Efficient Scheduling Scheme Research Articles

Energy efficient scheduling and power control of massive MIMO in massive IoT networks

We present various energy efficient (EE) scheduling and power control schemes for Massive MIMO with massive Internet of Things (IoT) connectivity. To support massive IoT devices simultaneously with low latency, the reference signal (RS) is heavily reused. To increase the performance, we reorganize the distributed IoT devices in ascending and in descending orders. We show that the spectral efficiency (SE) is improved based on water-filling effect. Next, we show that a generalized power control scheme is quite effective in increasing the fairness. Nevertheless, it requires a lot of power which is not fitted to low power IoT networks. Based on these facts, we propose sigmoid function based power allocation schemes to compromise between SE/EE performance and the fairness. Numerical results verify that the proposed scheme can achieve arbitrary SE/EE performance tradeoff with satisfactory fairness.

Exact and heuristic algorithms for the parallel machine total completion time scheduling problem with dual resources, ready times, and sequence-dependent setup times

This paper considers a dual resource constrained scheduling problem prevalent in high-capital cost manufacturing industry such as the photolithography area in the semiconductor industry. Specifically, the problem consists of assigning jobs to parallel machines that use common and constrained auxiliary resources such as lithography lens. Very often these auxiliary resources require heavy capital investment and setup times further complicates an already complex dual resource constrained scheduling problem. This paper advocates for the use of deterministic scheduling theory for the design and development of more efficient scheduling strategies. An Integer Programming (IP) model, a heuristic and a hybrid model were developed to schedule identical parallel machines with shared, constrained, auxiliary resources, with sequence-dependent setups, and job release dates with the goal of minimizing the sum of completion times.An IP model primed with a heuristic can add value to practitioners looking to solve real world resource constrained parallel machine scheduling problems with sequence-dependent setups. It is extremely important to control problem instance size and time horizon in order to obtain near-optimal or efficient solutions within acceptable run times. The proposed heuristic provides an initial feasible solution for the IP model which reduces the space search by decreasing the time horizon and eliminates the time needed to find an initial feasible solution. A tighter formulation is also proposed by reducing the time horizon formulation.

Towards user-friendly energy supplement service considering battery degradation cost

With the booming development of electric vehicles (EVs), the problem of managing a large number of EVs with frequent energy requirements due to limited batteries capacity has attracted more and more attention. It thus is significantly important to design an efficient energy supplement scheduling scheme. Due to the long charging waiting duration of the plug-in charging strategy, we introduce the battery swapping mechanism in this paper, which can considerably reduce the time of energy refill from hours to minutes. For the depleted batteries from EVs, we leverage a two-period charging with constant current-constant voltage (CC-CV) to model the recharging process. The CC-CV model depicts the characteristic of the battery during the practical charging scenario and is widely used for Li-ion battery charging. The battery degradation cost is further formulated to capture the performance degradation for the depleted batteries causing by charging. Considering the focus from EV drivers on waiting time of battery swapping service, and that from the operator on battery degradation cost, a demand balance battery swapping service framework is thus proposed. Moreover, a reservation-enabled scheme is designed to predicate swapping waiting time by capturing EV's reservations. Therefore, the demand balance can be eventually achieved by jointly optimizing waiting time and battery degradation cost. Simulations present the desirable performance of the proposed scheme with the comparison of other benchmarks. Particularly, the degradation of batteries is well alleviated, which is vital for BSSs' long-term operation.

A Collaborative Caching-Transmission Method for Heterogeneous Video Services in Cache-Enabled Terahertz Heterogeneous Networks

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes.

Task Scheduling in Cloud Using ACO

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

A learning-based distributed algorithm for scheduling in multi-hop wireless networks

We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

Rule-Based Dual Planning Strategy of Hybrid Battery Energy Storage System

A reasonable and efficient scheduling strategy does not only help ensure the safe and stable operation of battery energy storage system, but also extend the battery cycling life and reduce the system overall costs. In this paper, a novel rule-based dual planning strategy is proposed to achieve refined management for the hybrid battery energy storage system, including lead-acid battery storage system and lithium iron battery storage system. Aiming at different characteristics and SOC value of different batteries, the upper layer of the proposed scheduling strategy is designed to determine whether the hybrid battery energy storage system is enabled and grid-connected and in the lower layer, the battery scheduling controller will cooperate the operation of different batteries to realize shallow circulation of the lead-acid battery storage. Various simulations are conducted in the environment of Matlab/Simscape, and the simulation results have proved the feasibility and effectiveness of the proposed scheduling strategy.

ET2FA: A Hybrid Heuristic Algorithm for Deadline-constrained Workflow Scheduling in Cloud

Cloud computing is an emerging computational infrastructure for cost-efficient workflow execution that provides flexible and dynamically scalable computing resources at pay-as-you-go pricing. Workflow scheduling, as a typical NP-Complete problem, is one of the major issues in cloud computing. However, in the cloud scenario with unlimited resources, how to generate an efficient and economical workflow scheduling scheme under the deadline constraint is still an extraordinary challenge. In this paper, we propose a hybrid heuristic algorithm called enhanced task type first algorithm (ET2FA) to solve deadline-constrained workflow scheduling in cloud with new features such as hibernation and per-second billing. The objectives to be minimized include the total cost and total idle rate. ET2FA involves three phases: 1) Task type first algorithm, which schedules tasks based on topological level and task types, and utilizes a compact-scheduling-condition based VM selection method to assign each task. 2) Delay operation based on block structure, which further optimizes total cost and total idle rate based on block structure properties. 3) Instance hibernate scheduling heuristic, which sets an instance to hibernate if idle for a duration. Extensive simulation experiments based on seven well-known real-world workflow applications show that ET2FA delivers better performance in comparison to the state-of-the-art algorithms.

More Than Scheduling: Novel and Efficient Coordination Algorithms for Multiple readers in RFID Systems

How to efficiently coordinate multiple readers to work together is critical for high throughput in RFID systems. Existing researchs focus on designing efficient reader scheduling strategies that arrange adjacent readers to work in different time to avoid signal collisions. However, the impact of unbalanced tag number of readers on tag read throughput is still challenging. In RFID systems, the distribution of tags is usually variable and uneven, making the number of tags covered by each reader (i.e., the load) imbalanced. This imbalance leads to different execution time for readers: the heavily loaded readers take longer time to collect all tags, while the other readers whose finish execution earlier have to wait in vain. To avoid this useless waiting and improve the system throughput, this paper focuses on the load balancing problem of multiple readers, which is an NP-hard problem. In this paper, we design heuristic algorithms to adjust readers interrogation regions and efficiently balance their loads. The amazing advantage of our algorithm is that it can be adopted by almost all existing protocols in multi-reader systems, including the reader scheduling protocol, to improve system throughput. Extensive experiments demonstrate that our algorithm can significantly improve the throughput in various scenarios.

Evolutionary Algorithm Based Adaptive Load Balancing (EA-ALB) in Cloud Computing Framework

In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work. Initially, process of K-means clustering is used for computing optimal min-max rates and then, local search capability for solving the load balancing problems in cloud model is determined with the incorporation of Evolutionary Algorithm. The results show that the proposed model achieves better results in terms of load balancing factors, Virtual Machine (VM) migration, energy consumption and so on, when compared to the existing model.

Veterans Health Administration Scheduling System: The Path to High Reliability Realization.

Timely access to services is the gateway to patient safety and quality, and scheduling is foundational to providing access to highly reliable care. An effective and efficient scheduling strategy is dependent on an evidence-based approach that focuses on critical drivers of the scheduling system related to patient safety and quality as well as access. As part of a continuing effort to improve access, the Veterans Health Administration (VHA) completed a direct causation analysis (2015-2020) using an evidence-based framework and comprehensive measurement plan. The analysis, described here, validates access benefits realized specialty by specialty and facility by facility, identifies opportunities for improvement, and acknowledges limitations of the change from the Veterans Information Systems and Technology Architecture scheduling system to the Medical Appointment Scheduling System.This analysis of the assessments illustrates business validation structures, drivers, processes, and outcomes that can support leadership decision-making related to access. We drew our assessments of people, processes, policies, and technology from on-site interviews, over-the-shoulder observations, large-group discussions, and data from the VHA Support Service Center and facility data systems; we also mapped process steps, keystrokes, and workflow. Our assessments provided support for the VHA's decision to implement the stand-alone Cerner scheduling system at one site while continuing to implement the Cerner Millennium electronic health record platform that includes the Cerner scheduling system at other VHA sites. The VHA experience provides lessons learned for healthcare leaders who seek highly reliable efforts to improve access to care.

Energy-efficient collaborative optimization for VM scheduling in cloud computing

Energy-efficient resource scheduling has become a hot issue in the field of cloud computing. However, there is an inevitable conflict between energy-saving and QoS optimization. In real-world scenarios, the volatility of cloud task arrival will cause the optimization problem to become more difficult. To achieve a better trade-off between these two goals, a novel resource scheduling framework based on collaborative optimization is proposed for the cloud computing environment. Based on the Lyapunov optimization method, the optimization problem can be solved explicitly in each time slice. We build a multi virtual machine queuing model and analyze the relationships between the task queues’ backlog and the system energy consumption. We also introduce a method of using stacked denoising auto-encoder for extracting the QoS features to improve the constraints of the collaborative optimization objective function. Finally, we propose an efficient resource scheduling strategy to give full play to the processing capabilities of the virtual machine. Experimental results show that, compared with other advanced energy-saving strategies, our scheduling strategy can effectively reduce the energy consumption of the cloud data center while guaranteeing QoS, and reduce the total scheduling time cost of data center by more than 20%.

