Real Time Scheduling

In this paper novel technique for CPU scheduling in real time operating systems by using genetic algorithm (GA) is proposed. Proposed adaptive algorithm is a combination of existing dynamic priority driven algorithm i.e. Earliest Deadline First (EDF) and new genetic algorithm (GA) based scheduling algorithm. First we have developed GA based scheduling algorithm and tested it during both under loaded and overloaded condition. Initially, in underloaded condition EDF is used for scheduling and in overloaded condition system will change to a GA based scheduling algorithm .Thus our Adaptive algorithm uses the strong features of both algorithms and overcome their drawbacks. We have simulated, proposed adaptive algorithm along with both EDF and GA based algorithms for real time systems. %Success Rate and %Effective CPU Utilization are used as performance measuring criteria for all these 3 algorithms. The evaluation of results and comparison of our proposed adaptive CPU scheduling algorithm with EDF algorithm shows that the proposed adaptive algorithm is optimal and efficient during underloaded as well as overloaded situations compared to EDF.

In this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode and implemented real time monitoring system in real test-bed environment. The proposed scheme offers beacon based real-time scheduling for reliable association process with parent nodes and dynamically assigns network address by using NAA (Next Address Assignment) mechanism. For the large scale multi-sensor processing, our real-time monitoring system accomplished the intelligent database processing, which can generate not only the alert messages to the civilians but also process various sensing data such as fire, air, temperature and etc. Moreover, we also developed mobile robot which can support network mobility. Though the performance evaluation by using real test-bed system, we illustrate that our proposed system demonstrates promising performance for emergence monitoring systems.

Analyzing future distributed real-time systems, automotive and avionic systems, is requiring compositional hard real-time analysis techniques. Well known established techniques as SymTA/S and the real-time calculus are candidates solving the mentioned problem. However both techniques use quite simple event models. SymTA/S is based on discrete events the real-time calculus on continuous functions. Such simple models has been choosen because of the computational complexity of the considered mathematical operations required for real-time analysis. Advances in approximation techniques are allowing the consideration of more expressive descriptions of events. In this paper such a new expressive event model and its analysis algorithm are described. It integrates the models of both techniques. It is also possible in this module to integrate an approximative real-time analysis into the event model. This allows to propagate the approximation through the analysis of a distributed system leading to a much more efficient analysis.

Multicore processors have become the defacto standard today and this has led to renewed interest in multiprocessor scheduling. There are many factors that need to be considered while designing scheduling algorithms for multicore processors. These factors include reduction of overheads due to task migration / preemption, effective slack utilization and appropriate handling of

Critical Infrastructures (CIs) such as the Power Grid play an important role in our lives. Of all important aspects of CIs, real-time data delivery is the most important one because appropriate decisions cannot be made without having data delivered in a timely manner. Also, the current trend for the real-time data delivery system, specifically SCADA (Supervisory Control and Data Acquisition) systems, is to move from a closed system toward an open system that has an open architecture and uses mature computer and communication technologies. Specifically, CIs tend to adopt micro-processor-based devices a.k.a. Intelligent Electronic Devices (IEDs) with commodity multi-threaded operating systems such as Linuxbased OSs and COTS wireless LAN technology such as 802.11 WLAN. However, these trends pose a question of whether real-time guarantees, using these technologies, are feasible and can be practically implemented without many modifications of the commodity platform. In this paper, we present a design and implementation of iDSRT, a system that provides endto-end soft real-time data delivery guarantees over 802.11 WLAN with minimal changes of the operating system and no changes in the MAC layer. iDSRT consists of three important and integrated schedulers: task scheduler, packet scheduler and node scheduler to achieve the goal. The integration requires a coordination mechanism among these components. We formulate this coordination problem as a convex optimization problem that can be solved using standard convex optimization techniques. We implement iDSRT in Linux and evaluate it in an experimental testbed. The results are promising and show that iDSRT can successfully achieve soft real-time guarantees with very low packet loss rate compared to 802.11 best-effort systems.

