A scheduling algorithm for multi-processor real-time systems with relative timing constraints and resource restrictions

Allocation problem has always been one of the fundamental issues of building the applications in real-time computing systems. For real-time applications, the allocation problem should directly address the issues of task and communication scheduling. In this context, the allocation of tasks has to fully utilize the available processors and the scheduling of tasks has to meet the specified timing constraints. Clearly, the execution of tasks under the allocation and schedule has to satisfy the precedence, resources, and other synchronization constraints among them. Recently, the timing requirements of the real-time systems emerge that the relative timing constraints are imposed on the consecutive executions of each task and the inter-task temporal relationships are specified across task periods. In this paper we consider the allocation and scheduling problem of the periodic tasks with such timing requirements. Given a set of periodic tasks, we consider the least common multiple (LCM) of the task periods. Each task is extended to several instances within the LCM. The scheduling window for each task instance is derived to satisfy the timing constraints. We develop a simulated annealing algorithm as the overall control algorithm. An example problem of the sanitized version of the Boeing 777 Aircraft Information Management System is solved by the algorithm. Experimental results show that the algorithm solves the problem in a reasonable time complexity.

In this paper, a scheduling algorithm for multi-processor real-time systems with relative timing constraints and resource constraints is proposed. The proposed algorithm has the capability to satisfy any timing constraints expressed as inequalities in two variables and a constant. This implies that scheduling problems imposed on various timing constraints such as end-to-end timing constraints and/or relative timing constraints, which are known to be difficult to handle, can be solved. Since the problem addressed in this paper is a generalization of some NP-complete problems, it is difficult to solve efficiently. Thus, the goal of the authors is to derive a heuristic algorithm which performs well on many instances of the problem, although in some cases it will take enormous space and time. Experimental results show that the proposed algorithm achieves a success rate of 80% in 3 minutes of computation time. © 1997 Scripta Technica, Inc. Syst Comp Jpn, 28(3): 52–61, 1997

Traditional scheduling models assume that the execution time of a job in a periodic job-set is constant in every instance of its execution. This assumption does not hold in real-time systems wherein job execution time is known to vary. A second feature of traditional models is their lack of expressiveness, in that constraints more complex than precedence constraints (for instance, relative timing constraints) cannot be modeled. Thirdly, the schedulability of a real-time system depends upon the degree of clairvoyance afforded to the dispatcher. In this paper, we shall discuss Totally Clairvoyant Scheduling, as modeled within the E-T-C scheduling framework [Sub05]. We show that this instantiation of the scheduling framework captures the central issues in a real-time flow-shop scheduling problem and devise a polynomial time sequential algorithm for the same. The design of the polynomial time algorithm involves the development of a new technique, which we term Mutable Dynamic Programming. We expect that this technique will find applications in other areas of system design, such as Validation and Software Verification.

We present a tool that performs real time analysis and priority assignment for software tasks in a mixed hardware software system with a custom run time scheduler. The tasks in hardware and software have precedence constraints, resource constraints, relative timing constraints, and a rate constraint. A dynamic programming formulation assigns the static priorities such that a hard real time rate constraint can be predictably met. We describe the task control/data flow extraction, runtime scheduler implementation, real time analysis and priority scheduler template. We show how our approach fits into an overall tool flow and target architecture. Finally, we conclude with a sample application of the system to a design example.

This paper discusses a class of mission planning problems in which mission objectives and relative timing constraints are specified using the linear temporal logic language LTL-x. Among all mission plans that satisfy the LTL-x specifications, it is desired to find those minimizing a given cost functional. We show that such an optimization problem can be formulated as a mixed-integer linear program, and present an algorithm for this purpose. This algorithm mainly relies on a novel systematic procedure which converts a given LTL-x formula into a set of mixed-integer linear constraints. The approach presented here can be used for multiple-UAV mission planning purposes, allowing the operator to specify complex mission objectives in LTL-x in a very natural manner; the proposed algorithm constructs the optimal mission plan satisfying the given LTL-x specification. Examples for practical problem sizes are presented and discussed in the paper.

