Earliest Deadline First Algorithm Research Articles

Impact in the Parallel Processing of IHM-Plasma Using the Earliest-Deadline-First Algorithm for the Task-Scheduler Realized by Hardware

Abstract—This work studies the impact in the parallel processing of the Interlocked-Hardware-Microkernel (IHM) Plasma microprocessor (IHM-Plasma) by implementing the Earliest-Deadline-First (EDF) algorithm by hardware in the task-scheduler block that belongs to those task-based operational systems, such as that related to the real-time operational systems (RTOS). IHM-Plasma presents Reduced Instruction Set Computer (RISC) architecture. The main results have observed by this study show that the IHM-Plasma running the EDF algorithm by hardware has increased the tasks executed per second in 174% while running a task-based operational system with 14 active tasks in comparison to the original Plasma running the EDF algorithm by software. The developed work has great potential use in Hard Real-Time Systems and others where a rigid control of deadlines is essential and hold many tasks.

A security-aware hardware scheduler for modern multi-core systems with hard real-time constraints

In this paper, we propose an online security-aware hardware scheduler, the so-called Secure And Fast hardware Scheduler (SAFAS), for real-time task scheduling in multi-core systems in the presence of schedule-based side-channel attacks. To avoid such attacks and ensure that all tasks meet their deadlines, SAFAS schedules critical tasks and their replicas using a hardware-based strict Least Slack Time first (LST) algorithm independently while it independently schedules the non-critical tasks using a hardware-based EDF algorithm. SAFAS enhances the system performance and reduces the chance of side-channel attacks due to the different processing cores allocated to each task in each scheduling interval. The hardware scheduler operates independently and in parallel with the multi-core system and hides the scheduling characteristics from adversaries. The software-based Earliest Deadline First (EDF) algorithm is also used for schedulability tests and feasibility analysis of hard real-time periodic tasks to maximize the number of tasks scheduled successfully in the multi-core system. SAFAS has been synthesized and simulated on a Xilinx Vivado 2018.2 and implemented on a Spartan-7 FPGA chip. Our experimental results indicate that SAFAS increases the performance of the system by 4.8 times as compared to previous state-of-the-art hardware schedulers while guaranteeing that all critical tasks and their replicas meet their deadlines.

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Objectives: To propose an algorithm to balance resource utilization and revenue generation in the cloud environment. Methods: This study proposes the Entropy-Based TOPSIS algorithm for task scheduling in IaaS Clouds (EBTASIC) to balance resource utilization and revenue generation using the objective-based Entropy Weighting Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings/Novelty: Various performance evaluation factors are calculated using EBTASIC and compared with baseline algorithms First Come First Serve (FCFS) and Earliest Deadline First (EDF) algorithms over 12750 lease requests with hard deadlines. The actual response time of EBTASIC is 37.61 percent faster than FCFS and 47.95 percent faster than EDF. Total time spent on lease execution by EBTASIC is reduced by 3.43 percent when compared to FCFS and 3.99 percent when compared to EDF. Turnaround time of EBTASIC is lowered by 21.14 percent when compared to FCFS and 28.81 percent when compared to EDF. EBTASIC throughput is enhanced by 40.80 percent over FCFS and 54.3 percent over EDF. The average response time of EBTASIC is shortened by 9.38 percent compared to FCFS and 14.81 percent compared to EDF. Resource utilization of the proposed algorithm EBTASIC is enhanced by 25.54 percent over FCFS and 6.79 percent over the EDF algorithm. Keywords: Task and VM Scheduling; MCDM Techniques; TOPSIS; Entropy Weighting Method; EBTASIC Algorithm

Efficient task scheduling in cloud environment

SummaryCloud computing is the provision of on‐demand computing resources over the internet and on a pay‐as‐you‐go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p‐EDF) and Non‐Preemptive EDF (np‐EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de‐merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time.

Priority-based Task Pre-processing in IoT Sensory Environments

Background: The motive of the Internet of Things (IoT) is to monitor and to control the devices that are connected to the internet. In IoT sensory environments, the application queries for the physical quantities in the spatiotemporal domain. The interaction between the sensors and the applications from the internet is the next big thing in the era of the internet of things. To minimise the resource utilisation, task scheduling mechanisms are implemented to the network. Methods: The PRITRAPS (Priority-based Task aware Pre-processing and Scheduling) is a mechanism that is employed in real time scenarios of industries. In which different applications units are accessing the gateway unit to measure and monitor the parameters of different service types. PRITRAPS employs priority among the tasks to reduce the network load. Results: The QoS parameters of the system are analysed and compared with the previous methodologies. The PRITRAPS mechanism consists of a task pre-processor unit, Scheduler and EMS module within the gateway unit. The scheduling algorithm employed in PRITRAPS is EDF (Earliest Deadline First) algorithm. The pre-processing task unit decreases the number of tasks by choosing the tasks having similar spatial and temporal requirements. The residual energy of the sensor nodes can help the scheduler for deciding the sensor nodes in respective of task requirements. The scheduler finds the best potential nodes and assigns them to the task for processing. Conclusion: To reduce the tasks arrivals at the wireless sensor unit, a priority based CCTs (Critical Covering Task sets) is proposed, and it effectively reduces the packet congestion and network overload. The results obtained are satisfactory and proven that PRITRAPS outperform TRAPS in energy consumption of a node by processing the tasks on the node. PRITRAPS require only 50 % of the time that has been taken by TRAPS for serving the tasks. The PRITRAPS mechanism is implemented in NS3 simulator and tested for different task sets.

Modified Optical Burst Switching (OBS) Based Edge Node Architecture Using Real-Time Scheduling Techniques

Contention is the main problem in optical burst switching (OBS) systems that lead to loss more burst by dropping the contended burst. To deal with this problem, we propose a new proactive method which is based on the integration of Real-Time Scheduling Techniques: Earliest-deadline-first (EDF) and round-robin (RR). Based on this consideration, this paper proposes a series of novel coherent interdependence methods to propose a novel OBS edge node Architecture: (i) An adaptive priority system to obtain QoS guarantees, (ii) Admission control and bandwidth distribution to maximize the link throughput. The proposed technique classifies the incoming data packets into two categories: granted service (GS) for high priority and best effort (BE) for low priority. The RR technique is used for scheduling different BE and GS packets when they have the same deadlines while the EDF is used to manage GS packets with different deadlines. In addition, the admission and bandwidth assignment policies vary in function of traffic characteristics. For GS traffic, only the bandwidth used is needed, while the remaining bandwidth is assigned to best effort traffic. With the EDF and RR scheduling algorithms, the proposed edge node design obtains a brand new architecture. The proposed OBS architecture has been tested for dynamic traffic in which both BE and GS traffic arrives according to a Poisson distribution for different scenarios based on traffic distribution and switch resources reservation. In scenario#1, 90% GS traffic and 10% BE traffic and reserve 7% of the switch resources for BE traffic are considered. Therefore, the proposed architecture provides a higher grade of service to the end-users with the guaranteed service level of agreements than the existing architecture that does not implement resource reservation protocols. In addition, the modified OBS-edge node is designed and implemented on FPGA Virtex-11C2V40. The hardware performance analysis shows that the network dimension is a key factor in latency measurement. A 2×2 network dimension can serve approximately 230 Mbps throughput and approximately 400 Mbps in the case when the number of flits (segment of burst) is twice.

The Earliest Smooth Release Time for a New Task Based on EDF Algorithm

Although EDF (Earliest Deadline First) algorithm has received extensive study during the past more than 40 years, only a few researchers have published their efforts on the bandwidth transfer between tasks. If current running tasks are compressed to free part of their occupied bandwidth to accommodate new requirements, such as a new task’s insertion, then a basic requirement of this operation is smoothness, that is, no deadline should be missed. Suppose current tasks are immediately compressed at the request time of the new task, in order to guarantee the smoothness, the new task may have to be released later than the request time. An interesting and challenging problem is to find the earliest smooth release time. In this paper, an algorithm to evaluate the earliest release time for single task’s insertion is presented and formally proved. To finish the algorithm, only the deadlines during the transition should be checked, and each of them needs to be checked at most once. A novel experimental approach is adopted and more than 4549320 different tests are implemented to verify the theorems in simulation.

Research on Real-Time Embedded Software Scheduling Model Based on EDF

Schedulability analysis is a very important part in real-time system research. Because the scenarios faced by real-time systems are very complicated, the functional characteristics must be combined with the predictability of response time. It is necessary to ensure the correctness of the calculation results and meet the real-time requirements. To solve this problem, we propose the IEDF (Improved Earliest Deadline First) algorithm, which is combined with the queuing theory model. The IEDF algorithm is based on the EDF (Earliest Deadline First) algorithm, which is more suitable for the scheduling of real-time embedded system. Scheduling of non-periodic tasks that arrive randomly. There are two types of tasks in the task set, tasks with a high static priority are executed first. In the ready queue of the same priority task, the deadline and execution time are considered. The comparison of simulation experiments shows that: the sum of waiting time in the execution of IEDF with enough deadline is much less than that of ordinary queuing algorithm; the number of errors in the execution of IEDF algorithm with deadline is much less than that of ordinary queuing algorithm. These results demonstrate the feasibility of the IEDF algorithm.

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.

Earliest deadline first scheduling technique for different networks in network control system

The idea of this research is to determine the best network providing the reduced network time delay in network controlled system (NCS). NCS tackles robust and nonlinear processes that are time-varying or the requirements for the control change with the operating conditions using earliest deadline first (EDF) scheduling technique. It is very useful for minimizing the effects of parameter variations with respect to the process dynamics. The main advantage of applying EDF is that the parameters of the controller can be changed according to deadline along with the process changes. The main limiting factor is that auxiliary measurement should respond quickly to the process change. By applying the EDF algorithm in NCS schemes, the control of the two-tank water-level system is simulated using PID controllers and the control via Internet is simulated with the constant time delays and time-varying delay conditions. The drawbacks of NCS are network-induced delay, packet losses, bit errors, time-varying random transmission delay, lack of synchronization between devices, bandwidth limitations and integration of the modules. As the EDF scheduling approach is based on the mean time-delay parameters, they shift from one set to another when the mean time delay changes from one set to another. Therefore, time-delay issues are reduced by this algorithm. In this research work, the main challenge network-induced delay is reduced by scheduling technique and suitable network for PID control for EDF scheduling is determined among CAN, Ethernet, Switched Ethernet, TDMA and FDMA. CAN and Ethernet have the minimum of 1 ms network-induced time delay when compared with Switched Ethernet, TDMA and FDMA under overload condition. The integral of absolute error is employed to measure the quality of control of each control loop value for EDF scheduling 0.00354 and processor utilization is estimated as U = 0.12.

Design and Implementation of Task Reprocessing on Medium-large Multi-core Architecture

EDF-Based real-time scheduling approach is one of the efficient way of scheduling the recurrent real-time tasks in both soft real-time and hard real-time systems. EDF algorithms entile heavy overhead due to lower scheduling and huge migration. In this paper, an exploratory procedure has been presented for reprocessing of neglected task sets and also examine, whether an improved heuristic set attributed as Enhanced shared CAche Performance (ENCAP) can improve the shared cache performance which processes the task set based on Earliest-Deadline-First (EDF). An Object-Oriented real-time code reusable scheduling components which is designed from the scratch referred as LITMUSRT has been used to test the efficiency and average-case accomplishments where per task utilizations are shortened and overall utilization are not restricted. We also discuss the implementation and experimental evaluation of Task-Repo procedure for co-scheduling task sets, under the utilization of 50-60% (per-task) on small to medium-large multicore LINUX SYSTEM.

A 2-Approximation Algorithm for Scheduling Parallel and Time-Sensitive Applications to Maximize Total Accrued Utility Value

For a time-sensitive application, the usefulness of its end results (also called the application's accrued utility value in the paper) depends on the time when the application is completed and its results are delivered. In this paper, we address the accrued utility value maximization problem for narrow parallel and time-sensitive applications. We first consider the problem in the context of a discrete time domain and present the Spatial-Temporal Interference Based (STIB) scheduling algorithm. We formally prove that the STIB algorithm is a 2-approximation algorithm. Second, we extend our work to a continuous time domain and present a heuristic scheduling algorithm, i.e., the Continuous Spatial-Temporal Interference Based (STIB-C) algorithm to maximize the system's total accrued utility value when the system operates in a continuous time domain. The extensive empirical evaluations reveal that: (1) in a discrete time domain, the systems’ total accrued utility values obtained through the STIB algorithm are consistent with the theoretic bound, i.e., they never go below 50 percent of the optimal value. In fact, on average, the STIB algorithm can achieve over 92.5 percent of the optimal value; (2) compared to other scheduling policies listed in the literature, the developed STIB and STIB-C algorithms have clear advantages in terms of the system's total accrued utility value and the profitable application ratio. In particular, in terms of the system's total accrued utility value, both the STIB and the STIB-C algorithms achieve as much as six times for both the First Come First Come Serve(FCFS) with backfilling algorithm and the Gang Earliest Deadline First (EDF) algorithm, and 4.5 times for the 0-1 Knapsack based scheduling algorithm. In terms of the profitable application ratio, both the STIB and the STIB-C algorithms obtain as much as four times for both the FCFS with backfilling algorithm and the Gang EDF algorithm, and two times for the 0-1 Knapsack based scheduling algorithm.