The objective of this study is to develop a neural network based decision support system for selection of appropriate dispatching rules for a real-time manufacturing system, in order to obtain the desired performance measures given by a user, at different scheduling periods. A simulation experiment is integrated with a neural network to obtain the multi-objective scheduler, where simulation is used to provide the training data. The proposed methodology is illustrated on a flexible manufacturing system (FMS) which consists of several number of machines and jobs, loading/unloading stations and automated guided vehicles (AGVs) to transport jobs from one location to another.

The problem of maximizing quality of service (QoS) of real-time systems subject to both schedulability and energy constraints is addressed. A discrete system consisting of tasks with multiple operating modes, and which can be executed by the processor at different frequency/voltage levels, is considered. Although the system reconfiguration scheme assumes the earliest deadline first (EDF) policy and soft realtime tasks, it can be extended to other scheduling policies and handle hard real-time tasks. The described solution is suitable for adaptive real-time embedded systems which require energy savings associated to QoS. Despite being NP-Hard, the reconfiguration problem can be solved with a mixed-integer linear-programing solver sufficiently fast.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) . A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

P ECOS is a collaborative project between industrial and research part- ners that seeks to enable component-based technology for a class of embed- ded systems known as "field devices". Results so far include a component model for field devices and a composition language for specifying connections be- tween software components. Here we investigate the application of Rate Mo- notonic Analysis (RMA) to the problem of generating real-time schedules for compositions of field device components.

Time-critical jobs in many real-time applications have more than one feasible interval. Such jobs can be executed in any of their feasible intervals. Given a schedulable set of multiple feasible interval (MFI) jobs, energy can be saved by carefully selecting the executing interval for each job. In this paper, we explore the energy minimization problem for real-time systems in which jobs have more than one feasible interval. The static and dynamic energy management schemes are both investigated to minimize the energy consumption while preserving the system's feasibility. Focusing on EDF scheduling algorithm, we first study reducing the dynamic power consumption of a single CPU. We show that the static optimal speed assignment problem is NP-Hard and propose a simulated annealing (SA) based approach to solve it. Then, we develop several on-line greedy algorithms to exploit run-time slack by reselecting a job's executing interval on-the-fly. In addition, a leakage-aware version is discussed to improve the overall energy efficiency. Simulation results show that all proposed schemes achieve significant improvements of energy efficiency while the system remains schedulable.

Real-time scheduling theory is designed to provide a priori verification that all real-time tasks meet their timing requirements. However, this body of theory generally assumes that resources are instantaneously pre-emptable and ignores the costs of systems services. In previous work [1, 2] we provided a theoretical foundation for including the costs of the operating system scheduler in the real-time scheduling framework. In this paper, we apply that theory to the Real-Time (RT) Mach scheduler. We describe a methodology for measuring the components of the RT Mach scheduler in user space. We analyze the predicted performance of different real-time task sets on the target system using the scheduling model and the measured characteristics. We then verify the model experimentally by measuring the performance of the real-time task sets, consisting of RT Mach threads, on the target system, The experimental measurements verify the analytical model to within a small percentage of error. Thu...

Energy Management for multi-mode Software Defined Radio (SDR) systems remains a daunting challenge. In this paper, we focus on the issue of task allocation for multi-processor based systems with hybrid processing resources that can be reconfigured to support various operations. With the objective of minimizing energy, we present an analytical probabilistic model that considers static, dynamic, configuration and communication energy components for multiple applications characterized by certain probabilities of execution. Furthermore, we propose a fast, energy aware static task mapping heuristic to minimize the average overall energy consumption. In addition, the heuristic proposes re-ordering for the processing units for further energy reduction. Simulation results show that the proposed mapping heuristic is capable of achieving results that are within 20% of the optimal solution while providing orders of magnitude speedup in processing time. Further saving is achievable via joint mapping and placement.

Covert timing channels in real-time systems allow adversaries to not only exfiltrate application secrets but also to mount timing inference based attacks. Much effort has been put into improving real-time system predictability with the additional benefit of reducing the former class of confidentiality attacks. However, the more predictable the system behaves, the easier timing inference based attacks become. Time-triggered scheduling is particularly vulnerable to these types of attacks due to offline constructed tables that are scheduled with clock synchronization and OS-timer predictability. In this paper, we obfuscate timetriggered scheduling to complicate timing inference based attacks while maintaining strong protection against exfiltration attacks.

In this paper, we proposed a Non-preemptive Utility Accrual Scheduling (or NUAS) algorithm that enhanced the existing General Utility Scheduling (or GUS) proposed by Peng Li [1]. These algorithms were designed for adaptive real time system environment where undesirable effects such as overloads and deadline misses are tolerable and do not have great consequences to the system. We consider the independent task models that are subject to deadline constraints specified using step Time/Utility Functions (or TUFs). The main idea of our proposed algorithm is to reduce the unnecessary abortions by eliminating the greedy scheduling decision identified in GUS. We consider the scheduling objective of maximizing the accrued utility by completion of all tasks. Simulation results reveal that the proposed NUAS algorithm outperforms GUS algorithm with higher accrued utility in its entire load range.

The heterogeneity in the choice of simulation platforms for real time scheduling stands behind the difficulty of developing a common simulation environment. A Discrete Event Simulation (DES) for a real time scheduling domain encompassing event definition, time advancing mechanism and scheduler has yet to be developed. Approach: The study focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. GUS is one of the existing TUF/UA scheduling algorithms that consider the Time/Utility Function (TUF) of the executed tasks in its scheduling decision. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The TUF/ UA scheduling algorithms are design for adaptive real time system environment. The developed GUS simulator has derived the set of parameter, events, performance metrics and other unique TUF/UA scheduling element according to a detailed analysis of the base model. Results: The Accrued Utility Ratio (AUR) is investigated and compared to the benchmark of the modeled domain. Successful deployment of the GUS simulator was proven by the generated results. Conclusion: Extensive performance analysis of GUS simulator can be deployed using the developed simulator with low computational overhead. Further enhancements were to extend the developed GUS simulator with detail performance metrics together with a fault tolerance mechanism to support a reliable real time application domain.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) . A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

Component middleware is popular for enterprise distributed systems because it provides effective reuse of the core intellectual property (ie, the “business logic”). Component-based enterprise distributed real-time and embedded (DRE) systems, ...

Assume that a real-time program P T consisting of a number of parallel processes is executed on a system having a set Pr of processors which are shared between the processes by a real-time scheduler S T . Assume that P T must meet some timing deadlines. We show that such an implementation of P T can be represented as a transformationL( P T ) and that the deadlines of P T will be met if they are satisfied by the timing properties of the transformed program. The condition for feasibility of a real-time program executed under a scheduler is formalized and rules are provided for verification. The scheduler S T can be specified generically and applied to different programs, making it unnecessary to introduce low-level operations such as scheduling primitives into the programming language. Thus real-time program specification and Schedulability can be considered in the same framework and the timing properties of a program can be determined at the specification level. By separating the spe...

Fault-tolerance and timing have often been considered to be implementation issues of a program, quite distinct from the functional safety and liveness properties. Recent work has shown how these non-functional and functional properties can be verified in a similar way. However, the more practical question of determining whether a real-time program will meet its deadlines, i.e., showing that there is a feasible schedule, is usually done using scheduling theory, quite separately from the verification of other properties of the program. This makes it hard to use the results of scheduling analysis in the design, or redesign, of fault-tolerant and real-time programs. This article shows how fault-tolerance, timing, and schedulability can be specified and verified using a single notation and model. This allows a unified view to be taken of the functional and nonfunctional properties of programs and a simple transformational method to be usedto combine these properties. It also permits resu...

Generally, this paper deals with the problem of autonomy in logistics. Specifically here, a complex problem in inbound logistics is considered as real-time scheduling in a stochastic shop floor problem. Recently, in order to comply with real-time decisions, autonomous logistic objects have been suggested as an alternative. Since pallets are common used objects in carrying materials (finished or semi-finished), so they have the possibility to undertake the responsibility of real time dispatching jobs to machines in a shop-floor problem. By insisting on the role of pallets for this task, their sustainment's advantage in manufacturing systems motivated the idea of developing learning pallets. These pallets may deal with uncertainties and sudden changes in the assembly system. Here, among some intelligent techniques artificial neural network is selected to transmit the ability of decision making as well as learning to the pallets, as distributed objects. Besides, pallets make decisions based on their own experiences about the entire system and local situations. Consequently, the considered scheduling problem resembles an open shop problem with three alternative finished products. Finally, a discrete event simulation model is developed to solve this problem and defined the results of this transmission paradigm.

This work formalizes a special case of the strongly NP-Hard problem Precedence Constrained Knapsack Problem (PCKP). In this special case, the precedence constraints are restricted to paths within the same class of items, and all items have weight equal to one. This special case is named as Path-Precedence Constrained Knapsack Problem with Unit Weights (PPCKP-UW) and it has real-world applications, including a demand in logistics for solving it in real-time. This work proves that PPCKP-UW is solvable in polynomial-time, compares the computational performance of solving it using seven different approaches, including integer linear formulations, algorithms based on dynamic programming and shortest path, and applying pre/post-processing tasks to the instances. Ten new instances were generated and shared with the source codes, allowing the reproduction of all experiments made. Experimental results showed that the proposed approaches run faster than the conventional formulation based on PCKP.

One aspect to investigate in the attempt to enhance the support of process control traffic is the local transmission scheduling algorithm used to queue the requests. In the factory automation the correctness of many systems and devices depends not only on the ...

Future computer networks are expected to carry bursty real-time traffic with stringent time-delay requirements. Popular shortest-path routing protocols have the disadvantage of causing bottlenecks due to their singlepath routing. We propose a real-time routing and scheduling scheme that randomly distributes the traffic load over all available paths to the destination for load balancing and transmits the packet with the most urgency ahead of the other packets at a switch in a packet-switched network with two objectives -minimizing packet loss in the network and maximizing network throughput. Our scheme consists of two components, a routing algorithm and a scheduling algorithm. The routing algorithm randomly distributes data packets over the whole network to remove bottlenecks caused by the single-path routing of shortest-path routing protocols. The end-to-end delay is bounded by the scheduler, which uses a least-laxity scheduling algorithm. Our simulation results indicate that the proposed scheme decreases the number of packets dropped due to deadline miss and due to buffer overflow in transit in a network and increases network throughput. A desirable by-product is that the traffic load on the network tends to get evenly distributed.

Management, allocation and scheduling of heterogeneous resources for complex distributed real-time applications is a challenging problem. Timing constraints of applications may be fulfilled by a proper use of real-time scheduling policies, admission control and enforcement of timing constraints. However, it is not easy to design basic infrastructure services that allow for an easy access to the allocation of multiple heterogeneous resources in a distributed environment. In this paper, we present a middleware for providing distributed soft real-time applications with a uniform API for reserving heterogeneous resources with real-time scheduling capabilities in a distributed environment. The architecture relies on standard POSIX OS facilities, such as time management and standard TCP/IP networking services, and it is designed around CORBA, in order to facilitate modularity, flexibility and portability of the applications using it. However, real-time scheduling is supported by proper extensions at the kernel-level, plugged within the framework by means of dedicated resource managers. Our current implementation on Linux supports reservation of CPU, disk and network bandwidth. However, additional resource managers supporting alternative real-time schedulers for these resources, as well as additional types of resources, may be easily added. We present experimental results gathered on both synthetic applications and a real multimedia video streaming case study, showing advantages deriving from the use of the proposed middleware. Finally, overhead figures are reported, showing sustainability of the approach for a wide class of complex, distributed, soft real-time applications.

The increase of railway traffic efficiency and flexibility requires new real-time scheduling and control methods. New charter trains have to be added continuously without disturbing the other (periodic) train moves or decreasing the safety conditions. A distributed method to schedule new trains such that their real-time constraints are fulfilled is presented. The trains have timelines to meet and hence the deadlines are extracted taking into account the included laxity. The trains have pre-established routes specifying the stations and required arrival times. The paths containing the block sections from one station to another are dynamically allocated without leading to deadlocks.

Supercomputing power is one of the fundamental pillars of the digital society, which depends on the accurate scheduling of parallel applications in High-Performance Computing (HPC) centers to minimize computing times. However, precedence-constraint task scheduling is a well-known NP-Hard optimization problem, and no optimal polynomial-time algorithm exists to solve it. Therefore, as accurate as possible to the optimal values, heuristic algorithms are of relevant interest. Our new scheduling proposal name is EFT-GVNS, which stands for Earliest Finish Time-General Variable Neighborhood Search. EFT-GVNS uses a Composite Local Search (CLS), making our proposal more e cient than traditional GVNS. EFT-GVNS accuracy against four high-performance algorithms from the state-of-the-art (EDA, EFT-ILS, GRASP-CPA, MPQGA) and one reference algorithm in the literature (HEFT) is studied. Experimental results over four real-world applications (Fpppp, LIGO, Robot, Sparse) and 14 synthetic instances from the literature show that EFT-GVNS outperforms in terms of the median achieved results, with a global improvement of 37.6%, 27.4%, 17.8%, 6.1%, 2.2% to HEFT, EDA, EFT-ILS, GRASP-CPA, and MPQGA, respectively. EFT-GVNS achieves all the 14 optimal values of the synthetic benchmark.

In presenting this thesis in partial fulfillment of the requirements for the Master of Science degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor who supervised my thesis work, Prof. Nurul Chowdhury, or, in his absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.

Real-time scheduling theory has developed powerful tools for translating conditions on aggregate system utilization into per-task schedulability guarantees. The main breakthrough has been Liu and Layland's utilization bound for schedulability of periodic tasks. In 2001 this bound was generalized by Abdelzaher and Lu to the aperiodic task case. In this paper, we further generalize the aperiodic bound to the case of multiprocessors, and present key new insights into schedulability analysis of aperiodic tasks. We consider a special task model, called the liquid task model, representative of high-performance servers with aperiodic workloads, such as network routers, web servers, proxies, and real-time databases. For this model, we derive the optimal multiprocessor utilization bound, defined on a utilization-like metric we call "synthetic utilization". This bound allows developing constant-time admission control tests that provide utilization-based absolute delay guarantees. We show that the real utilization of admitted tasks can be close to unity even when synthetic utilization is kept below the bound. Thus, our results lead to multiprocessor systems which combine constant-time admission control with high utilization while making no periodicity assumptions regarding the task arrival pattern.

This contribution deals with scheduling problems of flexible chemical batch processes with a special emphasis on their real-time character. This implies not only the need for sufficiently short response times, but in particular the burden of in-complete knowledge about the future. To solve such problems, the application of two-stage stochastic integer programming techniques on moving horizons is proposed. They reflect the need for immediately applicable decisions and the potential of later recourse actions to cope with realized uncertainties. In addition to the classical expected value objective, simple measures of risk can be included. Motivated by an example process, some essential modeling prerequisites are discussed. As an important first step, the master scheduling problem is studied and a number of master scheduling models are presented. Large mixed-integer linear problems arise, which are well-suited for a dual decomposition approach. Numerical experiments with a problem-specific solution algorithm demonstrate the applicability of the method to real-world problems.