We propose a novel technique for modeling and verifying timed circuits based on the notion of generalized relative timing. Generalized relative timing constraints can express not just a relative ordering between events, but also some forms of metric timing constraints. Circuits modeled using generalized relative timing constraints are formally encoded as timed automata. Novel fully symbolic verification algorithms for timed automata are then used to either verify a temporal logic property or to check conformance against an untimed specification. The combination of our new modeling technique with fully symbolic verification methods enables us to verify larger circuits than has been possible with other approaches. We present case studies to demonstrate our approach, including a self-timed circuit used in the integer unit of the Intel/sup /spl reg// Pentium/sup /spl reg//4 processor.

Code generation methods for DSP applications are hampered by the combination of tight timing constraints imposed by the performance requirements of DSP algorithms, and resource constraints imposed by a hardware architecture. In this paper, we present a method for register binding and instruction scheduling based on the exploitation and analysis of resource- and timing constraints. The analysis identifies sequencing constraints between operations additional to the precedence constraints. Without the explicit modeling of these sequencing constraints, a scheduler is often not capable of finding a solution that satisfies the timing, resource and register constraints. The presented approach results in an efficient method of obtaining high quality instruction schedules with low register requirements.

A self-timed microarchitecture called KeyRing is presented, and a method for implementing KeyRing circuits compatible with a timing-driven electronic design automation (EDA) flow is discussed. The KeyRing microarchitecture is derived from the AnARM, a low-power self-timed ARM processor based on ad hoc design principles. First, the unorthodox design style and circuit structures are revisited. A theoretical model that can support the design of generic circuits and the elaboration of EDA methods is then presented. Also addressed are the compatibility issues between KeyRing circuits and timing-driven EDA flows. The proposed method leverages relative timing constraints to translate the timing relations in a KeyRing circuit into a set of timing constraints that enable timing-driven synthesis and static timing analysis. Finally, two 32-bit RISC-V processors are presented; called KeyV and based on KeyRing microarchitectures, they are synthesized in a 65 nm technology using the proposed EDA flow. Postsynthesis results demonstrate the effectiveness of the design methodology and allow comparisons with a synchronous alternative called SynV. Performance and power consumption evaluations show that KeyV has a power efficiency that lies between SynV with clock-gating and SynV without clock-gating.

The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.

In this work, we address the multiple voltage scheduling problem to minimize power consumption under both timing and resource constraints. We develop a tabu search-based algorithm with a general vector representation of solution and an effective performance estimation. Moreover, our approach tends to solve a series of scheduling problems with respect to multiple voltage designs. Specifically, our method represents each solution by a vector of operation types and solves the scheduling subproblem corresponding to a certain solution vector. To get feasible solutions of the subproblem, a two-stage method is presented by first adopting the PLNSM algorithm [3] to generate delay assignment satisfying the timing constraint and then performing delay adjustment iteratively until the resource constraints are met. A heuristic estimation is introduced to predict the total power and resource usage of the neighborhood. Our proposed method achieves near-optimal solutions with only an average power increase of 0.84% compared with ILP for small-size designs, and a 24.1% reduction over a previous tabu search-based algorithm [7] for a set of benchmarks.

The power consumption of modern high-performance processors is becoming a major concern because it leads to increased heat dissipation and decreased reliability. While many techniques have been proposed to reduce power consumption for uni-processors, there has been considerably less work on multi-processor systems. In this paper we focus on power-aware scheduling for multi-processor real-time systems. Based on the idea of slack sharing among processors, we propose two novel scheduling algorithms for task sets with and without precedence constraints. These scheduling techniques reclaim the time unused by a task to reduce the execution speed of future tasks, and thus reduce the total energy consumption of the system. Simulation results indicate that our algorithms achieve up to 60% energy savings on multi-processor systems with variable voltage processors.

