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.

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.

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.

A novel view on bounding execution demand under mixed-criticality EDF

In this paper, we are concerned with scheduling a mix of high-criticality (HI) and low-criticality (LO) tasks under Earliest Deadline First (EDF) on one processor. To this end, the system implements two operation modes, LO and HI mode. In LO mode, HI tasks execute for no longer than their optimistic execution budgets and are scheduled together with the LO tasks. The system switches to HI mode, where all LO tasks are prevented from running, when one or more HI tasks run for longer than expected. Since these mode changes may happen at arbitrary points in time, it is difficult to find an accurate bound on carry-over jobs, i.e., those HI jobs that were released before, but did not finish executing at the point of the transition. To overcome this problem, we propose a technique that works around the computation of carry-over execution demand. Basically, the proposed technique separates the schedulability analysis of the transition between LO and HI mode from that of stable HI mode. We prove that a transition from LO to HI mode is feasible, if an equivalent task set derived from the original is schedulable under plain EDF. On this basis, we can apply approximation techniques such as, e.g., the well-known Devi’s test to derive further tests that trade off accuracy versus complexity/runtime. Finally, we perform a detailed comparison with respect to weighted schedulability on synthetic data illustrating benefits by the proposed technique.