Workflow

About: Workflow is a research topic. Over the lifetime, 31996 publications have been published within this topic receiving 498339 citations.

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

Abstract: Now-a-days, Cloud computing is a technology which eludes provision cost while providing scalability and elasticity to accessible resources on a pay-per-use basis. To satisfy the increasing demand of the computing power to execute large scale scientific workflow applications, workflow scheduling is the main challenging issue in Infrastructure-as-a-Service (IaaS) clouds. As workflow scheduling belongs to NP-complete problem, so, meta-heuristic approaches are more preferred option. Users often specified deadline and budget constraint for scheduling these workflow applications over cloud resources. But these constraints are in conflict with each other, i.e., the cheaper resources are slow as compared to the expensive resources. Most of the existing studies try to optimize only one of the objectives, i.e., either time minimization or cost minimization under user specified Quality of Service (QoS) constraints. But due to the complexity of workflows and dynamic nature of cloud, a trade-off solution is required to make a balance between execution time and processing cost. To address these issues, this paper presents a non-dominance sort based Hybrid Particle Swarm Optimization (HPSO) algorithm to handle the workflow scheduling problem with multiple conflicting objective functions on IaaS clouds. The proposed algorithm is a hybrid of our previously proposed Budget and Deadline constrained Heterogeneous Earliest Finish Time (BDHEFT) algorithm and multi-objective PSO. The HPSO heuristic tries to optimize two conflicting objectives, namely, makespan and cost under the deadline and budget constraints. Along with these two conflicting objectives, energy consumed of created workflow schedule is also minimized. The proposed algorithm gives a set of Pareto Optimal solutions from which the user can choose the best solution. The performance of proposed heuristic is compared with state-of-art multi-objective meta-heuristics like NSGA-II, MOPSO, and e -FDPSO. The simulation analysis substantiates that the solutions obtained with proposed heuristic deliver better convergence and uniform spacing among the solutions as compared to others. Hence it is applicable to solve a wide class of multi-objective optimization problems for scheduling scientific workflows over IaaS clouds.

Manufacturing big data ecosystem: A systematic literature review

Abstract: Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.

KnowledgeScope: managing knowledge in context

Abstract: Knowledge repositories have been implemented in many organizations, but they often suffer from non-use. This research considers two key design factors that cause non-use: the extra burden on users to document knowledge in the repository, and the lack of a standard knowledge structure that facilitates knowledge sharing among users with different perspectives. We propose a design of a knowledge management system called KnowledgeScope that addresses these problems through (1) an integrated workflow support capability that captures and retrieves knowledge as an organizational process proceeds, i.e., within the context in which it is created and used, and (2) a process meta-model that organizes that knowledge and context in a knowledge repository. In this paper, we describe this design and report the results from implementing it in a real-life organization.

Towards Adaptive Workflow Enactment Using Multiagent Systems

Abstract: Advances in Information Technology have created opportunities for business enterprises to redesign their information and process management systems. The redesigned systems will likely employ some form of workflow management system. Workflow management systems exactly enact business processes described in a process description language. Unfortunately, such strict adherence to the prescribed workflow makes it impossible for the system to adapt to unforeseen circumstances. We firmly believe that the historic trajectory of software development paradigms and IT advancements will establish multiagent systems as the workflow enactment mechanism of the future. In this paper we provide a critical survey of workflow, workflow description languages, web services and agent technologies. We propose that workflow description languages and their associated design tools can be used to specify a multiagent system. Specifically, we advance the idea that the Business Process Execution Language for Web Services (BPEL4WS) can be used as a specification language for expressing the initial social order of the multiagent system, which can then intelligently adapt to changing environmental conditions.