This paper investigates the dynamic scheduling of an automated manufacturing workcenter at which jobs are processed in batches, and there is a constant changeover time between batches of different part types. The primary measures of schedule performance are mean flow time and mean tardiness. The dynamic scheduling problem is treated as a series of static problems which are solved on a rolling-horizon basis. Characteristics of the optimal solutions to the mean flow time and mean tardiness problems are developed, and an implicit enumeration approach to the mean tardiness problem is proposed. These results are used for constructing efficient scheduling procedures for the dynamic problem. We also derive the steady state relationship between workcenter utilization level, batch size and mean flow time for one and two part types. A simulation study extends this relationship to a larger number of part types.

To assure the consistency in a database, transactions have been used as a technique for a long time. Commonly transactions impart consistency at the overhead of appropriateness (abort or retry) and source (duplicate shared data and locking), Considerable attempts have been made to confine these two qualities of transactions while fulfilling application constraints. Consistency or operational trade-off develops severe concern for any retrieval of data over a network is not only costly, but comparatively slow when compared to co-located client/database systems, in situations where consumers are geologically far from a database. Additionally, improved transactions overhead may also boost power overhead for consumers hooked on battery power. Nevertheless, for all these shortcomings, transactions do provide the data consistency which is a basic requirement for many applications. In this paper we study the solution associated to prompt transactional systems for isolated clients and centra...

In order to use the FTT-CAN protocol (Flexible Time-Triggered communication over Controller Area Network) in safety-critical applications, the impact of network errors and node failures must be thoroughly determined and minimized. This paper presents and discusses fault-tolerance techniques to limit that impact. The particular configuration of the communication system can be more or less complex and fault-tolerant as desired by the system designer. The paper includes the fault hypothesis and presents a replicated network architecture using bus guardians. An important aspect is the replication of the master node that schedules the time-triggered traffic. In this case, it is particularly important to assure a correct synchronization of the master replicas. The mechanisms that support masters' replication and synchronization are described and their performance is evaluated. The resulting architecture allows reducing the conflict between safety and flexibility, supporting the use of FTT-CAN in safety critical applications.

These recent years, a lot of real-time applications are becoming increasingly sophisticated in their data needs, resulting in a greater demand for real-time data services. Real-time database systems (RTDBS) can manage these applications, but the workload in these systems is unpredictable, then RTDBS may become overloaded. A lot of work dealing with quality of service (QoS) has been done to control the transient overshoot. They are based on feedback control real-time scheduling theory. In this paper, we propose a work which allows to execute transactions before their deadlines while using the fresh data. We have extended the feedback-based miss ratio control, by using a multi versions data architecture to guarantee a set of requirements on the behavior of RTDBS and we have considered two data managment policies. In the first policy, we have limited the maximum number of data versions and this number is the same for all the data items. In the second policy, the number is dynamically adjusted for each data item.

PurposeThe aim of this paper is to present the rationale, a numerical example and a case study of the application of an algorithm to convert non‐calendar based preventive maintenance (PM) schedules into calendar‐time format for semiconductor manufacturing systems (SMS). The resulting calendar‐time PM schedules can be utilized as a baseline within a PM scheduling optimization process.Design/methodology/approachThe algorithm utilizes estimations of work‐in‐process (WIP) and system parameters to estimate an equivalent calendar‐time schedule for PM schedules based on different units. A numerical example based on fictitious data illustrates the utilization of the conversion algorithm within a mixed PM scheduling scenario, including wafer, processing‐time and energy‐based PM tasks for multi‐chamber tools. In addition, a case study illustrates the accuracy of the algorithm by comparing estimated PM targets (i.e. due, warning and late dates) with historical data from a real semiconductor fa...

