Complex Timing Constraints Research Articles

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics.

Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.

Intelligent Decision Support System of Emergency Language Based on Fog Computing

In recent years, various emergencies have frequently occurred worldwide, which has forced relevant service departments to pay more attention to decision-making and emergency management. Since emergency events are characterized by complex environments, unstable events, and time constraints, events usually involve multiple factors and promptly correct errors in the decision-making process. In fact, in many cases, emergency decision-making needs to select an optimal one from multiple alternatives for execution. The fog algorithm decision-making method can solve the problem of optimal solution selection, and it has been widely used in many fields. This article evaluates the emergencies that have occurred in the past 10 years. The evaluation indicators include direct economic loss, indirect reputation loss, ecological environment indicators, and healthy living indicators. The first two are cost-based indicators. The index value of direct economic loss and indirect reputation loss is as small as possible, while the index value of ecological environment index and healthy living index is the larger the better. Among the many selected emergencies, only the index evaluation scores of fires are reliable ( P < 0.01 ), and the evaluation scores of other emergencies belonging to natural disasters are a bit wrong ( P > 0.05 ). The reason for this may be that the direct economic losses caused by natural disasters are not well counted, and the families involved and the environment are too wide. Therefore, the emergency language intelligent decision support system based on fog computing has a good development prospect.

Batch process scheduling with es-graph: a case study

The scheduling of batch processes is a widely researched field of chemical engineering. Over the last few decades, a great number of tools have been developed for industrial examples and literature problems. These methods vary not only in their applied model, but in their representation of the problem inputs as well. The most well-known representations are the State-Task Network, the Resource-Task Network, the State Sequence Network, and the S-graph. While the latter also serves as the mathematical model for the related optimization approaches, the others only act as an intermediate model between the raw problem data and the model used for optimization, e.g., a mixed-integer linear programming model. The eS-graph model is a generalization of the S-graph, where the one-to-one relation between nodes and tasks has been relaxed, allowing a much wider range of scheduling problems to be tackled. Processes may simultaneously occupy several units, and release them at different stages of execution, and certain stages of separate processes can be forced to overlap in time. As in the case of the S-graph, the eS-graph models can be solved to optimality by specially designed combinatorial algorithms or serve as a basis for precedence based linear programming formulations. In this work, the modelling capacities of the eS-graph framework are illustrated via a Polymer production case study, where complex timing constraints are present.

Preceptors' experiences of using a competence assessment tool to assess undergraduate nursing students

The purpose of this study was to explore Irish preceptors' experience of using a competence tool to assess undergraduate nursing students' clinical competence. This study used a mixed methods design. This study was conducted in two phases, the qualitative phase involved six focus group interviews to ascertain preceptors' experiences of using an assessment tool to assess clinical competence. The quantitative phase involved a descriptive survey measuring preceptors (N = 843) attitudes linked with the use of the assessment tool.The key themes that emerged from qualitative analysis were challenges of using the assessment tool, recognising competence and valuing adult learners. The challenges of using the tool included negotiating complex language and time constraints in completing assessments. Recognising competence revealed the use of intuition and subjectivity. While valuing adult learners acknowledged the reciprocal learning process between the preceptor and the learner. These findings reveal the inherent skills of preceptors to intuitively and subjectively recognise competence. The quantitative findings revealed merits and challenges for the preceptors using the assessment tool. In particular the complexity of the language was highlighted as an issue. A key recommendation from this research is the need to revise the assessment tool to support objective and subjective measurement of competence.

Robust Optimization of Multiple Timing Constraints

Modern field-programmable gate array (FPGA) circuit designs often contain multiple clocks and complex timing constraints, and achieving these constraints requires timing optimization at all stages of the computer-aided design (CAD) flow. To our knowledge, no prior published work has either described or quantitatively evaluated how to compute connection timing criticalities for circuits with multiple timing constraints in order to best guide CAD optimization algorithms. While single-clock techniques have a simple extension to multi-clock circuits, this formulation is not robust for circuits with multiple constraints of different magnitudes, or impossible constraints. We describe a robust method of timing optimization for circuits with multiple timing constraints, implemented in the open-source versatile place and route FPGA CAD tool. Our formulation can optimize multiple constraints well, even in the case where some constraints are impossible, and achieves over 20% greater clock speed with aggressive constraints than a straight-forward extension of single-clock work.

Using tablet-based technology in patient education about systemic therapy options for early-stage breast cancer: a pilot study.

Patient education in early-stage breast cancer has been shown to improve patient well-being and quality of life, but it poses a challenge given the increasingly complex regimens and time constraints in clinical practice. Technology-aided teaching in the clinic could help to improve the understanding of adjuvant systemic therapy for patients. In this prospective pilot study, we used a clinician-administered, tablet-based teaching aid to teach patients with early-stage breast cancer about adjuvant systemic therapy. Participation was offered to newly diagnosed patients with early-stage breast cancer presenting for their first medical oncology visit at a provincial cancer centre. Participants were shown a tablet-based presentation describing procedures, rationales, risks, and benefits of adjuvant systemic therapy as an adjunct to a discussion with the medical oncologist. After the clinic visit, participants completed a questionnaire measuring satisfaction with the visit and knowledge of the treatment plan discussed. The 25 patients recruited for the study had a mean age of 57 years. An offer of upfront chemotherapy alone was made to 12 participants (48%), chemotherapy with trastuzumab to 4 (16%), and hormonal therapy to 9 (36%). Correct answers to all questions related to treatment knowledge were given by 22 patients (88%). Satisfaction with the clinic visit was high (mean satisfaction score: 4.53 ± 0.1 of a possible 5). We found that a tablet-based presentation about adjuvant systemic therapy was satisfactory to patients with early-stage breast cancer and that knowledge retention after the clinic visit was high. Tablet-based teaching could be a feasible and effective way of educating patients in the breast oncology clinic and warrants further investigation in randomized studies.

Communicating with graduate medical trainees: the Cleveland Clinic experience.

Optimizing communication with graduate medical trainees is critical, as they contribute importantly to the mission of academic medical centres. Yet, communication is challenged by their complex schedules, geographic separation, and time constraints. Few studies have examined this issue to offer valuable solutions. Because traditional approaches are suboptimal, two communication tools were implemented: (1) a web-based intranet site called [graduate medical education] GME|com, and (2) an electronic newsletter, GME|com Headlines. The goals were to: (1) build a single repository of information relevant to trainees, programme directors, and coordinators, and (2) minimize their email burdens. A post-launch survey showed that >75 % of respondents indicated they visited the site and, of those, >90 % perceived value to the site. Analysis of use over the first year showed 39,377 visits (mean 108/day) and 93,785 pageviews. Sixty percent of users visited GME|com between 9 and 201 times and 18 % >201 times. A survey of programme directors from the 25 largest training programmes in the US confirmed the challenges of communicating with trainees and suboptimal results of current solutions. GME|com and Headlines represent complementary communication tools that have been well-received and frequently used. Future opportunities include assessing the association of GME|com use with increments in quality and patient safety.

Determining and Mapping of Vegetation using GIS and Phytosociological Approach in Mount Tahan, Malaysia

The study on lowland tropical forest plants is complicated by the extreme species diversity, very complex plantmosaic and time constraints. These impediments however do not occur in mountain forest, where habitatdiversity is clearly distinguished by small homogenous plants types. Plant association and composition studywere presented in this work from two locations of Mount Tahan, Malaysia the relatively untrampled and thetrampled site. In each site, plant species number, vegetation cover, plant height, and species cover and frequencyin untrampled and trampled areas were counted or measured. The analysis included a field survey following therelevance method of Braun-Blanquet and mapping using a GIS. The study focused on altitudinal distribution ofspecific plants communities located between 1900 m and 2140 m altitude. The data from field survey weremapped and analyzed in GIS. The phytosociological classification revealed that untrampled areas in Botak andPuncak sites were high in species more diversified communities compared to the trampled areas. The resultsshowed that Leptospermum flavescens was the dominant species most in both sites (Botak and Puncak), with48%, specifically at the untrampled site. The abundance class and sociability value are also high for this specieswith score 4.5 out of 5, respectively. A total number of trees in the area probably play an important role inquantifying the species richness and diversity parameters. From the study it can be concluded that GIS techniqueuseful in developing a tree mapping system and creating a geo-database for spatial analysis. Further studies arerecommended to integrate more data into the system for better evaluation.

An Empirical Analysis of Team Coordination Behaviors and Action Planning With Application to Human–Robot Teaming

We conducted an empirical analysis of human teamwork to investigate the ways teammates incorporate coordination behaviors, including verbal and nonverbal cues, into their action planning. In space, military, aviation, and medical industries, teams of people effectively coordinate to perform complex tasks under stress induced by uncertainty, ambiguity, and time pressure. As robots increasingly are introduced into these domains, we seek to understand effective human-team coordination to inform natural and effective human-robot coordination. We conducted teamwork experiments in which teams of two people performed a complex task, involving ordering, timing, and resource constraints. Half the teams performed under time pressure, and half performed without time pressure. We cataloged the coordination behaviors used by each team and analyzed the speed of response and specificity of each coordination behavior. Analysis shows that teammates respond to explicit cues, including commands meant to control actions, more quickly than implicit cues, which include short verbal and gestural attention getters and status updates. Analysis also shows that nearly all explicit cues and implicit gestural cues were used to refer to one specific action, whereas approximately half of implicit cues did not often refer to one specific action. These results provide insight into how human teams use coordination behaviors in their action planning. For example, implicit cues seem to offer the teammate flexibility on when to perform the indicated action, whereas explicit cues seem to demand immediate response. We discuss how these findings inform the design of more natural and fluid human-robot teaming.

Verifying Real-Time Systems using Explicit-time Description Methods

Timed model checking has been extensively researched in recent years. Many new formalisms with time extensions and tools based on them have been presented. On the other hand, Explicit-Time Description Methods aim to verify real-time systems with general untimed model checkers. Lamport presented an explicit-time description method using a clock-ticking process (Tick) to simulate the passage of time together with a group of global variables for time requirements. This paper proposes a new explicit-time description method with no reliance on global variables. Instead, it uses rendezvous synchronization steps between the Tick process and each system process to simulate time. This new method achieves better modularity and facilitates usage of more complex timing constraints. The two explicit-time description methods are implemented in DIVINE, a well-known distributed-memory model checker. Preliminary experiment results show that our new method, with better modularity, is comparable to Lamport’s method with respect to time and memory efficiency.

Handling mixed sets of tasks in combined offline and online scheduled real-time systems

Many industrial applications with real-time demands are composed of mixed sets of tasks with a variety of requirements. These can be in the form of standard timing constraints, such as period and deadline, or complex, e.g., to express application specific or nontemporal constraints, reliability, performance, etc. As many algorithms focus on specific sets of task types and constraints only, system design has to focus on those supported by a particular algorithm, at the expense of the rest. In this paper, we present a method to deal with a combination of mixed sets of tasks and constraints: periodic tasks with complex and simple constraints, soft and firm aperiodic, and sporadic tasks. We propose the use of an offline scheduler to manage complex timing and resource constraints of periodic tasks and transform these into a simple EDF model with start-times and deadlines. At run-time, the execution of the offline scheduled tasks is flexibly shifted in order to allow for feasible inclusion of dynamically arriving sporadic and aperiodic tasks. Sporadic tasks are guaranteed offline based on their worst-case activation frequencies. At run-time, this pessimism is reduced by the online algorithm which uses the exact knowledge about sporadic arrivals to reclaim resources and improve response times and acceptance of firm aperiodic tasks.