The asymptotic limit theorems of information theory, and related formalism from punctuated equilibrium evolutionary theory, allow exploration of the dynamics of real-time, multi-component, hierarchical, cognitive enterprises – institutional, machine, or man/machine hybrid – under constraints of time, material resources, internal communication, situational awareness, and adversarial intent. Surprisingly direct argument shows such enterprises can almost always be confounded by an opponent with comparatively limited resources, provided it has sufficient situational understanding, adaptive capacity, and resolve. Consequently, there can be no free lunch for AI combat systems, driverless cars on intelligent roads, or other such entities, tasked with the control of real-time critical processes. Intelligent adversaries, resource limitations, “design flaws”, inattentional blindness, command stupidity, coevolutionary dynamics, and the like confront all real-time, multi-component cognitive processes. AI systems will not be spared simply by being labelled “high tech” in marketing campaigns.

Lean strategies have been developed to eliminate or reduce waste and thus improve operational efficiency in a manufacturing environment. However, in practice, manufacturers encounter difficulties to select appropriate lean strategies within their resource constraints and to quantitatively evaluate the perceived value of manufacturing waste reduction. This paper presents a methodology developed to quantitatively evaluate the contribution of lean strategies selected to reduce manufacturing wastes within the manufacturers’ resource (time) constraints. A mathematical model has been developed for evaluating the perceived value of lean strategies to manufacturing waste reduction and a step-by-step methodology is provided for selecting appropriate lean strategies to improve the manufacturing performance within their resource constraints. A computer program is developed in MATLAB for finding the optimum solution. With the help of a case study, the proposed methodology and developed model has been validated. A ‘lean strategy-wastes’ correlation matrix has been proposed to establish the relationship between the manufacturing wastes and lean strategies. Using the correlation matrix and applying the proposed methodology and developed mathematical model, authors came out with optimised perceived value of reduction of a manufacturer's wastes by implementing appropriate lean strategies within a manufacturer's resources constraints. Results also demonstrate that the perceived value of reduction of manufacturing wastes can significantly be changed based on policies and product strategy taken by a manufacturer. The proposed methodology can also be used in dynamic situations by changing the input in the programme developed in MATLAB. By identifying appropriate lean strategies for specific manufacturing wastes, a manufacturer can better prioritise implementation efforts and resources to maximise the success of implementing lean strategies in their organisation.

Existing global microcode compaction algorithms have all been based on the assumption that the parallelism exploitation is constrained only by data dependency and resource limitation. However, the timing constraint also has great impact upon microcode compaction, thus it is highly necessary to study global microcode compaction under timing constraints. This paper conducts an analysis on the global timing problem, modifies the MO motion rules and presents an algorithm based on Trace Scheduling for global microcode compaction under timing constraints.

Nowadays, there appeared a tendency of expanding scope and scale of activities in the Arctic. This is accompanied by the increasing number of security threats and potential emergencies factors in the region. Arctic region development involves implementing the goals of reducing risks to global and regional security. An important role in implementing the strategy of creating a comprehensive security system in the Arctic belongs to efficient protection of population and territories, critically important and potentially dangerous facilities from natural and man-made emergencies. All dangerous factors characterizing emergencies require generation of a complex approach to ensure security in the Arctic territories, increasing the rescue services resource potential and providing monitoring the environment components state and pollution. This article identifies ways to solve the problem of strategic planning and deployment of forces and facilities when creating an integrated security system under resource constraints and dynamically changing situation in the Arctic. An organization chart of strategic resource planning for emergencies management in the Arctic was developed. A decision-making algorithm is proposed to elaborate forces and facilitate composition for the Arctic integrated search-and-rescue centers based on the developed chart. Achieving results in strategic planning involves elaboration of a program for developing forces and facilities of the integrated security system in the Arctic region on the basis of results obtained within the simulation process. The program should reflect step-by-step solution of strategic planning tasks in accordance with goals, resource and time constraints.