A system and game strategy for the isolated island electric-gas deeply coupled energy network

For the isolated island micro grid, a safe and reliable energy supply system is indispensable. Generally speaking, the power generation modules of an small and medium-sized isolated island energy system with off-grid power supply mode mainly include diesel generator(DIG) and distributed power supply. In order to smooth the power output fluctuation of wind and solar, coordinate the output of multiple energy generation technologies, and increase the penetration rate of distributed energy, it is necessary to develop a flexible and reliable energy system, and an efficient and smart scheduling strategy. In this paper, the power-to-gas (P2G) technology is introduced to construct a multi-energy complementary integrated energy system with deep coupling of power-gas network. Four possible game planning models are proposed by using game theory analysis method, and the energy scheduling strategies under each game mode are obtained by particle swarm optimization(PSO). By comparing the scheduling strategies under the four game modes, it can be found that the synergistic response of hydrogen and methane in the alliance cooperative game mode has absolute advantages in reducing the curtailment rate of wind and solar energy, balancing the income of each participant, improving the total income of the system, and saving energy and environmental protection.

Numerical Simulations of 2D Hydraulic Jumps by a Parallel SPH Model

Hydraulic jumps are a rapid transition from supercritical to subcritical flow and generally occur in rivers or spillways. Owing to the high energy dissipation rate, hydraulic jumps are widely applied as energy dissipators in hydraulic projects. To achieve efficient and accurate simulations of 2D hydraulic jumps in open channels, a parallel Weakly Compressible Smoothed Particle Hydrodynamics model (WCSPH) with Shepard Density filter was established in this study. The acceleration of the model was obtained by OpenMP to reduce execution time. To further reduce execution time, a suitable and efficient scheduling strategy was selected for the parallel numerical model by comparing parallel speed-ups under different scheduling strategies in OpenMP. Following this, two test cases of uniform flow in open channels and hydraulic jumps with different inflow conditions were investigated to validate the model. The comparison of the water depth and velocity fields between the numerical results and the analytical solution generally showed good agreement, although there was a minor discrepancy in conjugate water depths. The numerical results showed free surface undulation with decreasing amplitude, which is more consistent with physical reality, with a low inflow Froude number. Simultaneously, the Shepard filter was able to smooth the pressure fields of the hydraulic jumps with a high inflow Froude number. Moreover, the parallel speed-up was generally able to reach theoretical maximum acceleration by analyzing the performance of the model according to different particle numbers.

Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm

Task scheduling in cloud environment is a hot topic in current research. Effective scheduling of massive tasks submitted by users in cloud environment is of great practical significance for increasing the core competitiveness of companies and enterprises and improving their economic benefits. Faced with the urgent need for an efficient scheduling strategy in the real world, this paper analyzed the process of cloud task scheduling, and proposed a particle swarm optimization genetic hybrid algorithm based on phagocytosis PSO_PGA. Firstly, each generation of particle swarm is divided, and the position of the particles in the sub population is changed by using phagocytosis mechanism and crossover mutation of genetic algorithm, so as to expand the search range of the solution space. Then the sub populations are merged, which ensures the diversity of particles in the population and reduces the probability of the algorithm falling into the local optimal solution. Finally, the feedback mechanism is used to feed back the flight experience of the particle itself and the flight experience of the companion to the next generation particle population, so as to ensure that the particle population can always move towards the direction of excellent solution. Through simulation experiments, the proposed algorithm is compared with several other existing algorithms, and the results show that the proposed algorithm significantly improves the overall completion time of cloud tasks, and has higher convergence accuracy. It shows the effectiveness of the algorithm in cloud task scheduling.

Guest Editorial: Edge Intelligence for Beyond 5G Networks

Beyond fifth-generation (B5G) networks, or so-called “6G”, is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g., self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/VR services. By integrating AI functions into edge networks, radio networks become service-aware and resource-aware to have a full insight into the operating environment and can adapt resource allocation/orchestration in a dynamic manner. Despite the potential of edge intelligence, however, many challenges also need to be addressed in this new paradigm. Until now, limited research efforts have been made on edge intelligence for B5G networks.

Correction to: Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.

Secrecy capacity and energy efficiency in D2D-enabled cellular networks

Device-to-device (D2D) communication is introduced in cellular networks to allow devices in close proximity to communicate directly with each other, freeing up spectral and other system resources. We propose a scheme that promotes the coexistence of cellular users (CUs) and D2D users (DUs). DUs are allowed to access the CU channel for D2D communications, enhancing the spectral efficiency. On the other hand, DU is expected to protect the secrecy of CU’s data by transmitting artificial noise to jam any potential eavesdroppers near the CU link, which may include other DUs. This enhances the cellular secrecy capacity at the expense of DU’s energy, which motivates us to assess the CU’s secrecy capacity and DU’s energy efficiency of this scheme. We have identified the causes and conditions of increased energy efficiency under certain secrecy capacity requirement, e.g. an optimal DU’s sensing duration that results in maximal energy efficiency. Such information is important for the planning of an efficient access control and scheduling scheme for the DU links in cellular networks.

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.