An Efficient Execution Framework of Two-Part Execution Scenario Analysis

Response Time Analysis ( RTA ) is an important and promising technique for analyzing the schedulability of real-time tasks under both Global Fixed-Priority ( G-FP ) scheduling and Global Earliest Deadline First ( G-EDF ) scheduling. Most existing RTA methods for tasks under global scheduling are dominated by partitioned scheduling, due to the pessimism of the -based interference calculation where is the number of processors. Two-part execution scenario is an effective technique that addresses this pessimism at the cost of efficiency. The major idea of two-part execution scenario is to calculate a more accurate upper bound of the interference by dividing the execution of the target job into two parts and calculating the interference on the target job in each part. This article proposes a novel RTA execution framework that improves two-part execution scenario by reducing some unnecessary calculation, without sacrificing accuracy of the schedulability test. The key observation is that, after the division of the execution of the target job, two-part execution scenario enumerates all possible execution time of the target job in the first part for calculating the final Worst-Case Response Time ( WCRT ). However, only some special execution time can cause the final result. A set of experiments is conducted to test the performance of the proposed execution framework and the result shows that the proposed execution framework can improve the efficiency of two-part execution scenario analysis by up to in terms of the execution time.

EDF-Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real-Time

Most of the multiprocessor real-time scheduling algorithms follow the partitioned approach, the global approach, or the semipartitioned approach which is a hybrid of the first two by allowing a small subset of tasks to migrate. EDF-fm (Earliest Deadline First-based Fixed and Migrating) and EDF-os (Earliest Deadline First-based Optimal Semipartitioned) are semipartitioned approaches and were proposed for soft real-time sporadic task systems. Despite their desirable property that migrations are boundary-limited such as they can only occur at job boundaries, EDF-fm and EDF-os are not always optimal and have higher tardiness and cost of overheads due to task migration. To address these issues, in this paper, we classify the systems into different types according to the utilization of their tasks and propose a new semipartitioned scheduling algorithm, earliest deadline first-adaptive, dubbed as EDF-adaptive. Our experiments show that EDF-adaptive can achieve better performance than EDF-fm and EDF-os, in terms of system utilization and tardiness overhead. It is also proved that EDF-adaptive is able to lessen the task migration overhead, by reducing the number of migrating jobs and the number of processors to which a task is migrated.

Guaranteeing Timely Response to Changes of Monitored Objects by Assigning Deadlines and Periods to Tasks

Timely response to changes of monitored objects is the key to ensuring the safety and reliability of cyber-physical systems (CPSs). There are two kinds of tasks in CPSs: update tasks and control tasks. Update tasks are responsible for updating the data in the system based on the state of the objects they monitor. Control tasks are responsible for making decisions based on the data in the system. The response time of the system to the change of a monitored object consists of two parts: the time taken by update tasks to reflect the change to the system, and the time taken by control tasks to make decisions according to the data in the system. Deadlines and periods of update tasks and control tasks directly affect the response time. Reasonable deadline and period assignment is the key to ensuring timely response to the changes of monitored objects. In this paper, we study the deadline and period assignment in CPSs. To the best of our knowledge, all existing work only focuses on the deadline and period assignment for update tasks with the goal of ensuring the freshness of the data in CPSs, and this is the first study focusing on the deadline and period assignment for both update tasks and control tasks with the goal of ensuring timely response to the changes of monitored objects. A new problem about response time control and system workload control is defined in this paper. Two deadline and period assignment methods are proposed to solve the defined problem. All the proposed methods can be used in the CPSs adopting the earliest deadline first (EDF) scheduling method. Experiments with randomly generated tasks are conducted to evaluate the performance of the proposed methods in terms of acceptance ratio and execution efficiency.

Semantics-Based Scheduling Approach of Ontology-Based Real-Time DBMS

Real-time DBMSs (DataBase Management Systems) are designed to manage transactions with time constraints and maintain the database consistency. The Quality of Service (QoS) in these systems is often evaluated based on the number of transactions satisfying their deadlines using a Feedback Control Scheduling Architecture (FCS). In this context, we are interested in the recently proposed FCS for Real-Time Ontology (FCSRTO) allowing to manage real-time ontological data, to which a specific execution of transactions is proposed. Being essential for the execution process of transactions, scheduling has been the subject of a recent work proposing the Advanced Earliest Deadline First based on Transactions Aggregation Links and Data Semantic Links (AEDF-TAL-DSL) as a scheduling protocol. It is mainly based on considering the aggregation links existing between transactions as well as the semantic links appearing between the users queries as scheduling parameters. Our work is to propose a new QoS approach called Semantics-Based FCSRTO. Our approach consists in combining the FCSRTO and the AEDF-TAL-DSL in a same architecture. Hence, we are improving the FCSRTO by using a sophisticated scheduling protocol on one hand, and on the other, it improves the AEDF-TAL-DSL scheduling protocol by managing transactions accessing to real-time ontological data. We also show the contributions provided by our QoS approach through a set of simulations.

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

<p>CubeSat is a small-sized satellite that provides a cheaper option for the manufacturer to have a fully operational satellite. Due to its size, CubeSat can only generate limited power, and this will restrict its functionality. This research aims to improve CubeSat’s power consumption by implementing the dynamic voltage and frequency scaling (DVFS) technique to on-board and data handling subsystem (OBDH). DVFS will find the best operating frequency to execute all of OBDH’s task. This paper explains how we determined the task set, representing all routine tasks performed by OBDH during normal operation mode. We have simulated the task set using two DVFS algorithms, static earliest deadline first (EDF) and cycle conserving edf (CC EDF). The result shows that both scheduling algorithms give a similar result to our task set. However, when the scheduler is configured as non-preemptive, the simulator failed to schedule the critical task. It means that the system fails to work as intended. Therefore, we conclude that we need to implement mixed-criticality scheduling to prevent critical tasks from being aborted by the system.</p>

Context-aware resource allocation and scheduling of hybrid mobile cloud

Cloud services can compensate for the resource constraints of mobile devices. However, challenges of utilizing the cloud service by mobile users arise from inherent characteristics such as user mobility and device energy. In this paper, we propose a scheme to monitor the energy level and communication quality as a part of a mobile user context information, and develop a resource allocation and scheduling scheme to adapt to the context changes by exploiting the slack time. The objective is to reduce the execution cost of the jobs while meeting the jobs deadlines set by the users. We developed Simulated Annealing based resource allocation algorithm and Earliest Deadline First scheduling. Simulation of our scheme using CloudSim and synthetic workload based on Google Cluster Traces shows benefits in reducing execution cost and improving resource utilization.

Context-aware resource allocation and scheduling of hybrid mobile cloud

Cloud services can compensate for the resource constraints of mobile devices. However, challenges of utilizing the cloud service by mobile users arise from inherent characteristics such as user mobility and device energy. In this paper, we propose a scheme to monitor the energy level and communication quality as a part of a mobile user context information, and develop a resource allocation and scheduling scheme to adapt to the context changes by exploiting the slack time. The objective is to reduce the execution cost of the jobs while meeting the jobs deadlines set by the users. We developed Simulated Annealing based resource allocation algorithm and Earliest Deadline First scheduling. Simulation of our scheme using CloudSim and synthetic workload based on Google Cluster Traces shows benefits in reducing execution cost and improving resource utilization.

Environment-driven Communication in Battery-free Smart Buildings

Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.