Earliest Deadline First Research Articles

Guarantee the Quality-of-Service of Control Transactions in Real-Time Database Systems

Temporal consistency analysis is an important problem in Real-Time Database Systems (RTDBSs). Most of existing works on hybrid transaction co-scheduling focus on exploring effective deadline and period assignment methods which can guarantee the temporal consistency of data objects and the schedulability of transactions. To the best of our knowledge, all existing researches have not yet invested sufficient efforts on guaranteeing the Quality of Service (QoS) of control transactions and allow control transactions finish their execution after the data obtained by these control transactions expire. In this paper, we define a new problem of how to schedule a hybrid transaction set under the guarantee on QoS of control transactions. Two deadline and period assignment methods for addressing the defined problem are proposed in this work. Both the proposed methods support the Earliest Deadline First (EDF) scheduling. Extensive experiments are conducted to evaluate the performances of the proposed methods in terms of acceptance ratio, processor workload and efficiency.

TIMER-Cloud: Time-Sensitive VM Provisioning in Resource-Constrained Clouds

Resource management is a vital factor for better performance in cloud systems and many resource allocation algorithms have been studied. In this work, focusing on applications with timing constraints (i.e., deadlines) running on resource-constrained clouds that have multiple heterogeneous nodes of computing resources (e.g., CPU cores and memory), we propose TIMER-Cloud , a time-sensitive resource allocation and virtual machine (VM) provisioning framework. As a key component of the framework, user requests (of running certain applications) are prioritized according to their deadlines and resource demands (in the form of VM and its operation time). Specifically, in addition to the intuitive Earliest Deadline First (EDF) ordering of requests, we propose three prioritization heuristics: a) one based on the Time-Sensitive Resource Factor (TSRF) that incorporates a request's deadline and usage efficiency of all its resources; b) the Dominant Share (DS) extension of TSRF that emphasizes the most demanded resource of a request aiming at obtaining balanced resource usage among the nodes; and c) a unified k-EDF scheme that integrates the ideas of EDF and TSRF/DS to balance the needs of meeting imminent deadlines of requests and improving resource usage efficiency. Then, for the mapping of the prioritized user requests to the heterogeneous nodes, we propose a novel request-to-node mapping algorithm based on the idea of euclidean Distance that finds the node with the best match of its resource requirements for each request. TIMER-Cloud has been implemented and validated on a cloud testbed powered by OpenStack with a few heterogeneous nodes. The proposed VM provisioning schemes are further evaluated through extensive simulations using the execution data of benchmark applications. The results show that the proposed schemes can outperform the state-of-the-art deadline oblivious scheme by serving up to 12 percent more user requests and achieving up to 8 percent more system rewards for the over-loaded scenario with 140 percent system load.

Adaptive Resource Allocation and Consolidation for Scientific Workflow Scheduling in Multi-Cloud Environments

The emerging multi-cloud environments (MCEs) empower the execution of large-scale scientific workflows (SWs) with sufficient resource provisioning. However, due to complex task dependencies in SWs and various cost-performance of cloud resources, the SW scheduling in MCEs faces huge challenges. To address these challenges, we propose an Online Workflow Scheduling algorithm based on Adaptive resource Allocation and Consolidation (OWS-A2C). In OWS-A2C, the deadline reassignment is first executed for SW tasks based on the execution performance of instance resources, which enhances resource utilization from a local perspective when executing an SW. Next, the execution instances are allocated and consolidated according to the performance requirements of multiple SWs, which improves resource utilization and reduces the total costs of executing multiple SWs from a global perspective. Finally, the SW tasks are dynamically scheduled to the execution instances with the earliest-deadline-first (EDF) discipline and completed before their sub-deadlines. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed OWS-A2C on SW scheduling in MCEs, which outperforms three baseline scheduling methods with higher resource utilization and lower execution costs under deadline constraints.

Hybrid Scheduler (S_LST) for Soft Real-Time System based on Static and Dynamic Algorithm

In the Soft Real-Time System scheduling process with the processor is a critical task. The system schedules the processes on a processor in a time interval, and hence the processes get chance to executes on the processor. Priority-driven scheduling algorithms are sub-categorized into mainly two categories called Static Priority and Dynamic Priority Scheduler. Critical Analysis of more static and dynamic priority scheduling algorithms have been discussed in this paper. This paper has covered the static priority algorithms like Rate Monotonic (RM) and Shortest Job First (SJF) and the dynamic priority algorithms like Earliest Deadline First (EDF) and Least Slack Time First (LST). These all algorithms have been analyzed with preemptive process set and this paper has considered all the process set are periodic. This paper has also proposed a hybrid approach for efficient scheduling. In a critical analysis, it has been observed that while scheduling in underload situation dynamic priority algorithms perform well and even EDF also make sure that all process will meet their deadline. However, in an overload situation, the performance of dynamic priority algorithms reduce quickly, and most of the task will miss its deadline, whereas static priority scheduling algorithms miss a few deadlines, even it is possible to schedule all processes in underload situation, whereas in an overload situation, the static algorithms perform well compared to the dynamic scheduler. This paper is proposing one Hybrid algorithm call S_LST which uses the concept of LST and SJF scheduling algorithm. This algorithm has been applied to the periodic task set, and observations are registered. We have observed the Success Ratio (SR) & Effective CPU Utilization (ECU) and compared all algorithms in the same conditions. It is noted that instead of using LST and SJF as an independent algorithm, Hybrid algorithm S_LST performs well in underload and overload scenario. Practical investigations have been led on a huge dataset. Data Set consists of the 7000+ process set, and each process set has one to nine processes and load varies between 0.5 to 5. It has been tried on 500-time unit to approve the rightness everything being equal.

Proactive Caching for Low Access-Delay Services under Uncertain Predictions

Network traffic for delay-sensitive services has become a dominant part in the network. Proactive caching with the aid of predictive information has been proposed as a promising method to enhance delay performance. In this paper, we analytically investigate the problem of how to efficiently utilize uncertain predictive information to design proactive caching strategies with provably good access-delay characteristics. We first derive an upper bound for the average amount of proactive service per request that the system can support. We then analyze the behavior of a family of threshold-based proactive strategies using an innovative Markov chain analysis, and show that the average amount of proactive service per request can be maximized by properly selecting the threshold. Finally, we propose the UNIFORM strategy, which is the threshold-based strategy with the optimal threshold. Surprisingly, we show that it outperforms the commonly used Earliest-Deadline- First (EDF) type proactive strategies in terms of delay. We perform extensive numerical experiments to demonstrate the influence of thresholds on delay performance, and explicitly compare performance of the EDF strategy and the UNIFORM strategy.

Weakly-hard Real-time Guarantees for Earliest Deadline First Scheduling of Independent Tasks

The current trend in modeling and analyzing real-time systems is toward tighter yet safe timing constraints. Many practical real-time systems can de facto sustain a bounded number of deadline-misses, i.e., they have Weakly-Hard Real-Time (WHRT) constraints rather than hard real-time constraints. Therefore, we strive to provide tight Deadline Miss Models (DMMs) in complement to tight response time bounds for such systems. In this work, we bound the distribution of deadline-misses for task sets running on uniprocessors using the Earliest Deadline First (EDF) scheduling policy. We assume tasks miss their deadlines due to transient overload resulting from sporadic jobs, e.g., interrupt service routines. We use Typical Worst-Case Analysis (TWCA) to tackle the problem in this context. Also, we address the sources of pessimism in computing DMMs, and we discuss the limitations of the proposed analysis. This work is motivated by and validated on a realistic case study inspired by industrial practice (satellite on-board software) and on a set of synthetic test cases. The synthetic experiment is dedicated to extensively study the impact of EDF on DMMs by presenting a comparison between DMMs computed under EDF and Rate Monotonic (RM). The results show the usefulness of this approach for temporarily overloaded systems when EDF scheduling is considered. They also show that EDF is especially useful for WHRT tasks.

Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.

ACO Based Scheduling Method for Soft RTOS with Simulation and Mathematical Proofs

The Ant Colony Optimization (ACO) algorithm is a mathematical model enlivened by the system searching conduct of ants. By taking a gander at the qualities of ACO, it is most suitable for scheduling of tasks in soft real-time systems. In this paper, the ACO based scheduling method for the soft real-time operating system (RTOS) has been profound with mathematical and practical proof. In Mathematical proof, three different Propositions and two Theorems have been given, which prove the correctness of the proposed algorithm. Practical experiments also support mathematical proofs. During the investigation, observations are gathered with different periodic task set. Algorithms have been observed regarding Success Ratio (SR) and Effective CPU utilization (ECU). ACO based scheduling algorithm has been compared with the Earliest Deadline First (EDF) algorithm with parameter SR and ECU. The EDF is dynamic scheduling algorithm and it is most suitable in RTOS when task set is preemptable. It is noted that the new algorithm is equally efficient during under loaded conditions when CPU load is less than one. ACO based scheduling algorithm performs superior during the overloaded conditions when CPU load is more than one where as EDF algorithm performance degraded in overload condition. Empirical study has been executed with a hefty Dataset consist of more than 7500 task set, and a set contains different one to nine processes where CPU load is dynamic for each process set and differ from 0.5 to 5. Algorithms have been executed on five-hundred-time unit for each process set to authenticate the accuracy of both algorithms.

A novel four-tier architecture for delay aware scheduling and load balancing in fog environment

Fog computing paradigm is located between IoT devices and cloud paradigm that aim is to minimize the latency in terms of task scheduling and load balancing. To deal with the huge amount of data sensing from different IoT devices, in this paper we propose a four tiers architecture for delay aware scheduling and Load Balancing in the fog environment. Tier-1 is the bottom tier which consists of IoT devices. In the second tier, the applications (workloads) are categorized into two categories: High Priority (HP) and Low Priority (LP) by router based on the Dual Fuzzy Logic Algorithm. Fuzzifier considers four input metrics: task size, arrival time, minimum execution time and maximum completion time. A task with high priority is transmitted to the third tier (fog tier). In the third tier, a novel fog structure has been invoked namely Artificial Fractals consists of nodes. The fog nodes are clustered using K-means++ clustering algorithm. Each fog node is carried out several actions such as scheduling, monitoring, and communication. To schedule tasks within the fog node, we propose the Earliest Deadline First (EDF) task scheduling algorithm. The current usage of fog node is determined by Artificial Neural Network (ANN). If an IoT device does not get the required resource then the request is forwarded to the cloud tier. Our proposed work has been validated over a real-time VSOT (Video Surveillance/Object Tracking) application using iFogSim and the performance is evaluated in terms of response time, scheduling time, load balancing rate, delay, and energy consumption.

Decomposition-Based Real-Time Scheduling of Parallel Tasks on Multicores Platforms

Multicore processors have become mainstream computation platforms not only for general and high-performance computers but also for real-time embedded systems. To fully utilize the computation power of multicores, software must be parallelized. Recently, there has been a rapidly increasing interest in real-time scheduling of parallel real-time tasks, but the field is still much less mature than traditional real-time scheduling of sequential tasks. In this article, we study the real-time scheduling and techniques for parallel real-time tasks based on decomposition, where a task graph is transferred to a set of independent sporadic tasks. In particular, we propose new decomposition strategies that better explore the structure feature of each task to improve schedulability. We develop schedulability tests for the global earliest deadline first (EDF) scheduling algorithm based on decomposition and three types of its variants, with their own pros and cons in different aspects. We conduct experiments to evaluate the real-time performance of our proposed scheduling algorithms against the state-of-the-art scheduling and analysis methods of different types.

Scheduling Periodic Real-Time Traffic in Lossy Wireless Networks as Restless Multi-Armed Bandit

Scheduling periodic real-time traffic in wireless networks is challenging due to the unreliable wireless channels. We first formulate the scheduling problem as a restless multi-armed bandit (RMAB) problem. Then, we theoretically prove the indexability of the scheduling problem. Based on the indexability, we further derive the closed-form Whittle’s index. It motivates us to propose a simple index-based scheduling algorithm which schedules users with higher indices. Simulation results have demonstrated that the derived scheduling algorithm outperforms the earliest deadline first (EDF) and myopic algorithms in terms of the total discounted reward.

Spatial–temporal routing for supporting end-to-end hard deadlines in multi-hop networks

We consider the problem of routing packets with end-to-end hard deadlines in multihop communication networks. This is a challenging problem due to the complex spatial–temporal correlation among flows with different deadlines. To tackle this problem, we introduce the concepts of virtual links and virtual routes to incorporate end-to-end deadline constraints into routing and propose a novel virtual queue architecture to guide the spatial–temporal routing where the routing algorithm specifies where and when a packet should be routed. For the frame-based periodic traffic, the proposed policies can support any arrival within throughput region when the ratio between the link capacity and the packet size is sufficiently large. For the general traffic, we integrate a constrained resource-pooling heuristic into the spatial–temporal routing, which improves the delivery ratio and performs well. Our extensive simulations show that the policies outperform traditional policies such as backpressure and earliest-deadline-first (EDF) for more general traffic flows in multihop communication networks.

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

SummaryInternet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

An Efficient Uplink Scheduler for WiMAX Communication System with Prevention from Security Attacks

ABSTRACTTo provide broadband access services to customers, which resides residential and enterprise field, Worldwide Interoperability for Microwave Access (WiMAX) is one of the famous wireless technologies. WiMAX can provide high-speed data rate with large-scale coverage such that it contains a high transmission range as well as it supports high mobility with a large number of nodes. It requires scheduling algorithm in the MAC layer of its protocol stack because the number of users that want service is high in number. One of the most used scheduling algorithms is the Earliest Deadline First (EDF) but it contains certain performance limitations. As well as there is a need for security before allocation of bandwidth to the users because WiMAX is a wireless technology and may be vulnerable to lots of security attacks and if once the bandwidth is allocated to any malicious user, then the system of WiMAX communication will be under dangerous attacks. In this paper, we proposed a secure heuristic earliest deadline first uplink scheduler in the MAC layer of the WiMAX with some authorization procedures. The proposed scheme protects the allocation of bandwidth to any malicious node such that the system will be prevented from security attacks like Denial of Service (DoS), flooding attack, etc., as well as it will use heuristic approach for scheduling. Therefore, it can efficiently schedule user’s request and provide fairness to the WiMAX system and we called as Secure Heuristic EDF Uplink Scheduler (SH-EDF). The performance of SH-EDF uplink scheduler will be evaluated in the provisions of various performance metrics.

Scheduling and power management in energy harvesting computing systems with real-time constraints

Abstract Recently, a new class of energy autonomous systems has emerged. They scavenge regenerative energy from the environment which allows them perpetual operation. In this paper we focus on systems with energy harvesting and DPM (Dynamic Power Management) capabilities under real-time requirements expressed in terms of deadlines. The classical Earliest Deadline First (EDF) scheduler is optimal when dedicated to deadline-constrained job sets in systems with no energy limitation. However, greediness in consuming the energy makes EDF not always the most appropriate solution to energy harvesting conditions. The purpose of this paper is to answer the following question: How to choose a scheduler for the design of a real-time energy harvesting application where the real-time jobs execute on a single processor? We consider a system model composed of a uniprocessor platform provided with energy harvester (e.g. solar panel) and energy reservoir (e.g. battery or capacitor). Firstly, we investigate three distinct schedulers based on the earliest deadline rule, namely ED-H, EH-EDF and EDF. They are respectively proved optimal for clairvoyant, idling and non-idling settings. Secondly, we evaluate the relative performance of the proposed scheduling algorithms based on simulation experiments. We bring to light the effect of clairvoyance and idling capabilities of the scheduler on the improvement in quality of service which is related to deadline success rate and energy storage capacity requirement for maintaining zero deadline miss rate. In addition, this paper gives a selection guide that leads any software designer to find the scheduler that is most pertinent in accordance to operational specifications.

