Energy-efficient Scheduling Approach Research Articles

Multipath TCP Meets Reinforcement Learning: A Novel Energy-Efficient Scheduling Approach in Heterogeneous Wireless Networks

Multipath TCP (MPTCP) has been standardized by the IETF as an extension of conventional TCP and it allows the system to utilize multiple paths simultaneously, which can aggregate bandwidth to improve network throughput. However, MPTCP needs to open multiple interfaces at the same time, which makes MPTCP consume more energy to maintain multiple interface connections. Thus, how to manage subflows with the MPTCP's scheduling system to determine which paths should be used for data transmission is of critical importance to reduce energy consumption and ensure network throughput. Due to the path heterogeneity and random packet losses in wireless networks, existing scheduling systems, and selecting paths based on the path's delay or energy cost, may suffer from performance degradation. In this article, we propose a reinforcement learning-based multipath scheduler called MPTCP-RL to determine the optimal path set for different flows. MPTCP-RL adopts deep reinforcement learning as well as MPTCP transmission model to manage path usage among multiple connections to make sure that the sender can adaptively select the optimal path set for a certain application according to the current network environment. MPTCP-RL is an asynchronous reinforcement learning framework, which separates the processes of offline training and online decision to ensure that the learning process will not introduce extra delay and overhead on the decision making process in MPTCP path management. The extensive experimental results show that MPTCP-RL can improve the aggregate throughput and reduce energy consumption significantly compared to the state-of-the-art mechanisms in a variety of network scenarios.

Research and Application of Energy-Efficient Management Approach for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are widely used in industrial applications. However, many of them have limited lifetimes, which has been a considerable constraint on their widespread use. As a typical application of WSNs, distributed measurement of the electric field under high-voltage direct-current (HVDC) transmission lines also suffers from this issue. This paper first introduces the composition of the electric-field measurement system (EFMS) and its working principle. Considering the actual power supply of the system, this paper mainly introduces the composition of the wireless sensor node (WSND) and analyzes the power consumption and potential working state transformation of the WSND, together with a comprehensive study on parameters affecting the power consumption of the wireless communication unit. Moreover, an energy-efficient scheduling approach is proposed after specially designing a working sequence and the study on system parameters. The proposed approach is verified by experiments on not only the experimental line of the national HVDC test base, but also a commercial operation HVDC transmission line with the challenge of long endurance, which is considered in this paper with a new strategy. The results show that the proposed method can greatly extend the lifetime of the WSND.

COMPREHENSIVE METHOD OF ENERGY-EFFICIENT WORKLOAD PROCESSING IN THE INFORMATION AND COMMUNICATION NETWORK

Background. Peculiarities of the workload in a modern information and communication network (ICN) determine specific requirements for energy efficiency, performance and availability of its processing system. Existing approaches to increase energy efficiency and performance of workload processing do not take into account the possibility of dynamic changes in ICN workload arrival rate and individual energy consumption characteristics of computing nodes of the system.
 Objective. The purpose of the paper is to increase the energy efficiency and performance of ICN workload processing while meeting the requirements for the availability of the processing system, taking into account dynamic changes in the input workload arrival rate and the individual characteristics of the computing nodes’ energy consumption.
 Methods. A mathematical model of the workload processing system was built using the queueing theory methods, and an ontological model of this system was built using intelligent data analysis methods, which made it possible to quantitatively and qualitatively describe the complex relationships between system parameters and workload processing efficiency indicators. On the basis of the built models, a comprehensive method of energy-efficient workload processing was proposed, which differs from the known ones by the use of individual energy consumption models of computing nodes, combining the advantages of horizontal scaling and energy-efficient scheduling taking into account dynamic changes in workload arrival rate.
 Results. The efficiency of ICN workload processing is increased by 15.722% according to the efficiency criterion, which includes energy efficiency and performance indicators, compared to the known energy-efficient Backfill approach while meeting the requirements for the availability of the processing system.
 Conclusions. The energy efficiency, performance and availability of the ICN workload processing system can be improved by combining horizontal scaling and energy-efficient scheduling approaches using individual energy consumption models of computing nodes and taking into account dynamic changes in the input workload arrival rate.

An energy-efficient scheduling approach for memory-intensive tasks in multi-core systems

An energy-efficient scheduling approach for memory-intensive tasks in multi-core systems

A DVFS-Weakly Dependent Energy-Efficient Scheduling Approach for Deadline-Constrained Parallel Applications on Heterogeneous Systems

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

Urgent point aware energy-efficient scheduling of tasks with hard deadline on virtualized cloud system

Cloud computing platform has emerged to be a promising computing paradigm of recent time. Various applications from different domains having rigid deadline constraints are deployed in the cloud system for their respective benefits. Energy-efficient execution of these applications, meeting their deadline constraints is a challenge. Most of the existing research on the energy-efficient scheduling of these applications in the cloud domain consider a linear relationship between the energy consumption and the resource utilization of the system, and they focus on maximizing the utilization of resources to reduce the active number of computing nodes to minimize energy consumption. In this paper, we first devise a power consumption model for the cloud system which considers both the static and dynamic components of it and assumes a nonlinear relationship with utilization. Then we introduce the concept of urgent points in case of tasks having deadline in the context of a heterogeneous cloud computing environment. Then we propose two energy-efficient scheduling approaches, named UPS and UPS-ES designed based on the urgent points of the tasks and two threshold values of the host utilization. Extensive simulation experiments are conducted both for synthetic tasksets and Google cloud tracelogs. The results are compared with a state of the art scheduling policy and found that our policies perform significantly better than them, with an energy reduction of up to 42% while the deadline constraints of all the tasks are met.

An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment

This paper addresses the energy-efficient scheduling and real-time control of flexible job shop that requires rescheduling affected operations and updating the scheduling. For energy-efficient scheduling shop floor efficiently, we propose a metaheuristic algorithm called PN-ACO algorithm, which combines a timed transition Petri nets (TTPN) based representation tool and an ant colony optimization (ACO) heuristic search method. To address the real-time control problem in energy-efficient scheduling of the shop floor, we apply the Internet of Things (IoT) technology to product production to form an Internet of Manufacturing Things environment (IoMT). In the IoMT environment, there are usually many abnormal event disturbances, including machine breakdown and urgent order arrival. To quickly handle the disturbance problem of abnormal events, the distributed control system architecture is proposed, the core of which is the negotiation and cooperation between manufacturing resources based on the wireless communication network. The proposed approach is further illustrated by a case energy-efficient of scheduling for a flexible job shop through which the optimal scheduling and correct supervisory control instructions can be obtained easily and quickly. In sum, the proposed optimization algorithm obtains a good effect in engineering applications while the validity of optimization is proved.

An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment

Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.

An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

Regenerative braking is an energy recovery mechanism that converts the kinetic energy during braking into electricity, also known as regenerative energy. In general, most of the regenerative energy is transmitted backward along the pantograph and fed back into the overhead contact line. To reduce the trains’ energy consumption, this paper develops a scheduling approach to coordinate the arrivals and departures of all trains located in the same electricity supply interval so that the energy regenerated from braking trains can be more effectively utilized to accelerate trains. Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains’ energy consumption with dwell time control. Secondly, we design a genetic algorithm and an allocation algorithm to find a good solution. Finally, we present numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results show that the proposed scheduling approach can reduce energy consumption by 6.97% and save about 1,054,388 CNY (or 169,223 USD) each year in comparison with the current timetable. Compared to the cooperative scheduling (CS) approach, the proposed scheduling approach can improve the utilization of regenerative energy by 36.16% and reduce the total energy consumption by 4.28%.

Energy-aware service provisioning in volunteers clouds

The goal pursued in this study is to optimise the service provisioning and the energy consumed in a special existing cloud system named SlapOS. The SlapOS cloud innovates in considering that the data centre can be composed by dedicated and volunteer machines. We will use the term volunteer clouds for referring to such organisations. Volunteer clouds offer potential advantages for cloud elasticity, storage of big data and the minimisation of the energy consumption. But, we must manage the potential unavailability of volunteer machines. In this paper, we focus on the design of energy-efficient scheduling approaches in such clouds. Our first contribution is to formulate the scheduling challenges in two new computational problems. These problems are NP-hard. Then, we propose an integer linear programming (ILP) formulation and several greedy heuristics. Finally, we evaluate our approaches throughout various simulations of the SlapOS system in a realistic volunteer computing context.

Leakage-aware scheduling for embedded real-time systems with (m, k)-constraints

In this paper, we study the problem of reducing both the dynamic and leakage energy consumption for real-time systems with (m, k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. Two energy efficient scheduling approaches incorporating both dynamic voltage scheduling (DVS) and dynamic power down (DPD) are proposed in this paper. The first one statically determines the mandatory jobs that need to meet their deadlines in order to satisfy the (m, k)-constraints, and the second one does so dynamically. The simulation results reveal that, with more accurate workload estimation, our proposed techniques outperformed previous research in both overall and idle energy reduction while providing the (m, k)-guarantee.

Rolling-horizon scheduling for energy constrained distributed real-time embedded systems

Energy-efficient scheduling approaches are critical to battery driven real-time embedded systems. Traditional energy-aware scheduling schemes are mainly based on the individual task scheduling. Consequently, the scheduling space for each task is small, and the schedulability and energy saving are very limited, especially when the system is heavily loaded. To remedy this problem, we propose a novel rolling-horizon (RH) strategy that can be applied to any scheduling algorithm to improve schedulability. In addition, we develop a new energy-efficient adaptive scheduling algorithm (EASA) that can adaptively adjust supply voltages according to the system workload for energy efficiency. Both the RH strategy and EASA algorithm are combined to form our scheduling approach, RH-EASA. Experimental results show that in comparison with some typical traditional scheduling schemes, RH-EASA can achieve significant energy savings while meeting most task deadlines (namely, high schedulability) for distributed real-time embedded systems with dynamic workloads.

Energy-Efficient Mobile Data Uploading from High-Speed Trains

Recent decades have witnessed the fast development of high-speed railway systems in many countries, which have significantly shortened the travel time between distant cities. Accompanied with this convenience is the challenge for cell phone vendors to provide broadband Internet access for passengers, particularly considering the fast changing channel conditions in high-speed trains and the limited battery of cell phones, which often cannot be re-charged in trains. In this paper, inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient scheduling approach for uploading data from cell phones, both with soft deadlines (e.g., documents) and hard deadlines (e.g., video streaming). Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario, and smartly adjusts the transmission rate to maximize the overall data transmission rate and yet conserves the energy consumption. Performance evaluation based on realistic railway scenarios and H.264 video traces demonstrate the effectiveness of our solution and its superiority as compared to the existing solutions.