Dynamic Priority Assignment Research Articles

A Sensitivity-based Heuristic for Vehicle Priority Assignment at Intersections

This paper presents a sensitivity-based heuristic to address the dynamic priority assignment problem of connected and autonomous vehicle (CAV) and human-driven vehicle (HDV) at traffic intersections. We exploit sensitivity analysis tools to approximatively predict the CAV's performance violation as a function of the HDV states. Such predictions are then used to decide on a crossing order that preserves optimality and feasibility despite the behavior of the HDV. The proposed algorithm is compared with the baseline first-come, first-serve (FCFS) and mixed-integer nonlinear programming (MINLP) approaches. In the closed-loop simulation, we show that the heuristic is computationally much faster than MINLP and able to retain a close-to-optimal solution, which is far better than FCFS.

Compositional virtual timelines: verifying dynamic-priority partitions with algorithmic temporal isolation

Real-time systems power safety-critical applications that require strong isolation among each other. Such isolation needs to be enforced at two orthogonal levels. On the micro-architectural level, this mainly involves avoiding interference through micro-architectural states, such as cache lines. On the algorithmic level, this is usually achieved by adopting real-time partitions to reserve resources for each application. Implementations of such systems are often complex and require formal verification to guarantee proper isolation. In this paper, we focus on algorithmic isolation, which is mainly related to scheduling-induced interferences. We address earliest-deadline-first (EDF) partitions to achieve compositionality and utilization, while imposing constraints on tasks' periods and enforcing budgets on these periodic partitions to ensure isolation between each other. The formal verification of such a real-time OS kernel is challenging due to the inherent complexity of the dynamic priority assignment on the partition level. We tackle this problem by adopting a dynamically constructed abstraction to lift the reasoning of a concrete scheduler into an abstract domain. Using this framework, we verify a real-time operating system kernel with budget-enforcing EDF partitions and prove that it indeed ensures isolation between partitions. All the proofs are mechanized in Coq.

Dynamic Priority Assignment for Aperiodic Tasks in Limited Preemptive Scheduling

Internet of Things (IoT) systems and other real-time systems are required to handle both periodic and aperiodic incoming tasks. In a situation in which periodic and aperiodic tasks are mixed into task scheduling using a fixed priority system, the schedulability of periodic tasks could be reduced due to interruptions by aperiodic tasks. This study analyzes the effects of aperiodic tasks on non-preemptive scheduling and limited preemptive scheduling methods, such as fixed preemption points, preemption threshold scheduling, and deferred preemptive scheduling, which have recently been proposed by complementarily combining fully preemptive and non-preemptive scheduling. This study then proposes an algorithm for assigning an appropriate priority to an aperiodic task by analyzing the worst-case response time of all tasks to enhance schedulability of both periodic and aperiodic tasks.

Inventory Management under Storage and Order Restrictions

Inventory management in most practical settings faces challenges due to various restrictions on storage and replenishment of products. These restrictions may be posed by budget availability, different production/supply schedules for different products, and limited storage space shared by a number of products—very common in retail, food, and the pharmaceutical industry. Motivated by this, we investigate in this study how simultaneous restrictions on shared storage capacity and product‐specific order capacities impact optimal replenishments in a multi‐product system. We formulate the inventory replenishment problem as a multi‐period stochastic dynamic program, where products face stochastic demand with general distributions and excess demand is lost or fulfilled by emergency orders. We first fully characterize the optimal replenishment policy for two‐product systems, and provide a methodology to compute optimal replenishment quantities, which can be described as a dynamic priority‐based replenishment rule. Our results show that for each product, the optimal replenishment priority as well as quantity depends on the inventory levels of both products and all available capacities. More interestingly, the results show that capacity restrictions can flip the stocking priorities of products. Based on the optimal policy for two‐product systems, we develop a heuristic for multi‐product systems whose complexity scales linearly with the number of products. Under moderate storage capacities, our heuristic significantly outperforms the naive heuristics that ignore dynamic priority assignment, and closely captures the benefits of the optimal policy for systems with large number of products.

Dynamic Priority Assignment Algorithm Based on Multi-Elements in Vehicular Ad-Hoc Network

In Vehicular Ad-hoc Network, different messages have different degrees of urgency and have specific requirements on delay and reliability. In this paper, we propose a dynamic priority assignment algorithm based on multi-elements to adapt to the dynamic changes of vehicles and transmit messages in an accurately and orderly manner. A priority is assigned to each message based on static and dynamic factors and size of the message. After a detailed analysis of the allocated priorities, the results show that the algorithm we conceived can quickly and accurately allocate the priority for different kinds of messages based on the characteristics and requirements of them.

Feasibility Analysis of Non-Preemptive Periodic Systems from Infeasibility Perspective

Due to simple implementation, non-preemptive scheduling has the advantage over preemptive counterpart when it comes to deployment of real-time systems. Accordingly, many feasibility techniques have been established to answer schedulability of the task set for non-preemptive case. The time complexity of exact condition for non-preemptive under dynamic priority assignment is of pseud-polynomial nature. Recently, efforts are made to decrease the computation cost of existing exact solutions. However, the time complexity class remains the same. In such systems, feasibility is tested by starting with highest priority task and test continues in that order until the last task is analyzed in the set. Normally, higher priority tasks rarely miss the deadline and hence, when a system is determined infeasible, it is mainly because of the low priority tasks as these tasks are assigned low priorities and can claim CPU time only when there is no pending higher priority task in the queue. In this study, we propose a mechanism that reduces the computation cost of feasibility for non-preemptive earliest deadline first scheduling algorithm by testing the infeasibility of the system in reverse priority order. In worst case, the proposed technique is not inferior for a system with low utilization that scans a task set from feasibility perspectives. On the other hand, our test exhibits better performance when the system infeasibility is tested for the system demanding higher CPU utilization. Our experimental results show that the overall computation cost, especially for the larger task sets with higher CPU demands, is significantly reduced with the proposed solution by evaluating a system from infeasibility perspective.

An SDN-based framework for improving the performance of underprovisioned IP Video Surveillance networks

In the coming years, the dominance of video in global Internet traffic is expected to intensify due to the ongoing migration from analog CCTV to IP-based surveillance systems. As opposed to analog, IP cameras can be integrated into pre-existing computer networks and thus, are more cost-effective. However, since video is bandwidth-intensive, contention among the streams from multiple IP cameras and traffic from other applications results to a severely degraded network performance. Software-Defined Networking (SDN) is a relatively new paradigm which aims to build dynamically configurable networks. The decoupling of forwarding and control functions in SDN architecture enables a centralized controller to create a map of the network topology by utilizing the information collected from the switches. This paper proposes an SDN-based framework to enhance the performance of IP Video Surveillance (IPVS) systems deployed over underprovisioned networks. As a means of allowing the controller to infer a stream's QoS metrics and to execute bitrate adjustment or rerouting as necessary, two video quality indicators were also formulated by utilizing the statistics messages available in OpenFlow, a widely recognized protocol in SDN. Moreover, our design is built on the idea that the video streams in surveillance systems offer different utilities depending on the captured event. Experimental results show that employing the proposed framework improved the video streams' overall packet loss, latency, jitter and throughput by 88%, 36%, 11% and 5% respectively. The comparison of the video streams' QoS metrics also suggests that the framework is capable of prioritizing the reception of selected streams. Furthermore, we also demonstrate that the proposed framework can be easily extended to handle the case of an IPVS system wherein the streams are subjected to dynamic priority assignment.

Embedded Energy and Power Management System

In this paper, Embedded Energy and Power Management System (EEPMS) to manage power consumption, automatic meter reading in home area is described. The EEPMS is a single phase digital kWh power meter with embedded Zigbee modem which utilize the Wireless sensor network to send its power usage reading using information back to the energy provider wirelessly. At the power provider side they have the control to change priority of the devices when power distributed in low range. The concept of dynamic assignment of priorities to interrupts is discussed which reduces the time delay for a lower priority task which under some circumstances becomes a higher priority task. Slit of interrupt timings is also observed which can be used to improve the performance.

English

This paper describes the Intelligent Energy and Power Management System (IEPMS) to manage power distribution and power consumption in home space. The thought of dynamic assignment of priorities for all the patron is delineate during this paper. Slicing of interrupt timings conjointly mentioned which might be wont to improve the performance and additionally we are able to show the Units of Power consumed and quantity on alphanumeric display and also we are able to send the quantity to the patron mobile mistreatment GSM module .The total system is dominant from home server and therefore the system has developed through ARM processor and wireless communication networks. in keeping with the management commands of the house server , the IEPM system may be controlled mechanically by dynamic the masses. The un- interrupted power provide of the patron is achieved through the projected design and it provides a lot of economical energy management.