이동 시퀀스의 빈발도를 이용한 최적 이동 패턴 탐사 기법

기존의 패턴 탐사 기법들은 제한된 시간 및 공간영역에서 발생하는 다양한 이동 패턴들 중 단순히 사용자 요구에 적합할 것으로 추정되는 불특정한 빈발 이동 패턴만을 탐사하기 때문에 특정지점들 간의 최적 이동 경로나 정해진 시간 내의 스케줄링 경로 탐색과 같은 복합적인 시간 및 공간 제약 조건을 갖는 최적 이동 패턴을 탐사하는 문제에는 적용하기 어렵다. 이에 본 논문에서는 방대한 이동 객체의 이력 데이터 집합으로부터 복합적인 시간 및 공간 제약을 갖는 최적 이동 패턴을 탐사하는 문제를 보이고, 적용 가능한 위치 기반 서비스로서 최적 이동 경로에 해당하는 패턴을 탐색하기 위한 새로운 패턴 탐사 기법인 STOMP-F를 제안한다. 제안된 기법은 특정한 지점들 사이를 이동한 객체의 패턴들 중 객체가 가장 빈번하게 이동한 경로를 탐색하여 최적 경로로 결정하는 패턴 빈발도를 이용한 탐색 방법으로, 최적 이동 패턴 탐사 과정의 이동 시퀀스 생성 단계에서 객체의 위치 값과 공간영역 간의 위상 관계를 고려하여 이동 객체의 위치 속성에 대한 최하위 수준에서의 공간 일반화를 통해 보다 효율적으로 패턴 탐사를 수행할 수 있다. 제안 방법을 Dijkstra 알고리즘과 <TEX>$A^*$</TEX> 알고리즘을 대상으로 실험 평가한 결과 <TEX>$A^*$</TEX> 알고리즘의 휴리스틱 가중치에 따라 차이는 있으나 연산 처리 시간을 기준으로 타 알고리즘들 보다 효과적임을 알 수 있다. Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and <TEX>$A^*$</TEX> algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on <TEX>$A^*$</TEX> algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

Perceived knowledge among patients cared for by nephrologists about chronic kidney disease and end-stage renal disease therapies

The need to educate patients in order to enable them to participate in making appropriate choices for all therapeutic options in end stage renal disease would seem obvious yet there are many barriers to providing such information. We measured 'perceived knowledge' of the therapeutic options for end stage renal disease in a cohort of patients with chronic kidney disease in established treatment programs. A self administered questionnaire was given to 676 patients with stage 3-5 chronic kidney disease as part of the CRIOS study designed to identify trends in practice patterns and outcomes over a 4 year period. The median patient age was 66, about three-fourths were Caucasian and almost half were diabetic. When patients were asked to rate their level of knowledge, about one-third reported limited or no understanding of their chronic kidney disease and no awareness regarding their treatment options. A significant and substantial number of patients indicated they had no familiarity with transplant, hemodialysis, and continuous ambulatory or automated peritoneal dialysis. Perceived knowledge improved with the progression of kidney disease and frequency of nephrology visits; however, only about half of patients with 4 or more nephrology appointments in the prior year reported knowing of hemodialysis, continuous ambulatory peritoneal dialysis or transplant. Age, gender and disease had no impact on levels of patient knowledge, but African-Americans reported having significantly less understanding than Asians or Caucasians. These findings suggest that the lack of perception concerning the treatment options chronic kidney and end stage renal disease reflects, in part, problems with the education of patients by nephrologists and not a lack of referral of these patients to nephrologists for care. The discrepancy of perceived knowledge between African-Americans and other races needs special attention.

On the Design and Implementation of a Shared Memory Dispatcher for Partially Clairvoyant Schedulers

With the onset of distributed computing in hard real-time applications, the problem of assigning to, scheduling in, and executing jobs on processors, has received a lot of attention. Usually, real-time systems are embedded in closed loop reactive environments with uncertain behaviors and such systems take varying times to respond to such stimuli. One of the fundamental features of such systems is the presence of complex timing constraints between pairs of jobs. A secondary feature is the non-constant nature of the execution times of jobs. Real-time operating systems such as MARUTI can measure the interval within which the execution time varies (Mosse et al. In: Second IEEE Workshop on Experimental Distributed System, pp. 29-34, IEEE, 1990; Levi et al. 1989, ACM Special Interest Group Operat Syst 23(3):90-106). Partially clairvoyant scheduling was introduced in (Saksena, Parametric Scheduling in Hard Real-Time Systems. PhD thesis, University of Maryland, College Park, June 1994) to schedule jobs with varying execution times and non-trivial timing constraints. The schedulability of the job set is determined offline and a set of dispatch functions are produced from the given set of constraints if the job set is schedulable. The dispatch functions bind the start time of a job J to an interval that depends on the start and execution times of jobs sequenced before J. The online dispatcher of the system reads these dispatch functions and computes the interval within which a job can start without violating the constraints imposed on the system. In certain situations, the dispatcher fails to dispatch a job as the time to compute the dispatch functions associated with a job is greater than the interval within which the job needs to be dispatched. This phenomenon is called Loss of Dispatchability (Subramani, Duality in the Parametric Polytope and its Applications to a Scheduling Problem. PhD thesis, University of Maryland, College Park, August 2000). In this paper, we propose and implement a partially clairvoyant dispatching algorithm on a shared memory cluster with Concurrent Read Exclusive Write (CREW) architecture and contrast it with the sequential approach. For a preset number of processors, our approach has O(1) dispatch complexity while using a total of O(n2) space, while the sequential approach requires Ω(n) time. The detailed implementation profile obtained clearly demonstrates the superiority of the multiprocessor approach to dispatching. We also address the issue of scalability of the dispatcher for increasing number of processors and show that job sets of different sizes require different number of processors. Finally, we demonstrate the effect of execution time on the dispatchability of schedules.

