YARTISS: A Tool to Visualize, Test, Compare and Evaluate Real-Time Scheduling Algorithms

International audienceIn this paper, we present a free software written in Java, YARTISS, which is a real-time multiprocessor scheduling simulator. It is aimed at comparing user-customized algorithms with ones from the literature on real-time scheduling. This simulator is designed as an easy-to-use modular tool in which new modules can be added without the need to decompress, edit nor recompile existing parts. It can simulate the execution of a large number of concurrent periodic independent task sets on multiprocessor systems and generate clear visual results of the scheduling process (both schedules and tunable metrics presentations). Other task models are already implemented in the simulator, like graph tasks with precedence constraints and it is easily extensible to other task models. Moreover, YARTISS can simulate task sets in which energy consumption is a scheduling parameter in the same manner as Worst Case Execution Time (WCET)

YARTISS: A Generic, Modular and Energy-Aware Scheduling Simulator for Real-Time Multiprocessor Systems

In this report, we present a free software written in Java, YARTISS, which is a real-time multiprocessor scheduling simulator. It is aimed at comparing user-customized algorithms with ones from the literature on real-time scheduling. This simulator is designed as an easy-to-use modular tool in which new modules can be added without the need to decompress, edit nor recompile existing parts. It can sim-ulate the execution of a large number of concurrent periodic independent tasksets on multiprocessor platforms and generate clear visual results of the scheduling process (both schedules and tunable metrics presentations). Other task models are already implemented in the simulator, like graph tasks with precedence constraints and it is easily extensible to other task models. Moreover, YARTISS can simulate tasksets in which energy consumption is a scheduling parameter in the same manner as Worst Case Execution Time (WCET)

Designing Enterprise Resources Planning Application for Integrating Main Activities in a Simulator Model of SCM Network Distribution

Collaborative supply chain is a specific topic in supply chain management and studied by industrial engineering students in supply chain management course. Unfortunately, conventional learning media cannot explain the phenomenon of collaborative supply chain to the students. This study aimed to design a dynamic learning media so that inter-company collaboration and information sharing on the activities of Supply Chain entities can be explained effectively to the students. The problem was solved using 3 (three) steps. First, the distribution network was described using mock up. It consists of miniature trucks, miniature network and miniature of the manufacturer-distributor-retailer embedded with tag and reader of RFID. Second, the Enterprise Resources Planning application was developed for supporting business activities. Third, we developed the integrator consists of monitor’s user interface and practice modules. The result of the research - an SCM-Simulator – will be able to improve learning skills of industrial engineering graduates, especially abilities to identify, formulate, and solve the activities of tactical plan & operational routines of Supply Chain entities. However, distribution module designed is for limited scale laboratory study of simple objects. Keywords: Distribution Network, Enterprise Resource Planning, Industrial Engineering Education, SCM Simulator,and Learning Media

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations