Priority Assignment Algorithm Research Articles

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.

Improved analysis and optimal priority assignment for communicating threads on uni-processor

This paper studies the system of communicating threads, which can be modeled as a set of task chains with each thread segment described as a task. Different from conventional approaches that assume the communication only happens at the end of thread executions, we assume a very generic scenario that the communication can happen at anytime during the execution. Under this scenario, the timing analysis and the priority assignment require carefully studying the possible execution dependencies among thread segments. We provide a more accurate timing analysis for the case that threads are communicating with message-based mechanisms. We prove that this analysis and the previously proposed one for synchronous communication are compliant with Audsley’s algorithm, which opens the possibility to use it to find an optimal priority assignment. We further propose two priority assignment algorithms that maximize the system’s robustness metrics. Finally, we evaluate our contributions in terms of both synthetic benchmarks and an industrial case study.

Priority assignment in hierarchically scheduled time-partitioned distributed real-time systems with multipath flows

The increasing complexity in the design of industrial embedded systems represents a challenge in the development of scheduling algorithms for such systems, which are essential to guarantee that they meet their deadlines even in the worst-case situation. In this work, we propose a new collection of non-iterative priority assignment algorithms for multipath flows within hierarchical schedulers based on state-of-the-art scheduling algorithms, which have been adapted to this complex system model. They are applied to an industrial railway use case that has motivated this work, and then their performance is evaluated in different general synthetic scenarios, with the aim of providing a view on how they behave in a wider range of system configurations.

Priority Assignment on Partitioned Multiprocessor Systems With Shared Resources

Driven by industry demand, there is an increasing need to develop real-time multiprocessor systems which contain shared resources. The Multiprocessor Stack Resource Policy (MSRP) and Multiprocessor resource sharing Protocol (MrsP) are two major protocols that manage access to shared resources. Both of them can be applied to Fixed-Priority Preemptive Scheduling (FPPS), which is enforced by most commercial real-time systems regulations, and which requires task priorities to be assigned before deployment. Along with MSRP and MrsP, there exist two forms of schedulability tests that bound the worst-case blocking time due to resource accesses: the traditional ones being more widely adopted and the more recently developed holistic ones which deliver tighter analysis. On uniprocessor systems, there are several well-established optimal priority assignment algorithms. Unfortunately, on multiprocessor systems with shared resources, the issue of priority assignment has not been adequately understood. In this article, we investigate three mainstream priority assignment algorithms–Deadline Monotonic Priority Ordering (DMPO), Audsley’s Optimal Priority Assignment (OPA), and Robust Priority Assignment (RPA), in the context of partitioned multiprocessor systems with shared resources. Our contributions are multifold: First, we prove that DMPO is optimal with the traditional schedulability tests. Second, two counter examples are given as evidence that DMPO is not optimal with the tighter holistic schedulability tests. Third, we then analyze the pessimism arising from the adoption of OPA and RPA with the holistic tests. Lastly, we propose a Slack-based Priority Ordering (SPO) algorithm that minimises such pessimism, and has polynomial time complexity. Comprehensive experiments show that SPO outperforms (i.e., results in a larger number of schedulable systems) DMPO, OPA, and RPA in general with the holistic schedulability tests, by up to 15 percent. With the theoretical contributions, this paper is a useful guide to priority assignment in real-time partitioned multiprocessor systems with shared resources.

Real-Time Scheduling for Mixed-Criticality Systems in the Automotive Industry

Currently, mixed-criticality systems (MCSs) are rapidly adopted by the automotive industry, with the shift in electricalelectronic architecture from federated to integrated design to reduce developmental costs, pull in the development schedule, and easy reconfiguration of the system with service-oriented architecture. Several studies have been based on Vestal’s original MCS model, in which the criticality modes are the same as criticality levels. However, the MCS model does not fit the automotive industry or the safety perspective. In this study, we identify the divergence of theory and automotive practice for real-time MCS. We also propose a generalized MCS model close to industry practice and a priority assignment algorithm along with schedulability analysis for both online and offline phases. Further, we present a practical example of memory partition and decomposition tasks based on AUTomotive Open System Architecture (AUTOSAR). The proposed design is currently being developed for battery management systems of electric and plug-in hybrid electric vehicles.

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.

Preemptive and Non Preemptive Switching Protocols for 5G Wireless Dynamic Network Architecture

This article focuses on 5G wireless dynamic network architecture and analyzes a novel paradigm for wireless network access, where certain classes of smart devices may act as an access point (AP) temporarily anytime while connected to the Internet. Due to the unpredictably available/unavailable time and uninformed disconnections of the APs, users need a seamless, efficient, and reliable schedule for switching between available APs. In this article, we model the available/unavailable behavior of each AP as a two-state Markov chain, and then propose two switching policies: push-based preemptive switching and pull-based non-preemptive switching. For each policy, the process of user switching among the APs or base station (BS) in its accessing list is modeled as a Markovian process. Approximated statistical models of the two switching policies are provided, and stationary distributions of these processes can be derived. In addition, we propose a dual-charging mechanism where the APs charge the users by signaling transmission and data transmission. Based on the derived stationary distributions and proposed dual-charging mechanism, we propose an optimal priority assignment algorithm to constitute the access lists for preemptive and non-preemptive switching. Simulation results confirm the accuracy of the statistical models and verify the performance of the preemptive and non-preemptive switching policies. In the low mobility scenarios, preemptive switching would be a better option because it has acceptable signaling overhead, brings higher capacity and utility for users, and brings higher profit for APs. In the high mobility scenarios, non-preemptive switching would be a better option because it incurs fewer switchings and higher profit for APs.

Home Energy Management System for a domestic load center using Artificial Neural Networks towards Energy Integration

This paper titled ‘Home Energy Management System (HEMS) for a domestic load center using Artificial Neural Networks towards Energy Integration’ focuses on implementing intelligent integration of Distribution Generation Systems (DGS) for domestic load. The proposed energy integration by HEMS, is executed by implementing a Load Management Algorithm (LMA), which functions based on ANN forecast models. Demand Side Management (DSM) techniques are the back bone for LMA proposed in this paper. Historical temperature data and electrical load data of a domestic load center at Municipality of Birmingham, City of Alabama, USA, is considered to implement the proposed LMA. Two subroutine programs/algorithms are designed to implement the proposed LMA. First is of implementing Load Priority Techniques, to assign Load Priority for the loads for the hour based on the temperature forecast. Temperature forecast is done using Nonlinear Auto Regression with Exogenous Inputs (NARX) ANN time series model. And this is termed as Load Priority Assignment Algorithm (LPAA). Second is of predicting Threshold Power (PTh) for the hour as per load priority assigned by LPAA, this is termed as estimation of PTh. Big data driven Nonlinear Auto Regression with Exogenous Inputs (NARX) Neural Net based temperature forecasting model is implemented and used to assign hourly priority for all the appliances/loads. Big data driven Artificial Neural Network (ANN) based load power forecasting model is implemented and used to predict hourly PTh, LMA proposed for HEMS is implemented with step by step comparison of current load demand with PTh and transferring the loads between the energy sources, according to the load priority assignment from LPAA. Proposed LMA based HEMS contributes to enhance the penetration of nonconventional DGS into domestic load sector. Simulation of proposed HEMS is implemented in MATLAB-Simulink environment using MATLAB 2018b. Simulation model is embedded into target hardware: Arduino 2560 by enabling serial communication protocols between MATLAB-SIMULINK and ARDUINO. Hence the experimental setup of HEMS would be functional to prove simulation results and to transfer the loads in real time. Working Hardware model of the HEMS at domestic load center is fabricated using simple and cost effective components such as current sensor ACS712, Arduino Mega 2560, relays and relay driver circuit.

GreenLoading: Using the citizens band radio for energy-efficient offloading of shared interests

Cellular networks are susceptible to being severely capacity-constrained during peak traffic hours or at special events such as sports and concerts. Many other applications are emerging for LTE and 5G networks that inject machine-to-machine (M2M) communications for Internet of Things (IoT) devices that sense the environment and react to diurnal patterns observed. Both for users and devices, the high congestion levels frequently lead to numerous retransmissions and severe battery depletion. However, there are frequently social cues that could be gleaned from interactions from websites and social networks of shared interest to a particular region at a particular time. Cellular network operators have sought to address these high levels of fluctuations and traffic burstiness via the use of offloading to unlicensed bands, which may be instructed by these social cues. In this paper, we leverage shared interest information in a given area to conserve power via the use of offloading to the emerging Citizens Broadband Radio Service (CBRS). Our GreenLoading framework enables hierarchical data delivery to significantly reduce power consumption and user fairness variation and includes a Broker Priority Assignment (BPA) algorithm to select data brokers for users. With the use of in-field measurements and web-based Google data across four diverse U.S. cities, we show an order of magnitude power savings via GreenLoading over a 24-hour period, on average, and power savings up to 97% at peak traffic times. Finally, we consider the role that a relaxation of wait times can play in the power efficiency of a GreenLoading network.

Priority Assignment Algorithms for Shared Mesh Protection Switching

Shared mesh protection (SMP) is a protection mechanism that recovers traffic delivery against failures on a working path as rapidly as linear protection. In addition, it allows for resource sharing among the protection paths with different endpoints in a mesh network by coordinating the use of shared resources when multiple protection paths compete for these resources. Owing to limited protection resources and the preemption in consequence of priority comparison, the SMP introduces a new optimization problem whose objective is to find the optimal priority assignment that maximizes the number of protected services against any failure case for a given SMP network. In this paper, we recognize that the problem of finding the optimal solution is NP-hard and propose min-degree and capacity-density approximation algorithms, whose approximation ratios are presented with the maximum degree of services and the maximum capacity of segments of an SMP network. Moreover, we propose a depth-based algorithm that always finds the optimal solution for a hypertree-shaped SMP network. We compare the performance of the proposed algorithms in terms of the protection ratio, which we define in this paper.

Optimal priority assignment for messages on controller area network with maximum system robustness

SummaryController Area Network (CAN) is broadly used for in‐vehicle networking. Each message on the CAN bus is given a unique identifier as its priority. When more than one node starts to transmit at the same time, the message with the highest priority will be sent. The schedulability of the message set is significantly affected by the priority assignment algorithms employed. To ensure that the CAN system tolerate additional interference, we propose system robustness, a concept to represent the total robustness of the message set. Maximum system robustness of message set problem is NP‐hard. We find that assigning a higher priority to the message with small transmission time or low utilization may lead to more system robustness. Based on this, this paper presents four optimal priority assignment approximation algorithms using the Audsley's priority assignment method: TMPA, UMPA, GTMPA, and GUMPA. TMPA and UMPA are heuristic algorithms. GTMPA and GUMPA are greedy algorithms. Experimental results show that, compared with the existing optimal algorithms, TMPA, GTMPA, and GUMPA can improve the system robustness of CAN message sets effectively.

A New Method of Priority Assignment for Real-Time Flows in the WirelessHART Network by the TDMA Protocol

WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow transmission is crucial in ensuring real-time network performance and the schedulability of real-time network flows. We study the priority assignment of real-time flows in WirelessHART on the basis of the multi-channel time division multiple access (TDMA) protocol to reduce the delay and improve the ratio of scheduled. We provide three kinds of methods: (1) worst fit, (2) best fit, and (3) first fit and choose the most suitable one, namely the worst-fit method for allocating flows to each channel. More importantly, we propose two heuristic algorithms—a priority assignment algorithm based on the greedy strategy for C (WF-C) and a priority assignment algorithm based on the greedy strategy for U(WF-U)—for assigning priorities to the flows in each channel, whose time complexity is . We then build a new simulation model to simulate the transmission of real-time flows in WirelessHART. Finally, we compare our two algorithms with WF-D and HLS algorithms in terms of the average value of the total end-to-end delay of flow sets, the ratio of schedulable flow sets, and the calculation time of the schedulability analysis. The optimal algorithm WF-C reduces the delay by up to and increases the schedulability ratio by up to , and it reduces the calculation time compared with the HLS algorithm.

Optimal priority and threshold assignment for fixed-priority preemption threshold scheduling

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold. This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable. We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

Fixed-Priority Scheduling for Two-Phase Mixed-Criticality Systems

In this article, a two-phase execution model is proposed for mixed-criticality (MC) tasks. Different from traditional MC tasks with a computation phase only, the two-phase execution model requires a memory-access phase first to fetch the instructions and data, and then computation. Theoretical foundations are first established for a schedulability test under given memory-access and computation priority assignment. Based on the established theoretical conclusions, a two-stage priority assignment algorithm, which can find the best priority assignment for both memory-access and computation phases under fixed-priority scheduling, is further developed. Extensive experiments have been conducted and the experimental results validate the effectiveness of our proposed approach.

An exact schedulability test for fixed-priority preemptive mixed-criticality real-time systems

The current literature of fixed-priority scheduling algorithms relies on sufficient tests to determine if a set of mixed-criticality sporadic tasks is schedulable on a single processor. The drawback of these safe tests is their pessimism, a matter that could be solved if an exact schedulability analysis is used. However, because of the non-deterministic behavior of tasks in the mentioned setups, exact quantification of worst-case response times, needed for the test, is a difficult problem; more precisely, such a quantification needs evaluation of enormous sequences of job executions. The core problem is thus to merge such sequences to make the analysis practical. This paper, for the first time, gives an algorithm for exact worst-case response time characterization of mixed-criticality sporadic real-time tasks executing according to a given fixed-priority scheduler. We use a set of techniques which carefully consider the task properties and their relation to the worst scenarios to prune the analysis state space. We also show an interesting result that if an exact schedulability test is used, the Audsley's optimal priority assignment algorithm is not applicable to the mixed-criticality case. Accordingly, we need new priority assignment algorithms to work with the exact test; we give a simple task priority assignment algorithm to this aim. The performance of the proposed exact test (in terms of time complexity) is examined and the effectiveness of some heuristic priority assignment algorithms using the test (in terms of the ratio of task sets which are deemed schedulable) are compared.

Schedulability analysis and stack size minimization with preemption thresholds and mixed-criticality scheduling

Abstract Mixed-Criticality Scheduling (MCS) is an effective approach to addressing certification requirements of safety-critical Cyber-Physical Systems that integrate multiple subsystems with different levels of criticality in application domains such as avionics and automotive systems. Although MCS was originally proposed in the context of safety-critical avionics applications, it is also finding its way into the automotive domain which faces intense cost-cutting pressure in today’s hyper-competitive market, so it is important to minimize hardware costs by adopting low-cost processors with limited processing and memory resources. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption in real-time scheduling, with benefits of reduced stack size and reduced number of preemptions compared to fully-preemptive scheduling. We present schedulability analysis to enable integration of PTS with MCS, including two variants PT-rtb and PT-max, in order to reduce application stack space requirement, and enable efficient implementation of MCS on resource-constrained embedded platforms. We also integrate our schedulability tests with priority and preemption threshold assignment algorithms, to have a complete solution for analysis and synthesis of mixed-criticality systems. Performance evaluation illustrates the benefits of our approach in terms of increased schedulability and reduced stack requirement.

On the compatibility of exact schedulability tests for global fixed priority pre-emptive scheduling with Audsley’s optimal priority assignment algorithm

Audsley's optimal priority assignment (OPA) algorithm can be applied to multiprocessor scheduling provided that three conditions hold with respect to the schedulability tests used. In this short paper, we prove that no exact test for global fixed priority pre-emptive scheduling of sporadic tasks can be compatible with Audsley's algorithm, and hence the OPA algorithm cannot be used to obtain an optimal priority assignment for such systems.