Parallel machine scheduling with precedence constraints and setup times

This paper presents different methods for solving parallel machine scheduling problems with precedence constraints and setup times between the jobs. Limited discrepancy search methods mixed with local search principles, dominance conditions and specific lower bounds are proposed. The proposed methods are evaluated on a set of randomly generated instances and compared with previous results from the literature and those obtained with an efficient commercial solver. We conclude that our propositions are quite competitive and our results even outperform other approaches in most cases

Cell-centric and user-centric multi-user scheduling in visible light communication aided networks

Visible Light Communication (VLC) combined withadvanced illumination has been expected to become an integralpart of next generation heterogeneous networks at the time ofwriting, by inspiring further research interests. From both theCell-Centric (CC) and the User-Centric (UC) perspectives, variousVLC cell formations, ranging from fixed-shape regular cellswith different Frequency Reuse (FR) patterns and merged cellsemploying advanced transmission scheme to amorphous userspecificcells are investigated. Furthermore, different Multi-UserScheduling (MUS) algorithms achieving Proportional Fairness(PF) are implemented according to different cell formations.By analysing some critical and unique characteristics of VLC,our simulation results demonstrate that, the proposed MUSalgorithms are capable of providing a high aggregate throughputand achieving modest fairness with low complexity in most of thescenarios considered.<br/

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms

Fear or Greed? Duty or Solidarity? Motivations and Stages of Moral Reasoning: Experimental Evidences from Public-Goods Provision Dilemmas

As economists increasingly recognize the limits of the canonical self-interest assumption, the lack of a theory of human valuation that clearly specifies what determines an individual’s utility judgments renders the prediction of behavior in social dilemmas virtually impossible.  In this study, we examined the explanatory power of a structuralist-constructivist theory of adult development and this theory’s analytical significance to the understanding of behavioral diversity in situations where individual and collective interests collide. Experimental results suggest that the theoretical constructs built into the selected theory provide a reliable basis for predicting participants’ behavior when presented with two different collective-action dilemmas under diverse institutional conditions.social dilemmas, experimental economics, sociocognitive and moral reasoning, adult development, Institutional and Behavioral Economics, C72, C92, D74,

System Design for a Long-Line Quantum Repeater

We present a new control algorithm and system design for a network of quantum repeaters, and outline the end-to-end protocol architecture. Such a network will create long-distance quantum states, supporting quantum key distribution as well as distributed quantum computation. Quantum repeaters improve the reduction of quantum-communication throughput with distance from exponential to polynomial. Because a quantum state cannot be copied, a quantum repeater is not a signal amplifier, but rather executes algorithms for quantum teleportation in conjunction with a specialized type of quantum error correction called purification to raise the fidelity of the quantum states. We introduce our banded purification scheme, which is especially effective when the fidelity of coupled qubits is low, improving the prospects for experimental realization of such systems. The resulting throughput is calculated via detailed simulations of a long line composed of shorter hops. Our algorithmic improvements increase throughput by a factor of up to fifty compared to earlier approaches, for a broad range of physical characteristics.Comment: 12 pages, 13 figures. v2 includes one new graph, modest corrections to some others, and significantly improved presentation. to appear in IEEE/ACM Transactions on Networkin

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version