An Efficient Non-Preemptive Real-Time Scheduling

This paper discusses non-preemptive, real-time scheduling

QOS-DRIVEN SCHEDULING FOR MULTIMEDIA APPLICATIONS

Multimedia applications have intrinsic quality of service (QoS) requirements that may not be captured by the simple traditional completion ratio model. We have proposed a new quantitative QoS metric based on task completion ratio while differentiating firm and soft deadlines and taking data dependency into consideration. Using the decoding of MPEG movies as an example, we have shown that the proposed QoS metric is much better than completion ratio in measuring the quality of presentation (QoP) of the movies. Based on the new QoS metric, we present a set of new online algorithms that outperform popular scheduling algorithms (such as EDF, FCFS, and LETF) and enhance QoP significantly, particularly when the system is overloaded. All the proposed online algorithms have low computation overhead and can be easily integrated into real-time operating systems to improve multimedia embedded system’s performance and/or to save system resources

A Non-Preemptive Scheduling Algorithm for Soft Real-Time Systems

This article discusses a non-preemptive scheduling algorithm for soft real-time systems

Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/1/poms12606.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/2/poms12606_am.pd

Group-EDF: A New Approach and an Efficient Non-Preemptive Algorithm for Soft Real-Time Systems

Hard real-time systems in robotics, space and military missions, and control devices are specified with stringent and critical time constraints. On the other hand, soft real-time applications arising from multimedia, telecommunications, Internet web services, and games are specified with more lenient constraints. Real-time systems can also be distinguished in terms of their implementation into preemptive and non-preemptive systems. In preemptive systems, tasks are often preempted by higher priority tasks. Non-preemptive systems are gaining interest for implementing soft-real applications on multithreaded platforms. In this dissertation, I propose a new algorithm that uses a two-level scheduling strategy for scheduling non-preemptive soft real-time tasks. Our goal is to improve the success ratios of the well-known earliest deadline first (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF), is based on dynamic grouping of tasks with deadlines that are very close to each other, and using a shortest job first (SJF) technique to schedule tasks within the group. I believe that grouping tasks dynamically with similar deadlines and utilizing secondary criteria, such as minimizing the total execution time can lead to new and more efficient real-time scheduling algorithms. I present results comparing gEDF with other real-time algorithms including, EDF, best-effort, and guarantee scheme, by using randomly generated tasks with varying execution times, release times, deadlines and tolerances to missing deadlines, under varying workloads. Furthermore, I implemented the gEDF algorithm in the Linux kernel and evaluated gEDF for scheduling real applications

An Efficient Online Benefit-aware Multiprocessor Scheduling Technique for Soft Real-Time Tasks Using Online Choice of Approximation Algorithms

Maximizing the benefit gained by soft real-time tasks in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). Examples of such applications and embedded systems include real-time medical monitoring systems, video- streaming servers, multiplayer video games, and mobile multimedia devices. In these systems, tasks are not equally critical (or beneficial). Each task comes with its own benefit-density function which can be different from the others’. The sooner a task completes, the more benefit it gains. In this work, a novel online benefit-aware preemptive approach is presented in order to enhance scheduling of soft real-time aperiodic and periodic tasks in multiprocessor systems. The objective of this work is enhancing the QoS by increasing the total benefit, while reducing flow times and deadline misses. This method prioritizes the tasks using their benefit-density functions, which imply their importance to the system, and schedules them in a real-time basis. The first model I propose is for scheduling soft real-time aperiodic tasks. An online choice of two approximation algorithms, greedy and load-balancing, is used in order to distribute the low- priority tasks among identical processors at the time of their arrival without using any statistics. The results of theoretical analysis and simulation experiments show that this method is able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan with fewer missed deadlines and more balanced usage of processors. I also propose two more versions of this algorithm for scheduling SRT periodic tasks, with implicit and non-implicit deadlines, in addition to another version with a modified loadbalancing factor. The extensive simulation experiments and empirical comparison of these algorithms with the state of the art, using different utilization levels and various benefit density functions show that these new techniques outperform the existing ones. A general framework for benefit-aware multiprocessor scheduling in applications with periodic, aperiodic or mixed real-time tasks is also provided in this work.Computer Science, Department o

An adaptive load sensing priority assignment protocol for distributed real-time database systems.

Transaction processing in a distributed real time database system (DRTDBS) is coordinated by a concurrency control protocol (CCP). The performance of a CCP is affected by the load condition of a transaction processing system. For example, the performance of the Adaptive Speculative Locking (ASL) protocol degrades in high load conditions of the system. Priority protocols help a CCP by prioritizing transactions. The performance of the priority protocols is also affected by system load conditions, but they can be optimized by dynamically switching between priority protocols at run time when the system load changes. The objective of this research is to develop a protocol, Adaptive Priority Assignment protocol (APAP), which changes the priority protocol at run time to improve the performance of a CCP in a DRTDBS. APAP is implemented in a DRTDBS, where ASL is used as the underlying CCP to validate APAP. The performance of APAP was tested under varying system load conditions with various combinations of the database system parameters. Under the scenarios tested, APAP performed better than other priority protocols and demonstrated that dynamic selection of priority protocols during run time is an effective way to improve the performance of a CCP in a DRTDBS. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b183575

Ensuring Service Level Agreements for Composite Services by Means of Request Scheduling

Building distributed systems according to the Service-Oriented Architecture (SOA) allows simplifying the integration process, reducing development costs and increasing scalability, interoperability and openness. SOA endorses the reusability of existing services and aggregating them into new service layers for future recycling. At the same time, the complexity of large service-oriented systems negatively reflects on their behavior in terms of the exhibited Quality of Service. To address this problem this thesis focuses on using request scheduling for meeting Service Level Agreements (SLAs). The special focus is given to composite services specified by means of workflow languages. The proposed solution suggests using two level scheduling: global and local. The global policies assign the response time requirements for component service invocations. The local scheduling policies are responsible for performing request scheduling in order to meet these requirements. The proposed scheduling approach can be deployed without altering the code of the scheduled services, does not require a central point of control and is platform independent. The experiments, conducted using a simulation, were used to study the effectiveness and the feasibility of the proposed scheduling schemes in respect to various deployment requirements. The validity of the simulation was confirmed by comparing its results to the results obtained in experiments with a real-world service. The proposed approach was shown to work well under different traffic conditions and with different types of SLAs

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv