Energy-efficient Scheduling Algorithm Research Articles

Energy-aware fault-tolerant scheduling for imprecise mixed-criticality systems with semi-clairvoyance

Integrating tasks with different functions and criticality levels onto the same hardware platform forms mixed-criticality systems (MCSs). Semi-clairvoyant scheduling, in which the mode transition is revealed at the arrival of high-criticality tasks, has attracted researchers’ attention. Most studies focus on the schedulability test of MCS and do not consider energy consumption. In this paper, we simultaneously consider fault tolerance, energy management, and semi-clairvoyant scheduling in MCS. Firstly, we propose a schedulability test based on compensating adaptive mixed-criticality called CAMC-sem-cp. Secondly, we propose an energy-efficient scheduling algorithm based on the CAMC-sem-cp, called EE-CAMC-sem-cp. Finally, we conduct experiments to evaluate the performance of the proposed algorithms. The experimental results show that EE-CAMC-sem-cp can save 47.84% of the energy consumption compared to CAMC-sem-cp.

A knowledge-guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem

With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming widespread due to the exploration of the business. Thus, this paper investigates the energy-efficient scheduling of the distributed flexible job shop problem (EEDFJSP) with the goal of minimizing the makespan and total energy consumption (TEC). Considering the difficulty of simultaneously optimizing both objectives, a knowledge-guided bi-population evolutionary algorithm (KBEA) is proposed to address this issue. Firstly, a problem-specific initialization strategy based on a four-vector representation is presented, which corresponds to four sub-problems including factory assignment, operation sequence, machine assignment, and speed assignment. Secondly, five different types of evolutionary operators with adaption strategy is designed to guide the bi-population to complete efficient evolution. Thirdly, a knowledge-guided local search strategy is used to enhance the exploitation capability of the algorithm. Furthermore, an elaborately-designed energy-saving strategy based on knowledge is developed to further reduce energy consumption. Additionally, to verify the effectiveness of the proposed KBEA, extensive experiments are conducted to compare with other 7 comparison algorithms on 39 instances. Experimental results manifest that KBEA is superior to its competitors.

A Novel Evolutionary Algorithm for Energy-Efficient Scheduling in Flexible Job Shops

Improving productivity at the expense of heavy energy consumption is often no longer possible in modern manufacturing industries. Through efficient scheduling technologies, however, we are able to still maintain high productivity while reducing energy costs. This paper addresses a flexible job shop scheduling problem under Time-Of-Use electricity tariffs with the objective of minimizing total energy consumption while considering a predefined makespan constraint. We propose a novel two-individual-based evolutionary (TIE) algorithm, which incorporates several distinguishing features such as a tabu search procedure, a topological order based recombination operator, a new neighborhood structure for this specific problem, and an approximate neighborhood evaluation method. Extensive experiments are conducted on widely used benchmark instances, which show that the proposed TIE outperforms traditional trajectory-based and population-based methods. We also analyze the key features of TIE to identify its critical success factors, and discuss the impact of varying key parameters of the problem to derive practical insights.

A novel carbon reduction engineering method-based deep Q-learning algorithm for energy-efficient scheduling on a single batch-processing machine in semiconductor manufacturing

The semiconductor industry is a resource-intensive sector that heavily relies on energy, water, chemicals, and raw materials. Within the semiconductor manufacturing process, the diffusion furnace, ion implantation machine, and plasma etching machine exhibit high energy demands or operate at extremely high temperatures, resulting in significant electricity consumption, which is usually carbon-intensive. To address energy conservation concerns, the industry adopts batch production technology, which allows for the simultaneous processing of multiple products. The energy-efficient parallel batch scheduling problem arises from the need to optimise product grouping and sequencing. In contrast to existing heuristics, meta-heuristics, and exact algorithms, this paper introduces the Deep Q-Network (DQN) algorithm as a novel approach to address the proposed problem. The DQN algorithm is built upon the agent’s systematic learning of scheduling rules, thereby enabling it to offer guidance for online decision-making regarding the grouping and sequencing of products. The efficacy of the algorithm is substantiated through extensive computational experiments.

Energy-Efficient Scheduling for Search-Space Periods in NB-IoT Networks

One of the significant properties of narrowband Internet of Things (NB-IoT) devices is supporting a long battery life. In the NB-IoT, devices have different search-space periods based on channel qualities. However, because a subframe of 180 kHz is a basic unit in the downlink, a base station can generally only simultaneously schedule devices with the same search-space period and allocate radio resources at the start of the period. If scheduling does not consider search-space periods, a base station will miss the start of the search-space period of some devices or sacrifice some devices. Massive devices will attempt multiple periods for blind decoding (BD), resulting in huge energy consumption. This article investigates a new direction in reducing the energy consumption of devices by reducing the BD and idle time for the NB-IoT. The challenge of the target energy-efficient scheduling problem is how to schedule search-space periods to reduce each device's BD and idle time subject to the data requirements. We propose an algorithm to determine each device's search-space period and an energy-efficient scheduling algorithm to schedule the search-space periods. The simulation results show that, compared with two baselines, the proposed algorithms reduce the energy consumption by 77%.

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.

