Editorial for FGCS Special issue on “Time-critical Applications on Software-defined Infrastructures”

Performance requirements in many applications can often be modelled as constraints related to time, for example, the span of data processing for disaster early warning [1], latency in live event broadcasting [2], and jitter during audio/video conferences [3]. These time constraints are often treated either in an “as fast as possible” manner, such as sensitive latencies in high-performance computing or communication tasks, or in a “timeliness” way where tasks have to be finished within a given window in real-time systems, as classified in [4]. To meet the required time constraints, one has to carefully analyse time constraints, engineer and integrate system components, and optimise the scheduling for computing and communication tasks. The development of a time-critical application is thus time-consuming and costly. During the past decades, the infrastructure technologies of computing, storage and networking have made tremendous progress. Besides the capacity and performance of physical devices, the virtualisation technologies offer effective resource management and isolation at different levels, such as Java Virtual Machines at the application level, Dockers at the operating system level, and Virtual Machines at the whole system level. Moreover, the network embedding [5] and software-defined networking [6] provide network-level virtualisation and control that enable a new paradigm of infrastructure, where infrastructure resources can be virtualised, isolated, and dynamically customised based on application needs. The software-defined infrastructures, including Cloud, Fog, Edge, software-defined networking and network function virtualisation, emerge nowadays as new environments for distributed applications with time-critical application requirements, but also face challenges in effectively utilising the advanced infrastructure features in system engineering and dynamic control. This special issue on “time-critical applications and software-defined infrastructures” focuses on practical aspects of the design, development, customisation and performance-oriented operation of such applications for Clouds and other distributed environments

D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method

Deadline-Budget constrained Scheduling Algorithm for Scientific Workflows in a Cloud Environment

Recently cloud computing has gained popularity among e-Science environments as a high performance computing platform. From the viewpoint of the system, applications can be submitted by users at any moment in time and with distinct QoS requirements. To achieve higher rates of successful applications attending to their QoS demands, an effective resource allocation (scheduling) strategy between workflow\u27s tasks and available resources is required. Several algorithms have been proposed for QoS workflow scheduling, but most of them use search-based strategies that generally have a higher time complexity, making them less useful in realistic scenarios. In this paper, we present a heuristic scheduling algorithm with quadratic time complexity that considers two important constraints for QoS-based workflow scheduling, time and cost, named Deadline-Budget Workflow Scheduling (DBWS) for cloud environments. Performance evaluation of some well-known scientific workflows shows that the DBWS algorithm accomplishes both constraints with higher success rate in comparison to the current state-of-the-art heuristic-based approaches