Request Scheduling Research Articles

Retrieval request scheduling in a shuttle-based storage and retrieval system with two lifts

We study a shuttle-based storage and retrieval system with two independent lifts shared in a single mast. In such a system, two lifts move simultaneously but cannot pass each other, and each retrieval request is finished with the cooperation of a lift and a shuttle. Therefore, given a set of retrieval requests, the retrieval request scheduling problem consists of determining the sequence in which the requests are retrieved and assigning each request to a lift with the objective of minimizing the makespan, considering both the lift-lift interaction and lift-shuttle interaction. The problem is formulated as a mixed-integer programming model and proved to be NP-hard. We propose a decomposition-based adaptive large neighborhood search heuristic to quickly compute near-optimal solutions, using the property that the assignment of requests to lifts for a given retrieval sequence can be exactly solved in polynomial time by a dynamic programming approach. Numerical results indicate that our algorithm achieves a lower makespan than the methods proposed in the literature and used in practice. We also apply the proposed algorithm using real data to a realistic setting that considers multiple planning horizons, showing that it significantly outperforms the policy the company currently uses.

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

Artificial Intelligent for Smart home Control Based on GSM Network Application

In our modern science and digital technological revolution, to provide information and services to manage the state of the elements around us, where Eternity is things that unite everything in the world under one infrastructure, including things, people, places and processes. In smart grid operations, we focus on increasing cost and efficiency through load management and control. To regulate all load/power controllers at each end of the customer, this prototype demand-side control system uses a centralized system with a power management module. At each client end, a data entry scheme is used to retransmit load scheduling requests: GSM is used for wireless data transmission between multiple devices. Several loads, an Arduino Uno, relay modules, current sensors, and a target to control four electronic devices or home lighting by sending a message containing a word that is compared to the word written in the code stored in the microcontroller. If the words match, an LED is required on one of the Adriano ports so that each LED represents a specific replacement candidate. We can easily change the word written in the code using the Adriano software. The system is implemented with a high time response, so all readings are updated and appear spontaneously. The developed system should be Efficient, safe and fast. The smart home system must be implemented in real time

A Tier-Based Loading-Aware Charging Scheduling Algorithm for Wireless Rechargeable Sensor Networks

Charging scheduling is an important issue of wireless rechargeable sensor networks. Previous research proposed to optimize the scheduling sequence by considering factors such as distance and remaining working time. However, packets are transmitted to the base station hop by hop, so that the burden on each sensor is not the same. The unbalancing nature of loading should also be taken into account when dealing with charging requests scheduling. In this paper, the authors have found, both through theoretical analysis on hypothetical model and simulation in more realistic environments, that the communication loading of sensors impacts power consumption of sensors in different tiers relative to the base station significantly. Accordingly, the proposed charging scheduling algorithm takes the loading factor into consideration so that sensors closer to the base station may be given higher priority for recharge. The simulation results show that the proposed method can significantly improve the data delivery rate and achieve higher network availability when compared to previous research.

Multistage Mixed-Integer Robust Optimization for Power Grid Scheduling: An Efficient Reformulation Algorithm

The penetration of renewables into power systems is gradually increasing, but its inherent uncertainty poses tremendous challenges to the coordinated operation of power grids. To overcome the demerits of canonical two-stage mixed-integer robust optimization (RO) method that neglects the nonanticipativity requirement and the computational burden in engineering applications, this paper offers a reformulated multistage mixed-integer RO method for regional power grid scheduling. Firstly, a multistage mixed-integer RO scheduling model is established considering the scheduling requests in real-world projects. The scheduling plans under the nominal scenario are determined ahead of uncertainty in the first-stage optimization, and the multistage max-min optimization with binary recourse variables is then executed for feasibility-checking with respect to the nonanticipative realization of uncertainty. Secondly, a dedicated reformulation algorithm is proposed for this intractable multistage mixed-integer RO model. Based on the implicit affine strategy, the multistage max-min optimization is equivalently encapsulated to a single-stage max-min problem, and the dual Fourier-Motzkin elimination is put forward to eliminate both continuous and binary recourse variables in the resulting feasibility-checking optimization. Therefore, the original multistage mixed-integer RO is finally recast as a mixed-integer linear programming that can be solved directly. Numerical tests on an actual 16-bus grid, the IEEE 118-bus system and the 319-bus provincial grid verify the superiority and applicability of the proposed reformulated mixed-integer RO scheduling method, which is of great significance in guiding system operations.

Enhanced Hybrid Automatic Repeat Request Scheduling for Non-Terrestrial IoT Networks

Enhanced Hybrid Automatic Repeat Request Scheduling for Non-Terrestrial IoT Networks

Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems

The successful operations of power system require the support of communication networks with massive nodes access and two-way reliable and latency-critical transmission, which is the most-important use case of ultra-reliable low-latency communications (uRLLc) in the Fifth-Generation and Beyond and Sixth-Generation (B5G/6G) networks. In this paper, based on the orthogonal frequency division multiplexing (OFDM) signal in 5G, a new OFDM employing the hopped subcarrier technique (OFDM/FH) is proposed to attain the multiple nodes connectivity and the enhancement of transmission reliability. Considering the short and bursty packet types in the control information of power systems, the OFDM/FH signal can be naturally integrated with the mini-slot frame structure and automatic repeat request (ARQ) scheduling scheme to reduce the latency. The trade-off between the reliability in terms of the bit-error rate (BER) and the retransmission latency is investigated by semi-analytic simulations. Through the new joint design of the signal waveform and frame structure for uRLLc, we find that the proposed system can attain up to 99.999% reliability and ms-level latency for electric power services, even in the harsh wireless environment.

Tenant-Grained Request Scheduling in Software-Defined Cloud Computing

Cloud providers host various services for tenants’ requests (e.g., software-as-a-service) and seek to serve as many requests as possible for revenue maximization. Considering a large number of requests, the previous works on fine-grained request scheduling may lead to poor system scalability (or high schedule overhead) and break tenant isolation. In this article, we design a tenant-grained request scheduling framework to conquer the above two disadvantages. We formulate the tenant-grained request scheduling problem as an integer linear programming and prove its NP-hardness. We consider two complementary cases: the offline case (where we know all request demands in advance), and the online case (where we have to make immediate scheduling decisions for requests arriving online). A normalization-based algorithm with an approximation factor of <inline-formula><tex-math notation="LaTeX">$ {O}(1)$</tex-math></inline-formula> is proposed to solve the offline problem and a primal-dual-based algorithm with a competitive ratio of <inline-formula><tex-math notation="LaTeX">$[(1-\epsilon), {O}(\log 3\cdot n+\log (1/\epsilon))]$</tex-math></inline-formula> is designed for the online scenario, where <inline-formula><tex-math notation="LaTeX">$\epsilon \in (0,1)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of racks in the cloud. We also discuss how to integrate our proposed algorithms with the previous (fine-grained) request scheduling mechanism. Extensive simulation and experiment results show that our algorithms can obtain significant performance gains, e.g., the online algorithm reduces the scheduler's overhead more than <inline-formula><tex-math notation="LaTeX">$90\%$</tex-math></inline-formula> and achieves tenant isolation, while obtaining similar network performance (e.g., throughput) compared with the fine-grained request scheduling methods.

Configured Grant for Ultra-Reliable and Low-Latency Communications: Standardization and Beyond

Uplink configured Grant allocation has been introduced in Third Generation Partnership Project New Radio Release 15. This is beneficial in supporting Ultra-Reliable and Low-Latency Communication for industrial communication, a key Fifth Generation mobile communication usage scenario. This scheduling mechanism enables a user with periodic traffic to transmit its data readily and bypass the control signaling entailed to scheduling requests and scheduling grants and provides low latency access. To facilitate ultra-reliable communication, the scheduling mechanism can allow users to transmit consecutive redundant transmissions in a predefined period. However, if the traffic is semi-deterministic, the current standardized configured grant allocation is not equipped to emulate the traffic as the configured grant's period is preconfigured and fixed. This article describes the recent advancements in the standardization process in Release 15 and 16 for configured grant allocation and the prospective solutions to accommodate semi-deterministic traffic behavior for configured grant allocations.

Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

Request Scheduling Combined With Load Balancing in Mobile-Edge Computing

Edge servers can cache some delay-sensitive and resource-intensive applications to reduce the delay of user tasks. However, due to the limited resources of edge servers, they cannot cache all services and process all user requests like remote clouds. In order to take advantage of the low latency, we need to make full use of the limited resources of the edge servers and reasonably allocate user requests to edge servers for processing. Meanwhile, in order to maintain the efficient and long-term operation of the server cluster, we should also consider the load balancing of the cluster. How to balance the server load while making users have the best experience is an urgent problem to be solved. Solving the problem faces the challenge of the interaction between service placement and request scheduling, the tradeoff between communication and computation, and the consideration of response time and edge server load. We propose the service placement algorithm based on user visits and our request scheduling algorithm based on the simulated annealing algorithm. We verify the superiority of our algorithms in response time and server load balancing. The experimental results based on the real data sets prove our algorithm can cope with the actual situation and quickly converge to a favorable value.

Advance admission scheduling via resource satisficing

We study the problem of advance scheduling of ward admission requests in a public hospital, which affects the usage of critical resources such as operating theaters and hospital beds. Given the stochastic arrivals of patients and uncertain usage of resources, it is often infeasible for the planner to devise a risk‐free schedule to meet these requests without violating resource capacity constraints and creating adverse effects that include healthcare overtime, long patient waiting times, and bed shortages. The difficulty of quantifying these costs and the need to safeguard against resource overutilization lead us to propose a resource satisficing framework that renders the violation of resource constraints less likely and also diminishes its impact whenever it occurs. The risk of resource overutilization is captured by our resource satisficing index (RSI), which is calibrated to reflect a risk‐adjusted utilization rate for a better interpretation to the healthcare planner. Unlike the expected utilization rate, RSI is risk‐sensitive and serves to mitigate the risks of overutilization better whenever overutilization can be avoided in expectation. Our satisficing approach aims to balance out the overutilization risks by minimizing the maximal RSI among all resources and periods. Under our proposed partial adaptive scheduling policy, the resource satisficing model can be formulated and solved via a converging sequence of mixed‐integer linear optimization problems. A computational study establishes that our approach reduces resource overutilization risks to a greater extent than the benchmark methods.

Multistage Scheduling of Regional Power Grids Against Sequential Outage and Power Uncertainties

Aiming at the scheduling problem for regional power grids under both outage and power uncertainties, this paper puts forward a multistage robust optimization (RO) method that ensures the reliable power supply in system operations. Firstly, a multistage RO model is proposed according to the practical scheduling request of the regional power grid. The startup and shutdown of slow-acting generators are optimized in the first stage. The optimal topology is then determined in the second stage in preparation to the outage uncertainty of grid-connected lines. In the third to 2+ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> stages, the scheduling plans of generation units in each time period are further formulated regarding the sequential realizations of renewable-load power uncertainties, which ensures the nonanticipativity of the obtained robust schedules. Secondly, a tailored and efficient transformation-decomposition algorithm is exploited for the solution of multistage RO problem. Based on the affine control function, the nested max-min optimization of the third to 2+ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> stages is explicitly transformed to a single-stage max-min model via the multi-to-one strategy. A column and constraint generation (C&CG) algorithm with looped alternative optimization procedure (AOP) handles the equivalent three-stage RO problem with binary recourses, thus avoiding numerous forward and backward iterations as well as speeding up the modeling solution. Numerical experiments of real-world regional power grids verify the effectiveness, superiorities and scalability of the proposed multistage RO scheduling method, which indicates great guidance for engineering applications.

Integrating deep reinforcement learning with pointer networks for service request scheduling in edge computing

With the increasing popularity of edge computing, service providers are more likely to deploy services at the edge of the network to reduce the latency of service requests. However, the resources offered by edge servers are extremely limited compared to those in the cloud. Therefore, a challenging issue in edge computing is how to sufficiently utilize service resources at the edge to satisfy as many service requests as possible. Existing service request scheduling methods mainly use a single optimization objective, e.g., resource utilization or running time. In this paper, the issue of service request scheduling with multiple requests is modeled as a sequential problem, where multiple optimization objectives, including resource utilization, running time, and waiting time, are involved. A reinforcement learning model with pointer networks is proposed to construct scheduling policies. Experiments conducted on three representative real-world datasets show that our proposed approach outperforms several state-of-the-art methods on the three metrics.

Energy-Efficient Service Placement for Latency-Sensitive Applications in Edge Computing

Edge computing is a promising solution to host artificial intelligence (AI) applications that enable real-time insights on user-generated and device-generated data. This requires edge computing resources (storage and compute) to be widely deployed close to end devices. Such edge deployments require a large amount of energy to run as edge resources are typically overprovisioned to flexibly meet the needs of time-varying user demand with a low latency. Moreover, AI applications rely on deep neural network (DNN) models that are increasingly larger in size to support high accuracy. These DNN models must be efficiently stored and transferred, so as to minimize their energy consumption. In this article, we model the problem of energy-efficient placement of services (namely, DNN models) for AI applications as a multiperiod optimization problem. The formulation jointly places services and schedules requests such that the overall energy consumption is minimized and latency is low. We propose a heuristic that efficiently solves the problem while taking into account the impact of placing services across time periods. We assess the quality of the proposed heuristic by comparing its solution to a lower bound of the problem, obtained by formulating and solving a Lagrangian relaxation of the original problem. Extensive simulations show that our proposed heuristic outperforms baseline approaches in achieving a low energy consumption by packing services on a minimal number of edge nodes, while at the same time keeping the average latency of served requests below a configured threshold in nearly all time periods.