Cache-Related Preemption Delay Analysis for Multilevel Noninclusive Caches

With the rapid growth of complex hardware features, timing analysis has become an increasingly difficult problem. The key to solving this problem lies in the precise and scalable modeling of performance-enhancing processor features (e.g., cache). Moreover, real-time systems are often multitasking and use preemptive scheduling, with fixed or dynamic priority assignment. For such systems, cache related preemption delay (CRPD) may increase the execution time of a task. Therefore, CRPD may affect the overall schedulability analysis. Existing works propose to bound the value of CRPD in a single-level cache. In this article, we propose a CRPD analysis framework that can be used for a two-level, noninclusive cache hierarchy. In addition, our proposed framework is also applicable in the presence of shared caches. We first show that CRPD analysis faces several new challenges in the presence of a multilevel, noninclusive cache hierarchy. Our proposed framework overcomes all such challenges and we can formally prove the correctness of our framework. We have performed experiments with several subject programs, including an unmanned aerial vehicle (UAV) controller and an in-situ space debris monitoring instrument. Our experimental results suggest that we can provide sound and precise CRPD estimates using our framework.

A REAL-TIME TASKS SCHEDULING METHOD BASED ON DYNAMIC PRIORITY

Recently, real-time system was widely applied to diverse environments. In order to meet the demands of those applications, many scheduling strategies were presented to achieve either maximal benefit or minimum miss deadline ratio. However, very little attention has been devoted to simultaneously achieve the two objectives. This paper proposes a dynamic priority assignment (DPA) strategy by analyzing the remainder value density and urgency of task, in which, two parameters p and q are used to adjust the weights of remainder value density and urgency on task's priority. Based on DPA strategy, the condition that can avoid system thrashing is discussed, and a dynamic real-time task scheduling (DRTS) algorithm is also proposed. Finally, experimental results show that the proposed method can improve the integrated performance of real-time system compared with analogous algorithms.

On the Stability of Prioritized Error-Based Scheduling for Resource-Constrained Networked Control Systems

The efficient usage of scarce communication resources is a vital necessity in networked control systems. This paper introduces a novel stochastic scheduling scheme for networked control systems with shared access through dynamic priority assignment. The overall system is assumed to consist of multiple control loops closed over a common communication network. A p-powered prioritized error-based (PEB) scheduler decides which transmission requests have the priority for channel access, and subsequently which controllers are updated with the actual states. According to the protocol, the likelihood to allocate the resource to a subsystem increases with a growing p-powered norm of its network-induced error. We show, under very mild assumptions, that the described system scheduled by the PEB protocol is stochastically stable. Moreover, numerical simulations demonstrate a significant reduction of the network-induced error variance in comparison to the other scheduling protocols such as CSMA or round robin.

Heterogeneous Dynamic Priority Scheduling in Time Critical Applications

In the unlicensed band, the notion of primary user and secondary user (To implement cognitive radio) is not explicit. By dynamic priority assignment the authors propose to implement cognitive radio in the unlicensed band. In time critical events, the data which is most important, has to be given the time slots. Wireless Sensor nodes in the authors’ case are considered to be mobile, and hence make it difficult to prioritize one over another. A node may be out of the reach of the cluster head or base station by the time it is allotted a time slot and hence mobility is a constraint. With the data changing dynamically and factors such as energy and mobility, which are major constraints, assigning priority to the nodes becomes difficult. In this paper, the authors have discussed about how Wireless Sensor Networks are able to allocate priorities to nodes in the unlicensed band with multiple parameters being posed. They have done simulations on NS-2 and have shown the implementation results.

Guaranteed Dynamic Priority Assignment Schemes for Real-Time Tasks with (m, k)-Firm Deadlines

We present guaranteed dynamic priority assignment schemes for multiple real-time tasks subject to (m, k)-firm deadlines. The proposed schemes have two scheduling objectives: providing a bounded probability of missing (m, k)-firm constraints and maximizing the probability of deadline satisfactions. The second scheduling objective is especially necessary in order to provide the best quality of service as well as to satisfy the minimum requirements expressed by (m, k)-firm deadlines. We analytically establish that the proposed schemes provide a guarantee on the bounded probability of missing (m, k)-firm constraints. Experimental studies validate our analytical results and confirm the effectiveness and superiority of the proposed schemes with regard to their scheduling objectives.