Task allocation, migration and scheduling for energy-efficient real-time multiprocessor architectures

Abstract Power consumption is becoming complicated to tackle each day in the embedded systems industry. As the demand for speed and accuracy increases, so does the power consumption of these systems. While introduction of multiprocessors have been shown to reduce the power problem compared to a uni-processor, researchers have tried to attain results using methods such as voltage and frequency scaling and scheduling methods. As an extension of these methods, task migration can be used to further reduce the power consumption of multiprocessor architectures. This article introduces a three-part framework to energy reduction of applications on a multiprocessor through an effective task allocation technique followed by task migration and finally a proper task scheduling scheme based on the earliest deadline first (EDF) method. Our experimental results showed great improvement over multiple state of the art energy reduction methods in the literature especially in the case of higher system workload over a range of underlying system architectures.

Handling Transients of Dynamic Real-Time Workload Under EDF Scheduling

Real-time dynamic workload consists of tasks that can arbitrarily join and leave the system at run-time. To avoid incurring deadline misses, tasks that request to join the system must pass an admission test, which has to cope with potential scheduling transients originated by the residual effect of the tasks that previously left the system. This phenomenon may require some tasks to suffer an admission delay before being accepted for execution. This paper focuses on uniprocessor earliest-deadline first (EDF) scheduling with constrained deadlines and explicitly considers methods for handling scheduling transients in the presence of dynamic real-time workload. A generalized analysis framework is first presented to overcome several limitations of the existing approaches (including the support for overlapping transients), and is then used to derive methods for computing bounds on the admission delays incurred by tasks. Building on such results, an on-line protocol is proposed to handle the admission control of a dynamic workload, which also comes with a variant that can execute in polynomial time to favor its practical application. Furthermore, the paper shows how the presented analysis can be used off-line for analyzing mode-changes among static task sets. Experimental results are finally presented to evaluate the proposed algorithms.

Deadline-Aware MapReduce Job Scheduling with Dynamic Resource Availability

As MapReduce is becoming ubiquitous in large-scale data analysis, many recent studies have shown that the performance of MapReduce could be improved by different job scheduling approaches, e.g., Fair Scheduler and Capacity Scheduler. However, most exiting MapReduce job schedulers focus on the scenario that MapReduce cluster is stable and pay little attention to the MapReduce cluster with dynamic resource availability. In fact, MapReduce cluster resources may fluctuate as there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads. Thus, there is a growing need for providing predictable services to users who have strict requirements on job completion times in such dynamic environments. In this paper, we propose, RDS , a Resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times. We further extend the design of RDS scheduler to support flexible performance goals in various dynamic clusters. In particular, we use flexible deadline time bounds instead of the single fixed job completion deadline. We have implemented RDS in the open-source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36 and 10 percent compared with Fair Scheduler and Earliest Deadline First (EDF) scheduler, respectively. In a Hadoop cluster running partially on renewable energy, the experimental result shows the green power based resource prediction approach can further reduce the penalty of deadline misses by 16 percent compared to Auto-Regressive Integrated Moving Average (ARIMA) prediction approach.

A Generalized Parallel Task Model for Recurrent Real-Time Processes

A model is considered for representing recurrent precedence-constrained tasks that are to execute on multiprocessor platforms. A recurrent task is specified as a directed acyclic graph (DAG), a period, and a relative deadline. Each vertex of the DAG represents a sequential job, while the edges of the DAG represent precedence constraints between these jobs. All the jobs of the DAG are released simultaneously and need to complete execution within the specified relative deadline of their release. Each task may release jobs in this manner an unbounded number of times, with successive releases occurring at least the specified period apart. Conditional control structures are also allowed. The scheduling problem is to determine whether a set of such recurrent tasks can be scheduled to always meet all deadlines upon a specified number of identical processors. This problem is shown to be computationally intractable, but amenable to efficient approximate solutions. Earliest Deadline First (EDF) and Deadline Monotonic (DM) are shown to be good approximate global scheduling algorithms. Polynomial and pseudo-polynomial time schedulability tests, of differing effectiveness, are presented for determining whether a given task set can be scheduled by EDF or DM to always meet deadlines on a specified number of processors.