Two-phase Scheduling Research Articles

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

Socio-economic advancement and grid-frequency control of a wind farm-PHS-solar connected EV hybrid electrical system considering imbalance prices

The Independent System Operator (ISO) plays a key role in a competitive electrical system by controlling the market with the received bidding from the different market participants. The ISO settled the energy at the location-based marginal pricing (LBMP) where all the participants get economic benefits. In the renewable combined system, the future renewable power production data need to be submitted to the ISO at least one day before the operation. There is always a chance for not fulfilling the contracted power due to the volatile nature of the renewable which creates an imbalance price in the system. In this situation, energy storage systems can alleviate the imbalance issues by supplying additional power to the electrical grid. This study proposes a two-phase scheduling technique for the optimum operation of a wind farm (WF)–pumped hydro storage (PHS)–solar-connected electric vehicle (EV) hybrid system to obtain more economic profit and revenue with a stable grid frequency. In the first phase, the energy level of PHS upper reservoirs has been scheduled to maintain the grid frequency with the presence of a wind farm. Along with the first phase, additionally solar-connected EVs are incorporated in the second phase to maximize the system profit further to get more economic benefit. A modified IEEE 30 bus system is used to effectively assess the aforesaid approach. MiPower software has been used to conduct this work. This proposed method has been compared with the existing method and got better results in all aspects.

Vehicle Dispatch and Route Optimization Algorithm for Demand-Responsive Transit

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling.

An online algorithm for scheduling big data analysis jobs in cloud environments

Cloud computing has become a popular platform for processing big data analysis jobs with its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis service providers use cloud instances to process users’ big data analysis job execution requests and they need efficient scheduling algorithms to improve job execution efficiency and economic benefits. This paper presents a problem of minimizing the execution time of a batch of big data analysis jobs without changing the number of cloud instances. Solving this problem can not only improve big data job execution efficiency in cloud environments and user satisfaction, but also bring higher economic benefits to big data analysis service providers. This paper proposes an online scheduling algorithm, which can make full use of the parallelism of big data analysis jobs to optimize job scheduling decisions on the premise that the job execution time cannot be accurately known. For evaluating the performance of the proposed online scheduling algorithm, a traditional two-phase scheduling algorithm is introduced as a benchmark for comparison in this paper. Theoretical analysis and extensive simulation experiments based on real datasets show that the online scheduling algorithm proposed in this paper can achieve more stable performance compared with the benchmark two-phase scheduling algorithm.

Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS

This study aims to build a machine scheduling method that involves the cutting tool management in parallel-type flexible manufacturing systems. These systems consist of multi-axis CNC machine tools and are equipped with an automated tool changer and a large-capacity tool magazine. The target scheduling problem could be described as a multi-objective parallel scheduling problem. We consider the availability of cutting tools stored in the magazine as so-called “machine eligibility,” and propose a two-phase scheduling method for tool allocation and job sequencing on machines to minimize the workload balance between machines and the total completion time. Two mathematical models for tool allocation are provided: a machine-eligibility-based model and an enhanced version of the model that considers each cutting tool. A series of computational experiments demonstrates the effectiveness of the proposed method. We also clarify the relationship between schedule performance measures and job routing flexibility in the system.

Integrated Scheduling of Real-Time and Interactive Tasks for Configurable Industrial Systems

With the recent advances in Internet of Things and cyber-physical systems technologies, smart industrial systems support configurable processes consisting of human interactions as well as hard real-time functions. This implies that irregularly arriving interactive tasks and traditional hard real-time tasks coexist. As the characteristics of the tasks are heterogeneous, it is not an easy matter to schedule them all at once. To cope with this situation, this article presents a new task scheduling policy that uses the notion of “virtual real-time task” and two-phase scheduling. As hard real-time tasks must keep their deadlines, we perform offline scheduling based on genetic algorithms beforehand. This determines the processor's voltage level and memory location of each task and also reserves the virtual real-time tasks for interactive tasks. When interactive tasks arrive during the execution, online scheduling is performed on the time slot of the virtual real-time tasks. As interactive workloads evolve over time, we monitor them and periodically update the offline scheduling. Experimental results show that the proposed policy reduces the energy consumption by 66.8% on average without deadline misses and also supports the waiting time of less than 3 (s) for interactive tasks.

The two-phase scheduling based on deep learning in the Internet of Things

With the growth of data generation speed and its huge volume, the cloud-based infrastructure alone does not meet the needs of the Internet of Things (IoT), thereby leading to inefficiency. By combining fog and cloud computing on the IoT, some processing occurs near the data generation location with a higher speed and without needing a large bandwidth. Cloud and fog computing requires proper management in scheduling, increasing resource efficiency, and reducing consumption costs. In this article, a two-phase scheduling algorithm is presented based on deep learning methods in the field of IoT. The first phase is to decide on the location of the task execution using the clustering method. The second phase involves scheduling the task according to the execution location. In the clustering section, three ideas based on the Self-Organizing Map (SOM) clustering method have been proposed. In the first and second ideas, the SOM and hierarchical SOM are used to cluster the features of the tasks received from the IoT layer. In the third idea, the feature is extracted, and its dimensions are reduced using the Autoencoder, which is one of the deep learning methods, after which clustering is done. After scheduling each cluster's tasks, comparing the methods presented in the article, it is shown that the feature extraction using deep learning can improve clustering in such a way to reduce the missed rate of tasks in the cloud and fog, as well as their costs.

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.

Two-Phase Scheduling for Efficient Vehicle Sharing

Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

A QoE-Aware Service-Enhancement Strategy for Edge Artificial Intelligence Applications

Due to the high complexity of artificial intelligence (AI) algorithms, performing the AI tasks on the resource-limited Internet-of-Things (IoT) devices has been proved to be inadvisable. Edge computing provides an effective computing paradigm for executing AI tasks, where large numbers of AI tasks can be offloaded to the edge servers. Most of the existing works focus on achieving efficient computing offload through improving the Quality of Service (QoS), such as reducing the average server-side delay. However, we show that those efforts are inefficient due to the heterogeneous impact of delays on users’ Quality of Experience (QoE). Inspired by the observations, in this article, we reconsider the scheduling method from an orthometric perspective, i.e., improving the QoE by designing a QoE-aware service-enhancement strategy for edge AI applications. Besides, multiple AI algorithms are utilized in our service model to execute the same type of tasks concurrently, thus meeting users’ heterogeneity requirements of accuracy and delays. Specifically, for the online arriving AI tasks, we optimize the task allocation and scheduling strategy according to the QoE sensitivity of each task. The model can be formulated as the mixed-integer nonlinear programming problem, which is known to be NP-hard. Hence, we then propose an efficient two-phase scheduling strategy for this problem. The results of comprehensive emulations validate that our model can effectively improve the average QoE of users and achieve a higher task completion ratio.

Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment

Cloud computing is a new paradigm which provides subscription-oriented services. Scheduling the tasks in cloud computing environments is a multi-goal optimization problem, which is NP hard. To exaggerate task scheduling performance and reduce the overall Makespan of the task allocation in clouds, this paper proposes two scheduling algorithms named as TBTS (Threshold based Task scheduling algorithm) and SLA-LB (Service level agreement-based Load Balancing) algorithm. TBTS is two-phase scheduling algorithm which schedules the tasks in a batch. It supports task scheduling in virtual machines with distinct configuration. Furthermore, in TBTS algorithm, threshold data generated based on the ETC (Expected Time to Complete) matrix. Virtual machines which execute tasks with the estimated execution time lesser than threshold value are allocated to the particular task. SLA-LB algorithm is a online model which schedules the task dynamically, based on the requirement of clients, like deadline and budget as the two criteria. Prediction based scheduling is implemented in TBTS to increase the system utilization and to improve the load balancing among the machines by allocation of the minimum configuration machine to the task, based on predicted robust threshold value. SLA-LB uses the level of agreement and finds the required system to reduce the Makespan and increase the cloud-utilization. Simulation of proposed algorithms is performed with benchmark datasets (Braun, 2015) and synthetic datasets are generated with random functions. The proposed TBTS and SLA-LB final values of the proposed algorithms are analyzed with assorted scheduling models, namely SLA-MCT, FCFS, EXIST, LBMM, Lbmaxmin, MINMIN and MAXMIN algorithms. Performance metrics such as Makespan, penalty, gain cost and also the VM utilization factor of proposed algorithm compared with existing algorithms. The comparison analysis among various existing algorithms with TBTS and SLA-LB algorithms show that the proposed methods outperform existing algorithms, even in the scalability situation of the dataset and virtual machines.

A cubic chromosome representation for patient scheduling in the Emergency Department

In healthcare institution management, hospital flow control and the prediction of overcrowding are major issues. The objective of the present study is to develop a dynamic scheduling protocol that minimizes interference between scheduled and unscheduled patients arriving at the emergency department (ED) while taking account of disturbances that occur in the ED on a daily basis. The ultimate goal is to improve the quality of care and reduce waiting times via a two-phase scheduling approach. In the first phase, we used a genetic algorithm (based on a three-dimensional cubic chromosome) to manage scheduled patients. In the second phase, we took account of the dynamic, uncertain nature of the ED environment (the arrival of unscheduled patients) by continuously updating the schedule.

Integrated Scheduling of Reheating Furnace and Hot Rolling Based on Improved Multiobjective Differential Evolution

In the hot rolling mill, a cold slab is reheated in reheating furnace and then rolled into thin coils through hot rolling process. Traditional research on hot rolling mill generally takes the two processes as two separate single-objective scheduling problems, though they are closely connected and their optimization objectives are conflicting with each other. In this paper, the integrated scheduling of the reheating furnace and hot rolling is investigated in the view of multiobjective optimization. A mixed-integer programming model with two objectives is formulated for this problem, and a multiobjective differential evolution (MODE) algorithm is developed to solve this model. To make it easy for algorithm design, a hot rolling schedule (i.e., a sequence of slabs) is used to represent a solution and for a given solution, a heuristic with consideration of energy consumption minimization is presented to obtain the schedule of reheating furnace. To achieve the balance of exploration and exploitation, a novel mutation operator with adaptive leader selection, a crossover operator with feasibility consideration, and a guided multiobjective local search are designed. Computational results based on simulated data illustrate that the integrated scheduling is superior to the traditional two-phase scheduling method used in practice and literature. Further results illustrate that the proposed MODE algorithm is effective and superior to some other multiobjective evolutionary algorithms.

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.

Cost-Efficient Scheduling for Deadline Constrained Grid Workflows

Cost optimization for workflow scheduling while meeting deadline is one of the fundamental problems in utility computing. In this paper, a two-phase cost-efficient scheduling algorithm called critical chain is presented. The proposed algorithm uses the concept of slack time in both phases. The first phase is deadline distribution over all tasks existing in the workflow which is done considering critical path properties of workflow graphs. Critical chain uses slack time to iteratively select most critical sequence of tasks and then assigns sub-deadlines to those tasks. In the second phase named mapping step, it tries to allocate a server to each task considering task's sub-deadline. In the mapping step, slack time priority in selecting ready task is used to reduce deadline violation. Furthermore, the algorithm tries to locally optimize the computation and communication costs of sequential tasks exploiting dynamic programming. After proposing the scheduling algorithm, three measures for the superiority of a scheduling algorithm are introduced, and the proposed algorithm is compared with other existing algorithms considering the measures. Results obtained from simulating various systems show that the proposed algorithm outperforms four well-known existing workflow scheduling algorithms.

REWIMO

Industrial applications and cyber-physical systems rely on real-time wireless networks to deliver data in a timely and reliable manner. However, existing solutions provide these guarantees only for stationary nodes. In this article, we present REWIMO, a solution for real-time and reliable communications in mobile networks. REWIMO has a two-tier architecture composed of (i) infrastructure nodes and (ii) mobile nodes that associate with infrastructure nodes as they move. REWIMO employs an on-join bandwidth reservation approach and benefits from a set of techniques to efficiently reserve bandwidth for each mobile node at the time of its admission and over its potential data forwarding paths. To ensure association of mobile nodes with infrastructure nodes over high-quality links, REWIMO uses the two-phase scheduling technique to coordinate neighbor discovery with data transmission. To mitigate the overhead of handling network dynamics, REWIMO employs an additive scheduling algorithm, which is capable of additive bandwidth reservation without modifying existing schedules. Compared to the algorithms used by static real-time wireless networks, the techniques and the algorithms employed by REWIMO result in a significant increase in real-time capacity, enhanced reliability, and considerably faster handling of network dynamics.

Towards two-phase scheduling of real-time applications in distributed systems

In this work we propose a two-phase scheduling technique (TOPS) for distributed real-time systems. Our TOPS scheduling approach has two distinct phases. The first phase is in charge of producing a scheduling sequence, whereas the second phase aims to dispatch tasks to computing nodes of a distributed system. The second phase also judiciously determines the starting time of each task. One salient feature of our approach lies in high flexibility, which allows system developers to apply multiple policies in each phase. The two phases are independent of one another; therefore, one can change a policy in one phase without configuring another phase. With TOPS in place, we are able to observe the impacts of sorting policies on the performance of scheduling policies. We implement a prototype of TOPS, where the first phase is comprised of three sorting policies, and the second phase consists of two scheduling policies. TOPS enables us to discover that combining the EDF (earliest deadline first) and AEAP (as early as possible) policies leads to an optimized performance among all six candidate algorithms.

SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

Service-level agreement (SLA) is a major issue in cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-cloud environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-cloud environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms.

A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %.

Energy-efficient scheduling algorithm for real-time job set

Purpose – The purpose of this paper is to investigate the scheduling problem of real-time jobs executing on a DVS processor. The jobs must complete their executions by their deadlines and the energy consumption also must be minimized. Design/methodology/approach – The two-phase energy-efficient scheduling algorithm is proposed to solve the scheduling problem for real-time jobs. In the off-line phase, the maximum instantaneous total density and instantaneous total density (ITD) are proposed to derive the speed of the processor for each time instance. The derived speeds are saved for run time. In the on-line phase, the authors set the processor speed according to the derived speeds and set a timer to expire at the corresponding end time instance of the used speed. Findings – When the DVS processor executes a job at a proper speed, the energy consumption of the system can be minimized. Research limitations/implications – This paper does not consider jobs with precedence constraints. It can be explored in the further work. Practical implications – The experimental results of the proposed schemes are presented to show the effectiveness. Originality/value – The experimental results show that the proposed scheduling algorithm, ITD, can achieve energy saving and make the processor fully utilized.