The platform has been deployed over an automated warehouse supervision system that serves as a case study.

Controlling NUMA effects in embedded manycore applications with lightweight nested parallelism support

Lightweight nested parallelism support for NUMA embedded many-cores is proposed.SW- and HW-accelerated solutions are explored and integrated in OpenMP.Implementation is provided on a real manycore (STMicroelectronics STHORM).We achieve 28 speedup VS flat parallelism on several real embedded applications. Embedded manycore architectures are often organized as fabrics of tightly-coupled shared memory clusters. A hierarchical interconnection system is used with a crossbar-like medium inside each cluster and a network-on-chip (NoC) at the global level which make memory operations nonuniform (NUMA). Due to NUMA, regular applications typically employed in the embedded domain (e.g., image processing, computer vision, etc.) ultimately behave as irregular workloads if a flat memory system is assumed at the program level. Nested parallelism represents a powerful programming abstraction for these architectures, provided that (i) streamlined middleware support is available, whose overhead does not dominate the run-time of fine-grained applications; (ii) a mechanism to control thread binding at the cluster-level is supported. We present a lightweight runtime layer for nested parallelism on cluster-based embedded manycores, integrating our primitives in the OpenMP runtime system, and implementing a new directive to control NUMA-aware nested parallelism mapping. We explore on a set of real application use cases how NUMA makes regular parallel workloads behave as irregular, and how our approach allows to control such effects and achieve up to 28 speedup versus flat parallelism.

Enabling Context-Awareness by Predicate Detection in Asynchronous Environments

Pervasive applications are involving more and more autonomous computing and communicating devices, augmented with the abilities of sensing and controlling the logical/physical environment. To enable context-awareness for such applications, we are challenged by the intrinsic asynchrony of the computing environment. Predicate detection is a well studied technique dedicated to detecting global predicates over asynchronous computations and can be employed to achieve context-awareness of the asynchronous environment. However, there is no methodological framework which guides us to systematically apply the abstract predicate detection theory to the development of concrete context-aware applications. To this end, we present the Predicate Detection-based Context-Awareness (PD-CA) framework. PD-CA maps the concepts of context-awareness to concepts of predicate detection. PD-CA also presents a design process of providing middleware support for context-aware applications. Under the guidance of the PD-CA framework, we design and implement the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA). We also propose the programming toolkit to facilitate the development of context-aware applications based on MIPA, and demonstrate the use of the toolkit by a case study of a chemical plant safety management application. Experimental evaluations show the performance of MIPA in enabling context-awareness despite of the asynchrony.

Dynamic Adaptation of Mobile Applications using Cloud Offloading

Motivated by the materialization of mobile and ubiquitous computing there is a growing demand for context-aware applications that can dynamically adapt to their runtime environment. Middleware support for providing such adaptation to mobile applications has been an attractive research and development issue for several years now. In the present scenario there are two types of application that is being used. One is the offline applications in which the entire logic (or) functionality of the application is implemented locally and the other is cloud offloaded applications in which some parts of the code is executed in a remote cloud. The developer decides how the application should work. The is to combine the best of both worlds and to give user the choice. This is done by asking the user his/her choice. The user can select his choice or leave the choice for the machine to choose the most effective way which is calculated using an algorithm, thus, power and network usage can be easily planned by the user with more freedom.

An integrated runtime QoS-aware middleware framework for distributed multimedia applications

Future-generation distributed multimedia applications are expected to be highly scalable to a wide variety of heterogeneous devices, and highly adaptive across wide-area distributed environments. This demands multiple stages of run-time support in QoS-aware middleware architectures, particularly, probing the performance of QoS parameters, instantiating the initial component configurations, and adapting to on-the-fly variations. However, few of the past experiences in related work have shown comprehensive run-time support in all of the above stages they often design and build a middleware framework by focusing on only one of the run-time issues. In this paper, we argue that distributed multimedia applications need effective run-time middleware support in all these stages to be highly scalable and adaptive across a wide variety of execution environments. Nevertheless, the design of such a middleware framework should be kept as streamlined and simple as possible, leading to a novel and integrated run-time middleware platform to unify the probing, instantiation and adaptation stages. In addition, for each stage, the framework should enable the interaction of peer middleware components across host boundaries, so that the corresponding middleware function can be performed in a coordinated and coherent fashion. We present the design of such an integrated architecture, with a case study to illustrate how it is simple yet effective to monitor and configure complex multimedia applications.

Munin: A Peer-to-Peer Middleware for Ubiquitous Analytics and Visualization Spaces.

We present Munin, a software framework for building ubiquitous analytics environments consisting of multiple input and output surfaces, such as tabletop displays, wall-mounted displays, and mobile devices. Munin utilizes a service-based model where each device provides one or more dynamically loaded services for input, display, or computation. Using a peer-to-peer model for communication, it leverages IP multicast to replicate the shared state among the peers. Input is handled through a shared event channel that lets input and output devices be fully decoupled. It also provides a data-driven scene graph to delegate rendering to peers, thus creating a robust, fault-tolerant, decentralized system. In this paper, we describe Munin's general design and architecture, provide several examples of how we are using the framework for ubiquitous analytics and visualization, and present a case study on building a Munin assembly for multidimensional visualization. We also present performance results and anecdotal user feedback for the framework that suggests that combining a service-oriented, data-driven model with middleware support for data sharing and event handling eases the design and execution of high performance distributed visualizations.

Swarm Intelligence for Perception Layer Design of Internet of Things

Internet of Things is the extension and expansion of Internet from the virtual world to the real world, in which a large number of resource-constrained devices are used to collaborative perception, and it identifies objects and gathers information in the case of no centralized control, no global model. However, the swarm intelligence can take advantage of many simple autonomous individuals to exhibit complex emergent behaviors. In this paper, the characteristics and commonality of perception layer and swarm intelligence were analyzed firstly, and then the architecture of smart individual, self-organization modeling, swarm behavior emergence pattern were analyzed and designed. Finally, intelligentization for perception layer was achieved on the support of middleware. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.4149