High Rate Data Synchronization in GALS SoCs

Globally asynchronous, locally synchronous (GALS) systems-on-chip (SoCs) may be prone to synchronization failures if the delay of their locally-generated clock tree is not considered. This paper presents an in-depth analysis of the problem and proposes a novel solution. The problem is analyzed considering the magnitude of clock tree delays, the cycle times of the GALS module, and the complexity of the asynchronous interface controllers using a timed signal transition graph (STG) approach. In some cases, the problem can be solved by extracting all the delays and verifying whether the system is susceptible to metastability. In other cases, when high data bandwidth is not required, matched-delay asynchronous ports may be employed. A novel architecture for synchronizing inter-modular communications in GALS, based on locally delayed latching (LDL), is described. LDL synchronization does not require pausable clocking, is insensitive to clock tree delays, and supports high data rates. It replaces complex global timing constraints with simpler localized ones. Three different LDL ports are presented. The risk of metastability in the synchronizer is analyzed in a technology-independent manner

Invisible Social Infrastructures to Facilitate Time-pressed Distributed Organizing

How do complex societal demands and time constraints posed by distributed temporary organizing affect organizational communication? Extending Bowker and Star’s (2002) work on infrastructures, we introduce two context-specific ‘invisible’, social infrastructures: organizational and relational. We empirically assess their role in an international, multi-site ERP-software implementation. We investigated how these infrastructures shaped organizational activities, aligned discourses, created order, and prevented divergent behaviours. We found that mutually interdependent organizational and relational infrastructures strengthened social relationships and saved time by facilitating non-routine collaboration and organizational communication under geographic and temporal constraints. We argue that the conceptualization of (infra)structural and process dynamics will help researchers and practitioners understand and handle organizational communication in distributed temporary organizations.

Periodic Linear Programming with applications to real-time scheduling

In this paper we introduce a new mathematical modelling technique called Periodic Linear Programming; the periodic properties of Periodic Linear Programs (PLPs) permit the specification of inter-period constraints in embedded systems, in a straightforward and natural manner. We analyse PLPs in which the relationship between program variables is restricted to the class of difference constraints. Our analysis establishes that such PLPs can be reduced to simple linear programs, and hence decided in polynomial time. The class of difference constraints is extremely important from the perspective of embedded systems design, in that it permits the specification of complex timing constraints in real-time specification languages. A PLP can be thought of as a finite-description tool that represents infinite-state systems; although we use this tool purely for the purpose of modelling real-time scheduling problems, PLPs also find applications in other areas, such as concurrency design. In studying this programming paradigm, we develop novel techniques that, to the best of our knowledge, are not part of the literature. We build on the PLP structure to introduce a generalisation called Periodic Quantified Linear Programming; this programming paradigm permits the specification and analysis of uncertainty in the parameters of a PLP. Consequently, a Periodic Quantified Linear Program (PQLP) is the natural modelling tool to capture the requirements of periodic, embedded systems that are characterised by uncertainty in the execution times of processes, periodicity and relative timing constraints. In this paper, we use the PQLP structure to model and solve the periodic version of the zero-clairvoyant scheduling problem. Modelling uncertainty in the problem description is a typical technique used to incorporate a measure of fault-tolerance in the specification.

