GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

Energy-Performance Tradeoffs in laaS Cloud with Virtual Machine Scheduling

In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.

Scheduling method based on virtual flattened architecture for Hierarchical system-on-chip

As the technology of IP-core-reused has been widely used, a lot of intellectual property （IP） cores have been embedded in different layers of system-on-chip （SOC）. Although the cycles of development and overhead are reduced by this method, it is a challenge to the SOC test. This paper proposes a scheduling method based on the virtual flattened architecture for hierarchical SOC, which breaks the hierarchical architecture to the virtual flattened one. Moreover, this method has more advantages compared with the traditional one, which tests the parent cores and child cores separately. Finally, the method is verified by the ITC＇02 benchmark, and gives good results that reduce the test time and overhead effectively.

Heuristic Scheduling Algorithms for Allocation of Virtualized Network and Computing Resources

Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications’ computation ability needs. These applications can be presented as meta-job of user’s demand. The total processing time of these jobs may need data transmission time over the Internet as well as the completed time of jobs to execute on the virtual machine must be taken into account. In this paper, we presented V-heuristics scheduling algorithm for allocation of virtualized network and computing resources under user’s constraint which applied into a service-oriented resource broker for jobs scheduling. This scheduling algorithm takes into account both data transmission time and computation time that related to virtualized network and virtual machine. The simulation results are compared with three different types of heuristic algorithms under conventional network or virtual network conditions such as MCT, Min-Min and Max-Min. e evaluate these algorithms within a simulated cloud environment via an abilenenetwork topology which is real physical core network topology. These experimental results show that V-heuristic scheduling algorithm achieved significant performance gain for a variety of applications in terms of load balance, Makespan, average resource utilization and total processing time.

Multi-Dimensional Aware Scheduling for Co-optimizing Utilization in Data Center

Resource Scheduling is crucial to data centers. However, most previous works focus only on one-dimensional resource models which ignoring the fact that multiple resources simultaneously utilized, including CPU, memory and network bandwidth. As cloud computing allows uncoordinated and heterogeneous users to share a data center, competition for multiple resources has become increasingly severe. Motivated by the differences on integrated utilization obtained from different packing schemes, in this paper we take the scheduling problem as a multi-dimensional combinatorial optimization problem with constraint satisfaction. With NP hardness, we present Multiple attribute decision based Integrated Resource Scheduling (MIRS), and a novel heuristic algorithm to gain the approximate optimal solution. Refers to simulation results, in face of various workload sets, our algorithm has significant superiorities in terms of efficiency and performance compared with previous methods.

High Throughput Scheduling Algorithms for Input Queued Packet Switches

The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications.These switching fabrics are efficiently driven by the deployed scheduling algorithms.In this paper,we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures.At first,we proposed a Simple 2-Bit(S2B)scheme which uses binary ranking procedure and queue size for scheduling the packets.Here,the Virtual Output Queue(VOQ)set with maximum number of empty queues receives higher rank than other VOQ’s.Through simulation,we showed S2B has better throughput performance than Highest Ranking First(HRF)arbitration under uniform,and non-uniform traffic patterns.To further improve the throughput-delay performance,an Enhanced 2-Bit(E2B)approach is proposed.This approach adopts an integer representation for rank,which is the number of empty queues in a VOQ set.The simulation result shows E2B outperforms S2B and HRF scheduling algorithms with maximum throughput-delay performance.Furthermore,the algorithms are simulated under hotspot traffic and E2B proves to be more efficient.

Implementation and Evaluation of Dynamically Weighted Low Complexity Fair Queuing(DWLC-FQ) Algorithm for Packet Scheduling in WiMAX Networks

Services provided by internet need guaranteed network performance. Efficient packet queuing and scheduling schemes play key role in achieving this. Internet engineering task force(IETF) has proposed Differentiated Services(Diff Serv) architecture for IP network which is based on classifying packets in to different service classes and scheduling them. Scheduling schemes of today's wireless broadband networks work on service differentiation. In this paper, we present a novel packet queue scheduling algorithm called dynamically weighted low complexity fair queuing(DWLC-FQ) which is an improvement over weighted fair queuing(WFQ) and worstcase fair weighted fair queuing+(WF2Q+). The proposed algorithm incorporates dynamic weight adjustment mechanism to cope with dynamics of data traffic such as burst and overload. It also reduces complexity associated with virtual time update and hence makes it suitable for high speed networks. Simulation results of proposed packet scheduling scheme demonstrate improvement in delay and drop rate performance for constant bit rate and video applications with very little or negligible impact on fairness.

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.

Energy Aware Processor Architecture for Effective Scheduling and Power Management in Cloud Using Inclusive Power-Cognizant Processor Controller

The fast acceptance of cloud technology to industry explains increasing energy conservation needs and adoption of energy aware scheduling methods to cloud. Power consumption is one of the top of mind issues in cloud, because the usage of cloud storage by the individuals or organization grows rapidly. Developing an efficient power management processor architecture has gained considerable attention. However, the conventional power management mechanism fails to consider task scheduling policies. Therefore, this work presents a novel energy aware framework for power management. The proposed system leads to the development of Inclusive Power-Cognizant Processor Controller (IPCPC) for efficient power utilization. To evaluate the performance of the proposed method, simulation experiments inputting random tasks as well as tasks collected from Google Trace Logs were conducted to validate the supremacy of IPCPC. The research based on Real world Google Trace Logs gives results that proposed framework leads to less than 9% of total power consumption per task of server which proves reduction in the overall power needed.

