Computing Resource Scheduling Research Articles

RETRACTED: Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization

In order to optimize the quality of service (QoS) and execution time of task, a new resource scheduling based on improved particle swarm optimization (IPSO) is proposed to improve the efficiency and superiority. In cloud computing, the first principle of resource scheduling is to meet the needs of users, and the goal is to optimize the resource scheduling scheme and maximize the overall efficiency. This requires that the scheduling of cloud computing resources should be flexible, real-time and efficient. In this way, the mass resources of cloud computing can effectively meet the needs of the cloud users. Field Programmable Gate Arrays (FPGA), high performance and energy efficiency in one field. Most of them would have been the particle algorithm. The current technological development is still in-depth at super-resolution image research at an unprecedentedly fast pace. In particular, systemic origin applications get a lot of attention because they have a wide range of abnormal results. The scientific resource scheduling algorithm is the key to improve the efficiency of cloud computing resources distribution and the level of cloud services. In addition, the physical model of cloud computing resource scheduling is established. The performance of the IPSO algorithm applied to cloud computing resource scheduling is analysed in the design experiment. The comparison result shows that the new algorithm improves the PSO by taking full account of the user's Qu's requirements and the load balance of the cloud environment. In conclusion, the research on cloud computing resource scheduling based on IPSO can solve the problem of resource scheduling to a certain extent.

Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

Evaluation of cloud computing resource scheduling based on improved optimization algorithm

Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments

The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing.

Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of SMDs and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with the cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this article, we investigate the task offloading and resource allocation problem in MEC-enabled dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, the Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. The simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

Cloud computing resource scheduling based on improved ANN model takeaway order volume forecast

In recent years, with the change of people’s living habits, food delivery has become more and more popular. Along with this is the rapid growth of takeaway distribution. It is significant to improve the accurate prediction of the take-out order quantity since it can reduce unnecessary losses for the merchants. The Cloud computing system is the main platform for processing massive data. Applying cloud computing resource scheduling to the forecasting of takeaway orders will greatly help merchants. The research purpose of this paper is to study the cloud computing resource scheduling based on improved ANN model takeaway order volume prediction. This paper improves the ANN model and proposes a cloud computing resource scheduling method based on improved ANN model takeaway order volume prediction. An experimental test was performed to verify the reliability of the proposed method. The results of this study show that the proposed method can accurately predict takeaway orders and contributes to reasonable resource scheduling.

Dynamic Request Scheduling Optimization in Mobile Edge Computing for IoT Applications

In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of Things (IoT), mobile edge computing (MEC) and ultradense network (UDN) are considered to be two enabling and promising technologies, which result in the so-called ultradense edge computing (UDEC). Task offloading as an effective solution offers low latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using the quasiconvex technique, we address the PA problem and present a noncooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely, the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. The simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, and MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.

An offloading scheme leveraging on neighboring node resources for edge computing over fiber-wireless (FiWi) access networks

The computation resources at a single node in Edge Computing (EC) are commonly limited, which cannot execute large scale computation tasks. To face the challenge, an Offloading scheme leveraging on NEighboring node Resources (ONER) for EC over Fiber-Wireless (FiWi) access networks is proposed in this paper. In the ONER scheme, the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly, so that it can support the offloading transmission with low delay and wide bandwidth. Based on the ONER scheme supported by FiWi networks, computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops (e.g., just one wireless hop), which achieves low delay. Additionally, an efficient Computation Resource Scheduling (CRS) algorithm based on the ONER scheme is also proposed to make offloading decision. The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm. Therefore, the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

Computation offloading and resource allocation for mobile edge computing with multiple access points

Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation-intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP-hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. Numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

Research on the Management Model of Marine Economic Information Resources in the Big Data Era Based on Cloud Computing

Zhao, Y.-N. and Zhao, H., 2019. Research on the management model of marine economic information resources in the big data era based on cloud computing. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 316–320. Coconut Creek (Florida), ISSN 0749-0208.Marine economic information resources are the basis for the planning, management, protection and rational utilization of marine resources, as well as the important support for economic development and marine development in coastal areas. It has become a very necessary task and a hot topic in academic research as how to carry out “big data” management of marine economic information resources and analyze and deal with “big data” efficiently and accurately through appropriate cloud resource scheduling strategies. Based on the analysis of the cloud computing resource scheduling system, this paper designs a dynamic scheduling framework for adaptive optimization of cloud computing resources by using the cloud computing and cloud service technology, and gives a specific structure and form of the framework system for marine information resources. The cloud management architecture has initially realized the digital cloud management of marine economic information resources, achieved the sharing of marine information resources and service resources, and made the selection and calling process of cloud resource scheduling methods more reasonable.

A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Research on Cloud Computing Resource Scheduling Strategy Based on Firefly Optimized Genetic Algorithm

In the cloud computing system environment, combined with the first-level scheduling model of task-virtual machine resource nodes, the individual coding, fitness function, selection replication and cross-variation process are redesigned, and the cloud computing resource scheduling model based on genetic algorithm is established. Corresponding to fireflies and virtual machine resource nodes, this paper redesigned the firefly decision domain update method, selected attraction probability formula and location movement strategy, and combined with genetic algorithm to establish cloud computing resource scheduling model based on firefly-genetic algorithm. Experiment with the CloudSim cloud computing simulation platform. The results show that the task completion time of the resource scheduling model is smaller than that of the single genetic algorithm. The virtual machine load is more balanced, the task completion time is short, and the overall optimization effect of the resource scheduling scheme is obvious.