A Comprehensive Framework for Specifying Clairvoyance, Constraints and Periodicity in Real-Time Scheduling

There are two principal modes in which real-time scheduling differs from scheduling in conventional models: (i) execution time variability and (ii) existence of complex timing constraints between jobs. There is a third issue that indirectly depends upon the non-constant nature of execution times, namely the degree of clairvoyance permitted by the application. Finally, there also exist the issues of periodicity and the presence of inter-period constraints; these issues need to be explicitly accounted for, in an analytic fashion. In traditional scheduling models, it is usual to assume fixed values for job execution times. This essentially means that a job will take exactly the same time to execute, in every instance of its invocation. From a real-time perspective, this assumption is both unrealistic and dangerous, in that it may lead to constraint violation at runtime. With a view towards enhancing the fault-tolerance of the system, the execution times of jobs are modeled through convex sets; doing so permits the capturing of situations that cannot be modeled otherwise. For instance, by monitoring the execution time of a job, over a number of independent runs, one can identify its safety interval with a high degree of confidence. The product of the safety intervals, over all the jobs in the system, forms a convex set. The second feature unique to real-time systems, is the presence of temporal relationships that constrain job execution. For instance, consider the pair of requirements: (i) job J1 should conclude 10 units before job J2 commences and (ii) job J2 commences within 12 units of job J1 concluding. These requirements cannot be modeled through precedence graphs, which by definition, are directed and acyclic. However, they can be easily modeled through simple, difference constraints. In hard real-time scheduling, it is important to guarantee the schedulability of the system a priori, since the violation of a constraint could lead to catastrophic consequences. However, in the presence of variable execution times and complex timing constraints, the definition of schedulablity is not obvious. In this context, it is necessary to mention the dispatchability problem; the dispatcher is concerned with strategies to assign jobs on the time line, once the scheduler has declared the job-set to be feasible. The nature of the schedulability query is determined by whether or not the dispatcher can use the execution times of jobs in the computation of the schedule. Observe that there are three possibilities, namely (i) the dispatcher never knows the execution time of a job, (ii) the dispatcher knows the execution time of a job after it has finished execution and (iii) the dispatcher knows the execution time of a job, even before it has commenced execution. Thus, depending upon the nature of the application involved, there are three different schedulability specifications, namely zero-clairvoyant (static), partially clairvoyant (parametric) and totally clairvoyant (co-static). Each specification affords a different level of flexibility and has different dispatching concerns. This paper presents the framework, called the E-T-C scheduling model, which explicitly accommodates execution time variability, complex timing constraints and different schedulability queries. This enables the specification of a wide range of real-time scheduling problems that occur in practical settings. This paper also discusses the relationships between flexibility and complexity in the proposed model. Each aspect of the model is motivated through examples from real-world design. The E-T-C model is itself a single period framework, in that all constraints are necessarily intra-period. This framework is extended to formulate the E-T-C-P scheduling framework, which explicitly accounts for inter-period constraints. The strengths of these two frameworks lie in their abilities to abstract the essentials of a number of real-time situations. In addition, decoupling the constraint system from the schedulability query greatly enhances the flexibility of the specification process. Although the primary purpose of this paper is to elaborate on scheduling problems only, it also discusses the problems of real-time dispatchability and optimization in clairvoyant scheduling.

Simulation of Simultaneous Events in Regular Expressions for Run-Time Verification

When specifying system requirements, we want a language that can express the requirements in the simplest and most intuitive form. Although the MaC system provides an expressive language, called MEDL, it is generally awkward to express certain features like temporal ordering of complex events, timing constraints, and frequencies of events which are inherent in safety properties. MEDL-RE extends the MEDL language to include regular expressions to easily specify timing dependencies and timing constraints. Due to simultaneous events generated by the MaC system, monitoring regular expressions by simulating DFAs would result in a potential problem. The DFA simulations would involve concurrent multi-path simulations and result in exponential running time. To handle simultaneous events inexpensively, we generate a dependency graph to identify possible simultaneous events. Further, we augment the original DFAs with alternative transitions, which will substitute for multi-path simulations.