We present an optimal real-time scheduling algorithm for multiprocessors-one that satisfies all task deadlines, when the total utilization demand does not exceed the utilization capacity of the processors. The algorithm called LLREF, is designed based on a novel abstraction for reasoning about task execution behavior on multiprocessors: the Time and Local Execution Time Domain Plane (or T-L plane). LLREF is based on the fluid scheduling model and the fairness notion, and uses the T-L plane to describe fluid schedules without using time quanta, unlike the optimal Pfair algorithm (which uses time quanta). We show that scheduling for multiprocessors can be viewed as repeatedly occurring T-L planes, and feasibly scheduling on a single T-L plane results in the optimal schedule. We analytically establish the optimality of LLREF. Further, we establish that the algorithm has bounded overhead, and this bound is independent of time quanta (unlike Pfair). Our simulation results validate our analysis on the algorithm overhead.

We argue that the key underpinning of the current stateof-the real-time practice-the priority artifact-and that of the current state-of-the real-time art-deadline-based timeliness optimality-are entirely inadequate for specifying timeliness objectives, for reasoning about timeliness behavior, and for performing resource management that can dependably satisfy timeliness objectives in many dynamic real-time systems. We argue that time/utility functions and the utility accrual scheduling paradigm provide a more generalized, adaptive, and flexible approach. Recent research in the utility accrual paradigm have significantly advanced the state-of-the-art of that paradigm. We survey these advances.

Heuristic algorithms have enjoyed increasing interests and success in the context of Utility Accrual (UA) scheduling. However, few analytical results, such as bounds on task-level and system-level accrued utilities are known. In this paper, we propose the S-UA algorithm that can provide probabilistic bounds on tasklevel accrued utilities. Lower bound on system-level accrued utility ratio (AUR) is also derived and maximized by S-UA.

We consider optimal real-time scheduling of periodic tasks on multiprocessors-i.e., satisfying all task deadlines, when the total utilization demand does not exceed the utilization capacity of the processors. We introduce a novel abstraction for reasoning about task execution behavior on multiprocessors, called T-L plane and present T-L plane-based real-time scheduling algorithms. We show that scheduling for multiprocessors can be viewed as scheduling on repeatedly occurring T-L planes, and feasibly scheduling on a single T-L plane results in an optimal schedule. Within a single T-L plane, we analytically show a sufficient condition to provide a feasible schedule. Based on these, we provide two examples of T-L plane-based real-time scheduling algorithms, including non-work-conserving and work-conserving approaches. Further, we establish that the algorithms have bounded overhead. Our simulation results validate our analysis of the algorithm overhead. In addition, we experimentally show that our approaches have a reduced number of task migrations among processors, compared to a previous algorithm.

We present the first Utility Accrual (or UA) real-time scheduling algorithm for multiprocessors, called global Multiprocessor Utility Accrual scheduling algorithm (or gMUA). The algorithm considers an application model where real-time activities are subject to time/utility function time constraints, variable execution time demands, and resource overloads where the total activity utilization demand exceeds the total capacity of all processors. We consider the scheduling objective of (1) probabilistically satisfying lower bounds on each activity's maximum utility, and (2) maximizing the system-wide, total accrued utility. We establish several properties of gMUA including optimal total utility (for a special case), conditions under which individual activity utility lower bounds are satisfied, a lower bound on systemwide total accrued utility, and bounded sensitivity for assurances to variations in execution time demand estimates. Finally, our simulation experiments validate our analytical results and confirm the algorithm's effectiveness.

This paper presents a feedback control real-time scheduling (FCS) framework for adaptive real-time systems. An advantage of the FCS framework is its use of feedback control theory (rather than ad hoc solutions) as a scientific underpinning. We apply a control theory based methodology to systematically design FCS algorithms to satisfy the transient and steady state performance specifications of real-time systems.

This paper presents the simulation design of Partition PUAS (PPUAS) that is a TUF/UA real time scheduling algorithm for multicore environment using partitioned scheduling scheme. PPUAS solved the overloaded problem in uniprocessor environment. Simulation results revealed that the PPUAS were more efficient compared to the uniprocessor PUAS algorithm, producing a higher utility ratio and suitable for real time system in multicore.

