Stretch-optimal scheduling for on-demand data broadcasts

Many of today's high-level parallel languages support dynamic, fine-grained parallelism. These languages allow the user to expose all the parallelism in the program, which is typically of a much higher degree than the number of processors. Hence an efficient scheduling algorithm is required to assign computations to processors at runtime. Besides having low overheads and good load balancing, it is important for the scheduling algorithm to minimize the space usage of the parallel program. This article presents an on-line scheduling algorithm that is provably space efficient and time efficient for nested-parallel languages. For a computation with depth D and serial space requirement S 1 , the algorithm generates a schedule that requires at most S 1 + O (K•D•p ) space (including scheduler space) on p processors. Here, K is a user-adjustable runtime parameter specifying the net amount of memory that a thread may allocate before it is preempted by the scheduler. Adjusting the value of K provides a trade-off between the running time and the memory requirement of a parallel computation. To allow the scheduler to scale with the number of processors we also parallelize the scheduler and analyze the space and time bounds of the computation to include scheduling costs. In addition to showing that the scheduling algorithm is space and time efficient in theory, we demonstrate that it is effective in practice. We have implemented a runtime system that uses our algorithm to schedule lightweight parallel threads. The results of executing parallel programs on this system show that our scheduling algorithm significantly reduces memory usage compared to previous techniques, without compromising performance.

IEEE 802.16 standards specify four types of services, while how to design an efficient scheduling algorithm remains an open issue. Manufacturers can implement their own scheduling algorithms depending on their specific requirements. There are a lot of scheduling algorithms proposed in the literature. Several scheduling algorithms propose hierarchical structures with strict priority or one-level scheduling mechanism. They are based conceptually on the round-robin approaches. However, they don't consider an important factor: fairness. In this paper we propose a fairness strategy algorithm for IEEE 802.16 broadband wireless access networks. The proposed algorithm applies a fairness strategy to utilize the system bandwidth, to achieve the QoS requirements of high priority service flows, and to reduce starvations of low priority service flows. The simulation studies show that the proposed scheduling algorithm provides a flexible way to utilize the system bandwidth.

Efficient scheduling algorithm is one of the key factors in determining the performance of distributed memory machines. This paper presents an enhanced Search and Du plication Based Scheduling (SDBS) algorithm which car schedule Directed Acyclic Graphs (DA Gs) with variable execution time tasks. Since the DAGs are based on data dependencies of the tasks, the control dependencies are clustered within each node. If the node encapsulates a loop or an if-then-else kind of statement, then assuming the execution times to be fixed would not be appropriate. It is assumed in this paper that the execution time varies between two time estimates. The complexity of this scheduling algorithm is in 0(V 4- E), where V is the number of node,- and E is the number of edges in the task graph. This algo rithm is based on some realistic assumptions and generates an optimal time schedule. If the assumptions cannot be completely satisfied then the algorithm provides a schedule which is close to optimal. The performance in these cases has been obtained using extensive simulation work which indicate the closeness of the results.

High efficient task scheduling algorithms can greatly influence the performance of reconfigurable systems. This paper analyzed the disadvantages of some current on-line reconfigurable system task scheduling algorithms, and proposed a new scheduling algorithm based on placement cost. This scheduling algorithm considered three types of cost, including hardware task execution time in FPGA, occupied FPGA area and fragmentation situation of FPGA, and also considered the unified scheduling of hardware and software tasks. When a hardware task was in scheduling, if the placement cost exceeded the setting threshold value, the task would be rejected to run in FPGA, but its software implementation could run in CPU. By reasonably rejecting some high cost tasks, this algorithm could achieve a higher overall successful scheduling rate. The simulation results show that this scheduling algorithm can achieve a higher deadline-guarantee ratio than others.

Most static scheduling algorithms that schedule parallel programs represented by directed acyclic graphs (DAGs) are sequential. This paper discusses the essential issues on parallelization of static scheduling algorithms. An efficient parallel scheduling algorithm, the HPMCP algorithm, is proposed. It produces high-quality scheduling and is much faster than existing algorithms.

Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this article, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is <inline-formula><tex-math notation="LaTeX">$O(t^{2} \times p)$</tex-math></inline-formula> , for <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> tasks and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

In the paper, a scheduling algorithm based on the estimation of packet loss amount is proposed for supporting real-time traffic in IEEE 802.22 WRAN systems. The proposed scheduling algorithm takes two steps for resource allocation. In the first step, it assigns resource to users having packets that are estimated to be dropped at next frame. In the second step, it assigns the remaining resource to the other users according to one of the existing scheduling algorithms. The simulation results show that the proposed scheduling algorithm provides much better performance than the PLFS and M-LWDF algorithm. In addition, a simple calculation method is proposed for the proposed scheme. With the simple method, we can reduce the number of checking packets in the queue as much as about a half time without any performance degradation in the case of video traffic.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.

In order to offload base station (BS) traffic and to enhance efficiency of spectrum, operators can activate many Device-to-Device (D2D) pairs or links in LTE networks. This increases the overall spectral efficiency because the same Resource Blocks (RBs) are used across cellular UEs (CUEs) (i.e., all UEs connected to BS for both C-Plane and D-plane communication) and D2D links (i.e., where the UEs are connected to BS only for C-plane communication). However, significant interference problems can be caused by D2D communications as the same RBs are being shared. In our work, we address this problem by proposing a novel scheduling algorithm, Efficient Scheduling and Power control Algorithm for D2Ds (ESPAD), which reuses the same RBs and tries to maximize the overall network throughput without affecting the CUEs throughput. ESPAD algorithm also ensures that Signal to Noise plus Interference Ratio (SINR) for each of the D2D links is maintained above a certain predefined threshold. The aforementioned properties of ESPAD algorithm makes sure that the CUEs do not experience very high interference from the D2Ds. It is observed that even when the SINR drop (i.e., maximum permissible drop in SINR of CUEs) is as high as 10 dB, there is no drastic decrease in CUEs throughput (only 3.78%). We also compare our algorithm against other algorithms and show that D2D throughput improves drastically without undermining CUEs throughput.

In Static List Scheduling (SLS), initially the ALAP (As Late As Possible) algorithm is performed, followed by ASAP (As Soon As Possible) algorithm. The time interval for control steps and successor selection are the vital drawback of ASAP algorithm. Hence, in this paper, the optimized scheduling algorithm is proposed for High Level Synthesis (HLS), where the ALAP in SLS is followed by Force Directed Scheduling (FDS). The proposed scheduling algorithm is implemented in Icarus Verilog and targeted in ZC7020 FPGA board. The proposed optimized scheduling algorithm is evaluated in terms of control steps and resource utilization. The evaluation is carried out for various benchmarks such as, EW Filter, IIR filter, FIR filter, AR filter and Discrete Cosine Transform (DCT). The achieved results in terms of the control steps and resource utilization (i.e. LUT and Flip-flop) shows the proposed optimized scheduling algorithm outperforms the conventional Static List Scheduling (SLS) algorithm.

Network services that dynamically allocate bandwidth resources, such as QoS and layer-1 bandwidth-on-demand(BoD), are increasingly required to advanced Internet backbones, such as science information networks (SINET) in Japan. In this paper, we propose scheduling algorithms for BoD service which allocate parts of full bandwidth dedicated to specific users according to their requests in advanced Internet backbones. The scheduling algorithms maximize the number of accepted requests, fairness, or the total bandwidth in BoD. Simulation results show that the proposed algorithms achieve high utilization of network resources and user’s fairness, compared with a simple random-based algorithm.

Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. At the same time, in order for the broadcast to proceed as predicted in the schedule, it must not contain parallel transmissions which can be conflicting based on the collision and interference parameters in the wireless network. Most existing work on this problem use a limited network model which accounts only for conflicts occurring inside the transmission ranges of the nodes. The broadcast schedules produced by these algorithms are likely to experience unpredictable delays when deployed in the network. This is because they do not take into consideration other important sources of conflict in parallel transmissions, namely the interference range and the carrier sensing range. In this paper we develop a conflict-aware network model, which uses these parameters to increase the probability of scheduling conflict-free transmissions, and thereby improve the reliability of the broadcast schedule. We present and prove correctness of a constant approximation algorithm for minimum-latency broadcast scheduling under this network model. We also present a greedy heuristic algorithm for the same problem. Experimental results are provided to evaluate the performance of our algorithms. In addition, the algorithms are analyzed to justify their performance trends.

A novel fault-tolerant scheduling algorithm for precedence constrained tasks in real-time heterogeneous systems

Efficient Scheduling Algorithm for Cloud

Jobshop scheduling is a classic instance in the field of production scheduling. Solving and optimizing the scheduling problem of the jobshop can greatly reduce the production cost of the workshop and improve the processing efficiency, thereby improving the market competitiveness of the manufacturing enterprises. In order to make decisions on the complex dynamic scheduling process more accurately and simplify the solution process, the jobshop scheduling problem can be transformed into a reinforcement learning problem based on the Markov decision process. The performance of the adaptive scheduling algorithm in a dynamic manufacturing environment is improved based on the Deep Q Network (DQN). In the proposed scheduling algorithm, five state features of continuous value ranges are designed for input to a Deep Neural Network (DNN), as well as ten well-known heuristic dispatching rules are selected as the action set of the DQN. In the proposed scheduling algorithm, the target network and the prediction network are used to train the parameters. An action selection strategy based on the “softmax” function is designed in DQN. It selects dispatching rules with the largest action value as the execution action, thereby solving the problem that the suboptimal action value is greater than the optimal action Q value in the early learning stage. Furthermore, the non-optimal action is selected with a greater probability in the later learning stage. Ten benchmark jobshop test instances called “LA” used as simulation objects and operated in a simulation environment composed of Python. The simulation results confirm that the proposed scheduling algorithm based on DQN has better performance and universality than a single dispatching rule or traditional Q learning algorithm.