A high-speed dynamic instruction scheduling scheme for superscalar processors

The wakeup logic is a part of the issuing window and is responsible to manage the ready flags of the operands for dynamic instruction scheduling. The conventional wakeup logic is based on association, and composed of a RAM and a CAM. Since the logic is not pipelinable and the delays of these memories are dominated by the wire delays, the logic will be more critical with deeper pipelines and smaller feature sizes. This paper describes a new scheduling scheme not based on the association but on matrices which represent the dependences between instructions. Since the update logic of the matrices detects the dependencies between instructions as the register renaming logic does, the wakeup operation is realized by just reading the matrices. This paper also describes a technique to reduce the effective size of the matrices for small IPC penalties. We designed the layouts of the logics guided by a 0.18µm CMOS design rule provided by Fujitsu Limited, and calculated the delays. We also evaluated the penalties by cycle-level simulation. The results show that our scheme achieves 2.7GHz clock speed for the IPC degradation of about 1%.

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

Research on Design Method of Dynamic Shop Floor Scheduling System Based on Human-computer Interaction

In the formulation of single process fault free intelligent RGV dynamic scheduling strategy, a double objective optimization model is established to improve the efficiency of RGV and shorten the production time of single material. For the solution of the model, firstly, the shortest moving path of RGV is determined according to Greedy algorithm and Tabu search algorithm, and the loading and unloading sequence of RGV is determined according to the short job priority scheduling algorithm. For intelligent RGV dynamic scheduling problem with single process failure, The failure time is determined by the cumulative failure probability model of the system, According to the period of the fault point, after determining the fault CNC, a new dynamic scheduling model is obtained according to the no fault scheduling strategy. Finally, the model and the algorithm are verified by the data.

Body Area Networks (BANs), targeting everywhere anytime health monitoring, operate in license-exempt frequency bands. They require a cross layer design that jointly considers the requirements of (1) access delay to the medium and (2) packet delivery ratio (PDR). In this paper, we experimentally evaluate the PDR of a BAN under static and dynamic scheduling schemes. Investigations show that static multi-hop routing enhances PDR with the help of packet relaying, but at the cost of using more transmission slots in the shared medium, which may compromise access delay. To reduce this cost, we propose a dynamic scheduling approach that adapts to BAN topology changes resulting from body movements. Our extensive experimental results indicate that for BANs colloccated with other wireless systems, our dynamic multi-hop scheduling is mandatory for satisfaction of the PDR constraint.

Heterogeneous systems with multiple different compute devices have come into common use recently, and the heterogeneity of the compute device is mainly reflected in three aspects: hardware architecture, instruction set architecture, and processing capability. Heterogeneous CPU-accelerator systems have attracted increasing attention especially. To make full use of multiple CPUs and accelerators to execute data-parallel applications, programmers may need to manually map computation and data to all available compute devices, which is tedious, error-prone, and difficult. Especially for some data-parallel applications, the inter-device communication could easily become the performance bottleneck of multi-device co-execution. Therefore, firstly, a runtime system is designed for supporting heterogeneous cooperative execution (HCE) of data-parallel applications, which can help programmers to automatically and efficiently map computation and data to multiple compute devices. Secondly, an incremental data transfer method is designed to avoid redundant data transfers between devices, and a three-way overlapping communication optimization method based on software pipelining is designed to effectively hide the inter-device communication overhead. Based on our previously proposed feedback-based dynamic and elastic task scheduling (FDETS) scheme and asynchronous-based dynamic and elastic task scheduling (ADETS) scheme, the modified FDETS that supports incremental data transfer and the modified ADETS that supports three-way overlapping communication optimization are proposed, which not only can effectively partition and balance the workload among multiple compute devices but also can significantly reduce data transfer overhead between devices. Thirdly, a prototype of the proposed runtime system is implemented, which provides a set of runtime APIs for task scheduling, device management, memory management, and transfer optimization. Our experimental results show that the communication overhead between devices is greatly reduced using the proposed inter-device communication optimization methods and the multi-device co-execution significantly outperforms the best single-device execution.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

In the Software Defined Networking (SDN) based Data Center Networks (DCN), load balancing is one of the research hotspots. This paper proposes a Dynamic Traffic Scheduling (DTS) strategy for load balancing taking the advantages of the SDN central controller. Considering the realtime network status, the paths can be adjusted during the flow transmission. A flow equilibrium degree is defined as the trigger of enabling DTS. The implementation process of DTS are also described in detail. An OpenFlow based SDN simulation platform is constructed. The experimental results indicate that DTS strategy generate better performance compared with other routing strategy without dynamic rerouting mechanism under various load.

<span>The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.</span>

In some practical applications, impulsive noise can severely degrade the performance of a communication system. As a class of powerful error correction codes, Low-density parity-check (LDPC) codes have been shown superior performance on AWGN channels. In addition, belief propagation (BP) decoding algorithm of LDPC codes based on scheduling strategy has better performance and faster convergence rate than conventional BP decoding algorithm. In this paper, a dynamic scheduling of LDPC codes is first used on impulsive noise channels. Moreover, we propose a new scheduling strategy which named grouping dynamic scheduling (GDS) for impulsive noise channels to further improve the performance. Simulation results show that the dynamic scheduling on impulsive noise channels provides more performance gain than AWGN channels and the gain becomes significant when the channel is more impulsive. In addition, compared with conventional dynamic scheduling strategy, our proposed algorithm not only provides better performance, but also lowers the actual decoding computational complexity.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

DTS: Dynamic TDMA scheduling for Networked Control Systems

Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.

In this paper, we provide simulation results for the optimal channel-aware adaptive transmission, dynamic resource allocation and multiuser scheduling schemes for the downlink of a wireless cell that is assisted by a number of relays. The relays are half-duplex and apply a regenerative decode-and-forward scheme, and the channels are Rayleigh block-faded. We apply the optimal resource allocation scheme for two specific scheduling criteria, the maximum capacity scheduler and the proportional fair scheduler. For both scheduling schemes, we study the gains of using relays on the overall aggregate throughput of the system as well as for the individual users. Furthermore, we compare the performance versus the total number of relays and their locations in the wireless cell and we study the statistics of relays' usage. Moreover, we compare between the two scheduling schemes in terms of achievable rate and fairness among users.

With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.