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

化工企业生产调度监控及预警系统集成研究与实践

当今时代信息技术日新月异的发展过程中,大数据的汇集与应用已成为重要特征。化工企业作为国民经济的重要支柱,由于生产工艺复杂、数据量大,对大数据的应用需求日益增大。本文以某化工企业应用大数据技术改造、升级、优化生产调度系统为具体研究样本,针对大数据在化工企业生产调度中的应用现状、面临的挑战与机遇等问题,有的放矢地解决生产运行中存在的管理难点和痛点问题。

＂Smart Cafe＂： A Mobile Local Computing System Based On Indoor Virtual Cloud

With network developing and virtualization rising, more and more indoor environment （POIs） such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components： 1）As to application, we create a parser to generate the ＂method call and cost tree＂ and analyze it to identify resource- intensive methods. 2） As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3） As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.

A Cloud-Based Virtualized Execution Environment for Mobile Applications

Smartphones and cloud computing technologies have enabled the development of sophisticated mobile applications. Still, many of these applications do not perform well due to limited computation, data storage, network bandwidth, and battery capacity in a mobile phone. While applications can be redesigned with client-server models to benefit from cloud services, users are no longer in full control of the application. This is also a serious concern. We propose an innovative framework for executing mobile applications in a virfualized cloud environment. With encryption and isolation, this environment is controlled by the user and protected against eavesdropping from cloud providers. We have developed efficient schemes for migrating applications and synchronizing data between execution environments. Performance and power issues within a virtualized execution environment are also addressed using power saving and scheduling techniques that enable automatic, seamless application migration.

A State-of-the-Art Survey on Real-Time Issues in Embedded Systems Virtualization

Virtualization has gained great acceptance in the server and cloud computing arena. In recent years, it has also been widely applied to real-time embedded systems with stringent timing constraints. We present a comprehensive survey on real-time issues in virtualization for embedded systems, covering popular virtualization systems including KVM, Xen, L4 and others.

Project Management and Coordination of Distributed Cooperative Product Design in a Virtual Enterprise

The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coordination is also discussed. First, based on the analysis of traditi onal project management, project management and coordination of distributed coop erative product design in the virtual enterprise is put forward. Then, aiming at the characteristics of a distributed concurrent product design process, the inh erent rules and complex interrelations in product development are studied. Accor dingly, the architecture of project management of distributed cooperative produc t design in a virtual enterprise is presented to adapt to distributed concurrent development of complex products. The main advantages of the architecture are al so discussed. Finally, the emphasis is placed on the project management process. Its main functions are set forth, such as project definition, task decompositio n and distribution, resource constraints and dynamic resource scheduling, proces s fusion, task scheduling and monitoring, project plan, cost and quality evaluat ion, etc.

A New Reliable System For Managing Virtual Cloud Network

Virtual cloud network(VCN)usage is popular today among large and small organizations due to its safety and money-saving.Moreover,it makes all resources in the company work as one unit.VCN also facilitates sharing of files and applications without effort.However,cloud providers face many issues in managing the VCN on cloud computing including these issues:Power consumption,network failures,and data availability.These issues often occur due to overloaded and unbalanced load tasks.In this paper,we propose a new automatic system to manage VCN for executing the workflow.The new system calledMulti-User Hybrid Scheduling(MUSH)can solve running issues and save power during workflow execution.It consists of three phases:Initialization,virtual machine allocation,and task scheduling algorithms.The MUSH system focuses on the execution of the workflow with deadline constraints.Moreover,it considers the utilization of virtual machines.The new system can save makespan and increase the throughput of the execution operation.

Teaching mechanism empowered by virtual simulation: Edge computing–driven approach

With the rapid development of network and communication techniques,the teaching forms have become diversified.To enhance the education experience and improve the teaching environment,an increasing number of educational institutions have adopted virtual simulation technology.A typical teaching mechanism is to exploit Virtual Reality(VR)technology,which affords participants an immersive experience.Unquestionably,such a VRbased mode is highly approved.However,the performance of this technology requires further optimization.On one hand,for VR 360video,the current intraframe decision cannot adapt to rapid response demands.On the other hand,the generated data size is considerably large and fast computation may not be realized,depending on the local VR device.Therefore,this study proposes an improved teaching mechanism empowered by edge computing–driven VR,called VE4T,that involves two parts.First,an intraframe decision algorithm for VR 360videos is devised to realize the rapid responses.Second,an edge computing framework is proposed to offload some tasks to an edge server for computation,where a task scheduling strategy is developed to check whether a task needs to be offloaded.Finally,experiments are performed using a practical teaching scenario with some VR devices.The obtained results demonstrate that VE4T is more efficient than existing mechanisms.

Optimal Load Balancing in Cloud Environment of Virtual Machines

Cloud resource scheduling is gaining prominence with the increasingtrends of reliance on cloud infrastructure solutions. Numerous sets of cloudresource scheduling models were evident in the literature. Cloud resource scheduling refers to the distinct set of algorithms or programs the service providersengage to maintain the service level allocation for various resources over a virtualenvironment. The model proposed in this manuscript schedules resources of virtual machines under potential volatility aspects, which can be applied for anypriority metric chosen by the server administrators. Also, the model can be flexible for any time frame-based analysis of the load factor. The model discussed inthis manuscript relies on the Bollinger Bands tool for understanding the potentialvolatility aspects of a Virtual Machine. The experimental study of the model compared to the contemporary load balancing model called STLB (Starvation Threshold-based Load Balancing) refers to a simple and potential model that can bemore pragmatic for sustainable ways of load balancing.