A cooperative whale optimization algorithm for energy-efficient scheduling of the distributed blocking flow-shop with sequence-dependent setup time

The distributed blocking flow-shop sequence-dependent scheduling problem (DBFSDSP) with the production efficiency measures has been extensively concerned as its wide industrial applications. Whereas, energy efficiency indicators are often ignored in the literature. The DBFSDSP considered makespan, total tardiness, and total energy consumption is investigated in this paper. A cooperative whale optimization algorithm (CWOA) is proposed to solve the DBFSDSP as the complexity of the distributed and multi-objective optimization. First, the critical path of DBFSDSP is defined. An energy-saving operation for the non-critical path is designed to reduce the energy consumption of the system. Second, an accelerated operation for the critical path is designed to reduce the makespan and total tardiness. Third, three acceptance criteria for multi-objective optimization are proposed to improve the diversity of the population. The statistical and computational experimentation in an extensive benchmark testified that the CWOA outperforms the state-of-the-art algorithms regarding efficiency and significance in solving DBFSDSP.

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

An Energy-Efficient Scheduling Algorithm With SIC and Power Control in WSNs

In large-scale wireless sensor networks (WSNs), spectrum efficiency and energy consumption are two challengeable problems to be solved due to the increasing traffic requirement and its limited energy. Successive interference cancellation (SIC) technology, which supports multiple parallel transmissions, gives an opportunity to improve the efficiency of data collection. However, those classical schemes suffer from high energy consumption, which presents a severe challenge to energy-constrained WSNs. To improve the transmission and energy efficiency of WSNs, we study an energy-efficient scheduling algorithm joint with SIC and power control in this paper. We first develop a non-uniform M-Level scheduling model to identify the beneficial opportunities and guarantee the transmission quality of SIC concurrency. Then, we formulate an energy efficiency maximization scheduling (EEMS) utility and further transform it into a combinatorial optimization problem. To reduce the computational complexity and obtain meaningful insights, two low-complexity optimization algorithms are proposed from the perspective of SIC scheduling and power control, respectively. Finally, extensive results demonstrate that the SIC scheduling design enables our proposed algorithm to achieve 51% higher throughput than the algorithm without SIC. Moreover, the energy efficiency of our proposed algorithm is improved by 37% compared with the existing algorithms, which indicates that our algorithm can search for the optimal configuration of SIC-concurrent transmissions and better manage the concurrent transmission power.

A Cost Effective and Energy Efficient Algorithm for Cloud Computing

Cloud-Computing offers high performance solution to solve complex engineering and scientific tasks by deploying resources at geo-diverse locations. With the large-scale demand of scientific and engineering jobs, huge number of cloud data centres needs to be constructed to fulfil the requirement of the jobs. The extensive use of cloud data centres leads to increases in cost as well as energy consumption. In this paper, an agent based Cost-Effective Energy Efficient scheduling algorithm, namely, CEEE has been proposed. To establish the effectiveness of the proposed algorithm, simulation is performed on CloudSim environment. The proposed algorithm is compared with state of art scheduling algorithms namely, Randam and MaxUtil. The experimental results demonstrates that CEEE algorithm is capable of refining energy efficiency with reduced cost. The proposed algorithm outperforms the prevailing algorithms in terms of energy consumption, resource utilization, number of hosts in sleep mode and completion time.

Energy-efficient scheduling algorithms based on task clustering in heterogeneous spark clusters

Spark is widely used for its fast in-memory processing. It is important to improve energy efficiency under deadline constrains. In this paper, a Task Performance Clustering of Best Fitting Decrease (TPCBFD) scheduling algorithm is proposed. It divides tasks in Spark into three types, with the different types of tasks being placed on nodes with superior performance. However, the basic computation time for TPCBFD takes up a large proportion of the task execution time, so the Energy-Aware TPCBFD (EATPCBFD) algorithm based on the proposed energy consumption model is proposed, focusing on optimizing energy efficiency and Service Level Agreement (SLA) service times. The experimental results show that EATPCBFD increases the average energy efficiency in Spark by 77% and the average passing rate of SLA service time by 14% compared to comparison algorithms. EATPCBFD has higher energy efficiency on average than comparison algorithms under deadline. The average energy efficiency of EATPCBFD with the deadline constraint is higher than the comparison algorithm.

Retraction Note to: Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital

Retraction Note to: Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital

A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (TEC). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions’ quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments.

A Pareto-based collaborative multi-objective optimization algorithm for energy-efficient scheduling of distributed permutation flow-shop with limited buffers

Energy-efficient scheduling of distributed production systems has become a common practice among large companies with the advancement of economic globalization and green manufacturing. Nevertheless, energy-efficient scheduling of distributed permutation flow-shop problem with limited buffers (DPFSP-LB) does not receive adequate attention in the relevant literature. This paper is therefore the first attempt to study this DPFSP-LB with objectives of minimizing makespan and total energy consumption ( T E C ). To solve this energy-efficient DPFSP-LB, a Pareto-based collaborative multi-objective optimization algorithm (CMOA) is proposed. In the proposed CMOA, first, the speed scaling strategy based on problem property is designed to reduce T E C . Second, a collaborative initialization strategy is presented to generate a high-quality initial population. Third, three properties of DPFSP-LB are utilized to develop a collaborative search operator and a knowledge-based local search operator. Finally, we verify the effectiveness of each improvement component of CMOA and compare it against other well-known multi-objective optimization algorithms on instances. Experiment results demonstrate the effectiveness of CMOA in solving this energy-efficient DPFSP-LB. Especially, the CMOA is able to obtain excellent results on all problems regarding the comprehensive metric, and is also competitive to its rivals regarding the convergence metric. • A green criterion is considered in the studied problem. • A new constraint of the limited buffers is introduced into this problem. • A multi-objective optimization algorithm is presented to solve this problem. • An effective energy saving strategy is proposed. • An initialization strategy and local search strategy are proposed.

Survey on energy efficient scheduling techniques on cloud computing

With ever-growing technical advances, performance of complex scientific and engineering applications has arrived at petaflops and exaflops range. However, massive power drawn from the large scale computing infrastructure has caused commensurate rise in electricity consumption, escalating data center ownership costs besides leaving carbon footprints. Judicious scheduling of complex applications with an objective to reduce overall makespan and reduced energy consumption has become one of the biggest confront in the realm of computing architectures. This paper presents a survey on energy efficient scheduling algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic power management (DPM) techniques. The parameters considered are mainly the makespan, processor energy (dynamic and static) consumption, and network energy (communication) consumption, wherever appropriate during task scheduling.

A memetic algorithm for energy-efficient scheduling of integrated production and shipping

ABSTRACT Energy-efficient manufacturing is critical as the industrial sector accounts for a substantial portion of global energy consumption. This research aims to address an energy-efficient scheduling problem of production and shipping for minimizing both makespan and energy consumption. It contributes to an integrated energy-efficient production and shipping system, which is separately studied in most existing research. The production stage allocates jobs onto unrelated parallel machines that can be shut off and adjust their cutting speed to save energy. The shipping stage aims to allocate jobs to vehicles of various sizes with varied unit energy consumption. The problem is modelled as a mixed-integer quadratic program. Considering its complexity, a memetic algorithm (MA) is proposed to incorporate a knowledge-driven local search strategy considering the balance between exploration and exploitation. Two dominance rules are derived from the characteristics of the specific problem and embedded into the proposed MA to enhance its performance. Experimental results demonstrate that the proposed MA outperforms two other population-based algorithms, genetic algorithm and traditional MA, in terms of performance and computing time. This research practically contributes to improving productivity and energy efficiency for the production-shipping supply chain of make-to-order products.

Knowledge-Based Genetic Algorithm for Energy-Efficient Scheduling for Steel Making and an Empirical Study

Knowledge-Based Genetic Algorithm for Energy-Efficient Scheduling for Steel Making and an Empirical Study

Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption

Cloud computing has become an important paradigm in which scalable resources such as CPU, memory, disk and IO devices can be provided to users to remotely process their applications. In a cloud computing platform, energy consumption accounts for a significant cost portion. This article thus aims to present an energy-efficient scheduling algorithm for processing a user application with a real-time requirement. This problem is formulated as a non-linear mixed integer programming problem. We start with providing an optimal closed-form solution to its relaxation problem that aims to minimize the energy consumption without considering real-time requirements. To meet real-time requirements, we propose how to adjust task placement and resource allocation by making a good tradeoff between energy consumption and task execution time. Lastly, we find two equivalent optimal resource allocation strategies once task placement has been done. We then propose to adjust the start time of task execution such that an application’s completion time can be further shortened. Experimental results on two real-case enchmarks and extensive synthetic applications demonstrate that our proposed method finds a schedule that generally has 30 and 20 percent less energy consumption than enhancement heterogeneous earliest finish time (E-HEFT) and genetic algorithm, respectively. Besides, the proposed method has a higher rate to successfully find a feasible schedule than them, and its computation time is close to E-HEFT’s, but far less than the genetic algorithm's.

Deadline aware and energy-efficient scheduling algorithm for fine-grained tasks in mobile edge computing

These days, more and more web are converting into mobile devices such as the internet of things (IoT). These applications are distributed in nature and installed on mobile and servers to achieve many business goals. With this motivation, mobile edge computing (MEC) is an evolving paradigm that reduces the energy consumption of mobile devices through the offload process. However, currently all studies only concentrated on mobile energy and widely overlooked the energy usage of the computing node resources and application deadlines during offloading and scheduling in the MEC network. This research suggests a new mobile edge cloud (mob-cloud) architecture that minimises the nodes energy and meets the deadline requirements of applications. This research formulates this problem as the convex optimisation problem under linear integer programming. The mob-cloud consists of different types of virtual machines which are implementing at the edge computing. This study introduces a novel deadline and energy-efficient task scheduling (DEETS) algorithm framework consisting of task sequencing, resource searching, and mobility-aware scheduling. Furthermore, DEETS keeps tradeoff performances between resource energy usage and task deadline. Simulation results show that the proposed DEETS outperformed all contemporary methods regarding energy usage and deadlines of applications by 50%.

Collaborative Edge Computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.