A task scheduling algorithm based on supercapacitor charge redistribution and energy harvesting for wireless sensor nodes

This paper studies a task scheduling problem for wireless sensor nodes that harvest energy from the ambient environment and use supercapacitors to store the scavenged energy. Power management for supercapacitor-based wireless sensor nodes usually takes into account the supercapacitor self-discharge characteristic. Recently, another supercapacitor characteristic called charge redistribution has also been investigated. This paper considers a task scheduling problem, which is a fundamental power management problem in wireless sensor networks, and develops a modified earliest deadline first (MEDF) algorithm based on supercapacitor charge redistribution and energy harvesting. The MEDF algorithm modifies the earliest deadline first (EDF) algorithm by taking into account the energy constraints of tasks in addition to their timing constraints. The MEDF algorithm is developed and implemented based on a model used to analyze the power flow in wireless sensor nodes. Two metrics are defined to evaluate the MEDF algorithm performance: deadline miss rate for timing performance and energy violation rate for energy performance. The MEDF algorithm is evaluated using both experiments and simulations. Measurement and simulation results show that the MEDF algorithm improves the energy performance of the EDF algorithm while maintaining its timing performance.

Kiểm soát đầu vào để lập lịch cho các yêu cầu người dùng trên tính toán đám mây dựa vào ràng buộc QoS

The problem of admission control to schedule for user requirements is NP-complete [1] in cloud computing environment. To solve this problem it is usually to put building heuristic algorithms to form a simple algorithm with complex polynomial. In this paper, we propose an algorithm of admission control and a scheduling algorithm for user requirements based on the use of ACO algorithm (Ant Colony Optimization) and take advantage of validity period between the requirements so that the total cost of the system is minimal but still satisfying QoS (Quality of Service) constraints for the requirements. Two algorithms are set up and run a complete test on CloudSim. The experimental results show the effectiveness and superiority of the proposed algorithm in comparing with sequential and EDF (Earliest Deadline First) algorithms.

Enhanced uplink scheduling algorithm for efficient resource management in IEEE 802.16

Abstract Worldwide interoperability for Microwave Access (WiMAX) is based on the IEEE 802.16 standard. This technology provides broadband wireless last-mile access in a Metropolitan Area Network (MAN). The IEEE 802.16-2004 standard (i.e. Fixed WiMAX) provides specification for the Medium Access Control (MAC) and Physical (PHY) layers for WiMAX. A critical part of the MAC layer specification is scheduling, which resolves contention for bandwidth and determines the transmission order of users. The algorithms are researched under different mixes of traffic and for various characteristics of the IEEE 802.16 MAC layer. In this research the focus is on the WiMAX uplink traffic scheduling. A scheduling algorithm’s task in a multi-class network is also to categorize the users into one of the pre-defined classes. In this research, the algorithms are tailored at enhancing the collective performance of hybrid algorithms in the WiMAX domains which complement the assigned priorities. The spectrum of constraints which have been extracted from the hybrid Earliest Deadline First(EDF) + Weighted Fair Queuing (WFQ) + First in First out (FIFO) EDF+WFQ+FIFO algorithm include the static nature by which priorities are assigned and maintained during the entire duration of a transmission time and the core attributes of absolute dependence on deadline. The second constraint is embedded within the EDF scheduling algorithm and the perseverance of pursuing deadline associated weightages. In this research, the reengineering of the scheduling mechanics governing the EDF algorithms has been pursued. The dominance of the pre-stipulated deadline of the EDF algorithm is indeed acknowledged in the proposed and developed enhanced algorithm. The simulation results indicate that the legacy algorithms are not suitable for the multi-class traffic systems of WiMAX. This is because these algorithms do not explicitly incorporate the WiMAX QoS parameters into their mechanisms and are highly static. Extensive discrete – event simulation experiments have been done for the purpose of performance analysis. The performance metrics used are average throughput, average delay, missed deadline ratio and average queue size utilization ratio. The acquired results have proven that the proposed algorithms have successfully enhanced the static constrained algorithm.

Optimal energy efficient packet scheduling with arbitrary individual deadline guarantee

Given a rate adaptive wireless transmitter, a challenging problem is to design a rate control policy for it such that the energy consumption is minimized at transmitting a set of dynamically arrived packets with arbitrary individual deadlines. In a decade, researches have partially made progress on this topic. A latest work offers an optimal algorithm that allows packets to have arbitrary deadlines but requires them to follow the order they arrive. This paper first presents the Densest Interval First (DIF) policy which repeatedly locates the densest data interval and determines its transmission rate. This policy is proved to be optimal for the most general model that allows arbitrary arrival times as well as arbitrary deadlines. Then, this paper presents a simple EDF (earliest deadline first) algorithm to actually schedule the transmission time for each packet. It is proved that the EDF always guarantees every packet to complete transmission before its deadline with minimum energy consumption which is computed and required by DIF. Finally, this paper also proposes a novel online policy named Density Guided Cooling (DGC) policy which models Newton’s Law of Cooling. Simulations show that online DGC policy constantly produces a rate scheduling that on average consumes energy within 110% of the minimum value obtained by the offline DIF.