Hierarchical Real-Time MAC Protocol for (m,k)-firm Stream in Wireless Sensor Networks

In wireless sensor networks (WSNs), both efficient energy management and Quality of Service (QoS) are important issues for some applications. For creating robust networks, real-time services are usually employed to satisfy the QoS requirements. In this paper, we proposed a hierarchical real-time MAC (medium access control) protocol for (m,k)-firm constraint in wireless sensor networks shortly called HRTS-MAC. The proposed HRTS-MAC protocol is based on a dynamic priority assignment by (m,k)-firm constraint. In a tree structure topology, the scheduling algorithm assigns uniform transmitting opportunities to each node. The paper also provides experimental results and comparison of the proposed protocol with E_DBP scheduling algorithm.

Guaranteed dynamic priority assignment scheme for streams with ( m , k )-firm deadlines

A guaranteed dynamic priority assignment scheme for multiple real-time streams with (m, k)-firm deadlines is presented. Analytical and experimental studies show that the proposed scheme provides assurance of timeliness performance and relatively high quality of service compared to existing schemes.

Dynamic Priority Assignment and Conflict Resolution in Real Time Databases

Dynamic Priority Assignment and Conflict Resolution in Real Time Databases

A Scheduling Algorithm with Dynamic Priority Assignment for WCDMA Systems

In third generation WCDMA systems, shared channels allow many users to jointly utilize a single orthogonal variable spreading factor (OVSF) code. In this paper, we propose a scheduling algorithm with dynamic priority assignment (DPA) which is designed for the downlink-shared channel (DSCH) of 3G WCDMA systems and operates within a cross layer framework. The DPA scheduler has low computational complexity and is able to provide QoS differentiation among traffic flows based on their delay sensitivity. Through the cross layer framework, DPA takes into account the variations of the wireless channel, and exploits processing gain to improve transmission quality and enable service provisioning when possible. Additionally, by providing a guaranteed rate per traffic flow at each scheduling period, DPA can offer a deterministic delay bound to each connection when transmissions are reliable. Stochastic delay guarantees under transmission power limitations are also provided when the traffic flows are identical. Simulation results show that DPA outperforms feasible earliest due date (FEDD), a variation of EDD for wireless environments.

Effective Scheduling Algorithms for I/O Blocking with a Multi-Frame Task Model

A task that suspends itself to wait for an I/O completion or to wait tor an event from another node in distributed environments is called an I/O blocking task. Conventional hard real-time scheduling theories use framework of rate monotonic analysis (RMA) to schedule such I/O blocking tasks. However, most of them are pessimistic. In this paper, we propose effective algorithms that can schedule a task set which has I/O blocking tasks under dynamic priority assignment. We present a new critical instant theorem for the multi-frame task set under dynamic priority assignment. The schedulability is analyzed under the new critical instant theorem. For the schedulability analysis, this paper presents saturation summation which is used to calculate the maximum interference function (MIF). With saturation summation, the schedulability of a task set having I/O blocking tasks can be analyzed more accurately. We propose an algorithm which is called Frame Laxity Monotonic Scheduling (FLMS). A genetic algorithm (GA) is also applied. From our experiments, we can conclude that FLMS can significantly reduce the calculation time, and GA can improve task schedulability ratio more than is possible with FLMS.

Supervisory control of mobile sensor networks: math formulation, simulation, and implementation

This paper uses a novel discrete-event controller (DEC) for the coordination of cooperating heterogeneous wireless sensor networks (WSNs) containing both unattended ground sensors (UGSs) and mobile sensor robots. The DEC sequences the most suitable tasks for each agent and assigns sensor resources according to the current perception of the environment. A matrix formulation makes this DEC particularly useful for WSN, where missions change and sensor agents may be added or may fail. WSN have peculiarities that complicate their supervisory control. Therefore, this paper introduces several new tools for DEC design and operation, including methods for generating the required supervisory matrices based on mission planning, methods for modifying the matrices in the event of failed nodes, or nodes entering the network, and a novel dynamic priority assignment weighting approach for selecting the most appropriate and useful sensors for a given mission task. The resulting DEC represents a complete dynamical description of the WSN system, which allows a fast programming of deployable WSN, a computer simulation analysis, and an efficient implementation. The DEC is actually implemented on an experimental wireless-sensor-network prototyping system. Both simulation and experimental results are presented to show the effectiveness and versatility of the developed control architecture.