Adaptive Request Scheduling and Service Caching for MEC-Assisted IoT Networks: An Online Learning Approach

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-hungry and latency-sensitive services by enabling the placement of services and execution of computing tasks at the edge of radio access networks much closer to resource-constrained devices. However, how to serve more requests while reducing service latency by exploiting limited resources (storage capacities, CPU cycles, communication bandwidth) is still a critical issue in the multidevice MEC-assisted IoT networks, since the time-varying computing demands of devices and unavailability of future information make it difficult to determine where to handle computation tasks and which services to cache. In this article, we propose a twin-timescale framework to jointly optimize adaptive request scheduling (RS) and cooperative service caching (SC) in the multidevices and MEC-assisted networks, in order to explore request dynamic, MECs heterogeneity, service difference. To accommodate the unavailability of future information and unknown system dynamics, we, respectively, formulate RS and SC as partially observable Markov decision process (POMDP) problems. Then, we propose a deep reinforcement learning (DRL)-based online algorithm to improve the service latency reduction ratio and hit rate, which do not require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge such as service popularity. Moreover, we give the optimal CPU cycles and communication bandwidth allocations in order to further minimize the average service latency. Extensive and trace-driven simulation results demonstrate the efficacy of the proposed approach.

Efficient Resource Allocation in Fast-Uplink Grant for Machine-Type Communications With NOMA

Efficient uplink access is a keystone for the successful deployment of machine-type communication (MTC) that enables the promising Internet of Things (IoT). In this article, we introduce a communication framework that provides uplink access for MTC while avoiding both collisions and large signaling. Particularly, we propose a two-stage uplink access technique that combines both fast-uplink grant and nonorthogonal multiple access (NOMA). Using the fast grant, the base station (BS) schedules the devices without sending scheduling requests. Afterward, NOMA facilitates the grant sharing where pairing is done in a distributed manner to reduce signaling overhead. In addition, NOMA plays a major role in decoupling the challenges associated with fast grant, namely, active set prediction and optimal scheduling. In massive networks, full information gathering at the BS is impractical. Hence, multiarmed bandit (MAB) learning is adopted to schedule the fast grant devices. We devise an abstraction model for the source traffic predictor needed for a fast grant such that the prediction error can be evaluated. Consequently, the performance of the proposed scheme is analyzed in terms of average resource wastage and outage probability. The simulation results show the effectiveness of the proposed method in saving the scarce resources while verifying the analysis accuracy. In addition, the results show that the proposed scheme can easily attain the impractical optimal OMA performance, in terms of the achievable rewards, at an affordable complexity. Furthermore, the ability of the proposed scheme in picking quality MTC devices (MTDs) with strict latency is also proved.

RoNS: Robust network function services in clouds

In multi-tenant clouds, the traffic of tenants (e.g., enterprises) needs to be processed by network functions (NFs), for security and business logic issues. Due to potential hardware failures and software errors, NFs may break down. When encountering NF failures, we should consider two critical requirements for maintaining cloud robustness: limited influence scope and fast failure recovery. Without considering these two requirements, prior works based on deploying backup NF instances may result in large influence scope and long recovery time when a failure occurs. To bridge the gap, this paper investigates how to build robust network function services (RoNS) in multi-tenant clouds. Specifically, RoNS limits the number of tenants that each NF instance will serve so as to control the influence scope of an NF failure, and schedule requests with the help of agents designed in the data plane to achieve fast failure recovery. This is however a difficult undertaking. To solve this problem, RoNS takes a two-phase approach: NF instance allocation and tenant request scheduling. For NF instance allocation, we propose an efficient algorithm with bounded approximation factors based on the randomized rounding method. For tenant request scheduling, we present a primal–dual-based algorithm with a superior competitiveness ratio to solve it. We implement RoNS on a real testbed for experimental studies and use simulations for large-scale investigation. Both experiment results and simulation results show the superior performance of the proposed algorithms compared with other alternatives. For example, RoNS can cut down the number of affected tenants by 60%, and reduce recovery delay from 1170 ms to 316 ms on average, compared with existing failure recovery mechanisms based on deploying backup instances.

Solving large-scale dynamic vehicle routing problems with stochastic requests

Dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to approximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knapsack with a capacity given by the remaining service time available along the vehicle’s route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed methods in prescribing high-quality offline route plans and online scheduling decisions.