Node localization method for massive sensor networks based on clustering particle swarm optimization in cloud computing environment

In order to reduce the positioning error of wireless sensor network nodes and deal with the positioning of a large number of sensor network nodes, a mass sensor node positioning method based on particle swarm optimization (SNPSO) is proposed. The node location error of node distance correction value is corrected by SNPSO algorithm, and the sensing data is encoded by the index of cloud computing resources. The dynamic target strategy (DTS) algorithm is used to solve the strict deadline constraints. The algorithm focuses on optimizing execution time to meet deadline constraints, and once feasible solutions are obtained, it focuses on optimizing execution costs within deadline constraints. The performance of the algorithm is simulated and analyzed on the platform of MATLAB 2016. Compared with the sensor positioning method, SNPSO improves the positioning accuracy of mass sensor nodes. The simulation results verify the validity of SNPSO. Compared with the improved quantum genetic algorithm (IQGA) under different scale data scheduling and different deadlines constraints, the proposed algorithm can improve the positioning accuracy of mass sensor nodes. The proposed algorithm can find the optimal solution of cloud computing resource scheduling with lower execution cost under strict deadline constraints, and can more easily meet the needs of massive sensor network node location data processing.

Cloud Resource Adaptive Scheduling Framework and Optimization Strategy Based on Swarm Intelligence

Resource scheduling framework is very important for the overall cloud service efficiency and service quality,which is the core module of cloud computing. This paper proposes an adaptive cloud computing resource scheduling framework based on swarm intelligence. The proposed adaptive framework includes resource deployment module, scheduling module, recommendation module, optimization module, and monitoring module.The simulation results show that the proposed framework is more efficient than the traditional framework in terms of task execution time, system load balancing and resource service quality, and can improve the service quality of cloud applications while improving resource utilization.

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

With the development of Internet of Things (IoT), the massive data generated by it forms big data, and the complexity of dealing with big data brings challenges to resource scheduling in edge computing. In order to solve the problem of resource scheduling and improve the satisfaction of users in edge computing environment, we propose a user-oriented improved spectral clustering scheduling algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which makes the obtained clustering results more stable. Finally, the edge computing resource scheduling scheme is obtained based on the clustering results. The experimental results show that the resource scheduling scheme based on improved spectral clustering algorithm is superior to traditional spectral clustering algorithm in edge computing environment.

Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing

In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks with mobile edge computing. An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation, and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyze the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium is proved. To jointly allocate uplink subchannel, uplink transmission power, and computation resource for the offloading mobile terminals, a sub-algorithm named cloud and wireless resource allocation algorithm is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse method without interference and fractional frequency reuse method based on Hungarian and graph coloring with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks

As the demand of data-hungry and computing intensive tasks grows dramatically, cache-aided device-to-device multicast (D2MD) networks are introduced, which can offload traffic from the base stations to D2D users (DUEs) directly to alleviate the heavy burden on backhaul links and improve the energy and spectrum efficiency. However, most previous works ignored the limitation of battery power and scarce computing capabilities of DUEs. In this paper, we study the computation and traffic offloading in cache-aided D2MD networks for the content delivery and delay sensitive task offloading services. Firstly, in order to provide stable multicast links and enhanced computing resources, a D2D cluster head (DCH) selection strategy is proposed that jointly considers the social attributes, available energy, and transfer rate of DUEs. Secondly, to improve the efficiency of content distribution and optimize the energy consumption of content delivery, we propose a novel multicast-aware coded and cooperative caching scheme, which may increase the opportunity for D2D multicasting to obtain the desired contents. Thirdly, considering the DUEs association, uplink full duplex DCH transmission power allocation, and mobile edge computing computation resource scheduling, an optimization computation offloading model is formulated. On this basis, we model the computation offloading and resource allocation optimization problem. Furthermore, we transform this problem into user allocation optimization problem and resource allocation optimization problem (RAOP), and RAOP is proved as a convex problem, and the optimal resource allocation solution is found. Finally, the simulation results show that our proposed schemes can effectively decrease the energy consumption and computing costs.

The optimization for recurring queries in big data analysis system with MapReduce

As data-intensive cluster computing systems like MapReduce grow in popularity, there is a strong need to promote the efficiency. Recurring queries, repeatedly being executed for long periods of time on rapidly evolving data-intensive workloads, have become a bedrock component in big data analytic applications. Consequently, this paper presents optimization strategies for recurring queries for big data analysis. Firstly, it analyzes the impact of recurring queries efficiency by MapReduce recurring queries model. Secondly, it proposes the MapReduce consistent window slice algorithm, which can not only create more opportunities for reuse of recurring queries, but also greatly reduce redundant data while loading input data by the fine-grained scheduling. Thirdly, in terms of data scheduling, it designs the MapReduce late scheduling strategy that improve data processing and optimize computation resource scheduling in MapReduce cluster. Finally, it constructs the efficient data reuse execution plans by MapReduce recurring queries reuse strategy. The experimental results on a variety of workloads show that the algorithms outperform the state-of-the-art approaches.

A Cloud Computing Resource Scheduling Method Based on Particle Swarm Optimization and Ant Colony Optimization

Cloud computing system needs to process a large amount of data and has arduous tasks. An important question in cloud computing research is how to scheduling the resource rationally in this vast system. This paper presents an algorithm of resource scheduling combining Particle Swarm Optimization and Ant Colony Optimization. Firstly, get a good initial solution quickly through PSO algorithm, then ACO use the initial solution to determine the generation of initial pheromone distribution which is used to calculate the optimal solution through iteration. By experiment, the scheduling algorithm which combines PSO and ACO is better than both of the algorithms based on PSO or ACO.