Energy Saving EDF Scheduling for Wireless Sensors on Variable Voltage Processors

Advances in micro technology has led to the development of miniaturized sensor nodes with wireless communication to perform several real-time computations. These systems are deployed wherever it is not possible to maintain a wired network infrastructure and to recharge/replace batteries and the goal is then to prolong as much as possible the lifetime of the system. In our work, we aim to modify the Earliest Deadline First (EDF) scheduling algorithm to minimize the energy consumption using the Dynamic Voltage and Frequency Selection. To this end, we propose an Energy Saving EDF (ES-EDF) algorithm that is capable of stretching the worst case execution time of tasks as much as possible without violating deadlines. We prove that ES-EDF is optimal in minimizing processor energy consumption and maximum lateness for which an upper bound on the processor energy saving is derived. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. Experimental results show that ES-EDF outperforms EDF and Enhanced EDF (E-EDF) algorithms in terms of both percentage of feasible task sets and energy savings.

Novel bandwidth allocation and packet scheduling strategy for WiMAX

IEEE 802.16 and its variants provide Quality of Service (QoS) by dividing the data traffic into five service classes governed by QoS parameters. Efficient bandwidth allocation algorithm and packet scheduling algorithms ensure that the desired QoS requirements are met. In this paper, a novel bandwidth allocation algorithm and a packet scheduling algorithm are proposed that regulate bandwidth intensive connections by applying three–strike technique. The proposed algorithms ensure that the bandwidth is distributed effectively among the conforming connections thereby improving the overall QoS of the network. Simulation results of the proposed algorithm, when compared with standard algorithms like Earliest Deadline First (EDF), reveal that the proposed algorithm significantly increases the throughput and decreases the data drop for the conforming connection as compared to the EDF algorithm. Throughput improvements and data drop reduction are seen for intra–class inter–MS connections as well as inter–class intra–MS connections.

Adaptive EDF

Along with the growing diversity and complexity of real-time embedded systems, significance of real-time scheduling is increasing. Rate monotonic (RM) and earliest deadline first (EDF) are representative scheduling algorithms for periodic tasks. RM has a merit that tasks with high-priority (short-period) have small jitters and short response times. However, it is impossible that processor utilization reaches 100% while maintaining schedulability or that tasks are given importance independent of their periods. On the other hand, EDF can utilize processors by 100% with schedulability, while it cannot give tasks fixed priorities or importance, therefore, it is difficult to keep jitters or response times of particular tasks short. This paper, in EDF algorithm, proposes a method of shortening response times of a task that is important for the system by introducing predictive execution times and dividing execution of the important task into two instances based on the predictive execution times. The use of predictive execution times has an effect of making deadline of the first instance earlier and therefore the first instance would be executed earlier by EDF. In the evaluation by simulation, the proposed method reduced average response times of the most important task by up to 14.2%.

Aggregation of EDF and ACO for Enhancing Real Time System Performance

Time constraint is the main factor in real time operating system and it affects the deadline of the process. To achieve deadline, proper scheduling algorithm is required to schedule the task. In this paper an Adaptive scheduling algorithm is developed which is the combination of Earliest Deadline First (EDF) and Ant Colony Optimization (ACO). The EDF algorithm places the process in a priority queue and executed using the deadline. The priority of the processes depends upon the deadline and handles the under loaded condition. The limitation of EDF algorithm is that it cannot handle the overloaded condition. The execution of ACO algorithm is based on the execution time. Process which contains the minimum execution time is executed first. The limitation of ACO algorithm is, it takes more time for execution than EDF. Therefore, to remove the limitation of both the algorithms an Adaptive scheduling algorithm is developed. It increases performance of the system and decreases the system failure. Also the percentage of missing deadline is reduced. The advantage of an Adaptive scheduling algorithm is, it handles over-loaded and under-loaded condition simultaneously. The performance of an Adaptive scheduling algorithm is calculated in terms of Success Ratio that is the number of process scheduled and CPU Utilization. The result of execution time is compared with the EDF and ACO scheduling algorithm. The goal of an Adaptive scheduling algorithm is to show the switching between the scheduling algorithms and to decrease the system failure and increase the system performance.