In a wide category of embedded systems, sensors, controllers and actuators are located in distant locations. To ensure the control functions, these components need to be interconnected. Usually, communication networks are chosen to support the required communication exchange. In the context of the automotive industry, the Controller Area Network (CAN) bus is widely used because of its interesting properties. In theory, a CAN network can ensure real-time communications, when no transmission errors are detected. However, an increasing number of applications are sharing this bus, generating jitter and delays on the exchanged messages, which can degrade the performance of control applications. In this paper, the distributed implementation of an active suspension controller is modelled in Simulink thanks to the use of the toolbox TrueTime. The network traffic is generated using the well-known SAE benchmark. Using this simulation model, the control performance of the active suspension system is studied as a function of the available network bandwidth.

This research work considers a scenario of cloud computing job-shop scheduling problems. We consider m realtime jobs with various lengths and n machines with different computational speeds and costs. Each job has a deadline to be met, and the profit of processing a packet of a job differs from other jobs. Moreover, considered deadlines are either hard or soft and a penalty is applied if a deadline is missed where the penalty is considered as an exponential function of time. The scheduling problem has been formulated as a mixed integer non-linear programming problem whose objective is to maximize netprofit. The formulated problem is computationally hard and not solvable in deterministic polynomial time. This research work proposes an algorithm named the Tube-tap algorithm as a solution to this scheduling optimization problem. Extensive simulation shows that the proposed algorithm outperforms existing solutions in terms of maximizing net-profit and preserving deadlines.

Real-time scheduling theory has developed powerful tools for translating conditions on aggregate system utilization into per-task schedulability guarantees. The main breakthrough has been Liu and Layland's utilization bound for schedulability of periodic tasks. In 2001 this bound was generalized by Abdelzaher and Lu to the aperiodic task case. In this paper, we further generalize the aperiodic bound to the case of multiprocessors, and present key new insights into schedulability analysis of aperiodic tasks. We consider a special task model, called the liquid task model, representative of high-performance servers with aperiodic workloads, such as network routers, web servers, proxies, and real-time databases. For this model, we derive the optimal multiprocessor utilization bound, defined on a utilization-like metric we call "synthetic utilization". This bound allows developing constant-time admission control tests that provide utilization-based absolute delay guarantees. We show that the real utilization of admitted tasks can be close to unity even when synthetic utilization is kept below the bound. Thus, our results lead to multiprocessor systems which combine constant-time admission control with high utilization while making no periodicity assumptions regarding the task arrival pattern.

In this paper, we derive bounds on performance guarantees of online algorithms for real-time preemptive scheduling of jobs with deadlines on K machines when jobs are characterized in terms of their minimum stretch factor α (or, equivalently, their maximum execution rate r = 1/α). We consider two well known preemptive models that are of interest from practical applications: the hard real-time scheduling model in which a job must be completed if it was admitted for execution by the online scheduler, and the firm real-time scheduling model in which the scheduler is allowed not to complete a job even if it was admitted for execution by the online scheduler. In both models, the objective is to maximize the sum of execution times of the jobs that were executed to completion, preemption is allowed, and the online scheduler must immediately decide, whenever a job arrives, whether to admit it for execution or reject it. We measure the competitive ratio of any online algorithm as the ratio of the value of the objective function obtained by this algorithm to that of the best possible offline algorithm. We show that no online algorithm can have a competitive ratio greater than 1 − (1/α) + ε for hard real-time scheduling with K ≥ 1 machines and greater than 1 − (3/(4 α)) + ε for firm real-time scheduling on a single machine, where ε > 0 may be arbitrarily small, even if the algorithm is allowed to know the value of α in advance. On the other hand, we exhibit a simple online scheduler that achieves a competitive ratio of at least 1 − (1/α) in either of these models with K machines. The performance guarantee of our simple scheduler shows that it is in fact an optimal scheduler for hard real-time scheduling with K machines. We also describe an alternative scheduler for firm real-time scheduling on a single machine in which the competitive ratio does not go to zero as α approaches 1. Both of our schedulers do not know the value of α in advance.