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9th international Conference on Logistics and systems Engineering 2019

作者： Wu, Jia-Guang School of Economics Management and Law University of South China Hengyang China

ISBN: (纸本)9781921712715

Green supply chain management emphasizes the organic integration of green procurement, green production, green storage, green transportation, green distribution and other processes. Only in this way can the overall optimal benefits be realized, it can avoid the waste of resources, build the brand image of green products and bring good reputation to the enterprise, so as to expand the product market and create greater profits. In the process of a series of logistics operations, effective control of the ownership of raw materials and parts can reduce unnecessary expenses, improve the efficiency of capital utilization, thus greatly improving the economic benefits of enterprises. The author introduces the distributed inventory centralized control strategy into the spare part inventory management of the logistics center. Firstly, the main idea of centralized control strategy and the advantages of distributed centralized control are discussed. Then, according to the warehouse management cost of the logistics center at a certain level of service, the decision modeling of insurance reserve is carried out. Finally, the realization process of insurance reserve management optimization in logistics center is discussed by means of heuristic algorithm. © Sulphur international Conference on Logistics and systems Engineering *** rights reserved.

关键词： Insurance

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Guest Editorial: Softwarized Networking for Next Generation Industrial Cyber-Physical systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第8期17卷 5506-5510页

作者： Garg, Sahil Wang, Honggang Granelli, Fabrizio Li, Hongwei Univ Quebec Ecole Technol Super Montreal PQ H3C 1K3 Canada Univ Massachusetts Dartmouth N Dartmouth MA 02747 USA Univ Trento I-38122 Trento Italy Univ Elect Sci & Technol China Chengdu 610054 Peoples R China

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

关键词： Special issues and sections Cyber-physical systems Informatics Data analysis Deep learning resource management Industrial Internet of Things Computer architecture Blockchain Real-time systems Cloud computing

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Design Of TT&C resource Automatic scheduling Interface Middleware With High Concurrency and Security

Design Of TT&C Resource Automatic Scheduling Interface Middl...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： CHEN Junchao ZHAI Baorong DONG Yibing WU Tao YOU Kai Xi’an Xi’an Satellite Control Center China

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

In order to significantly improve the reliable interaction and fast processing when TT&C(Tracking, Telemetry and Command) resource scheduling and management System (TRSMS) communicate with external systems which are diverse, multiple directional and high concurrent, this paper designs and implements a highly concurrent and secure middleware for TT&C resource Automatic scheduling Interface (TRASI). The middleware designs memory pool, data pool, thread pool and task pool to improve the efficiency of concurrent processing, uses the rule dictionary, communication handshake and wait retransmission mechanism to ensure the data interaction security and reliability. This middleware can effectively meet the requirements of TRASI for data exchange with external users and system, significantly improve the data processing speed and efficiency, and promote the information technology and automation level of Aerospace TT&C Network management Center (TNMC).

关键词： Memory management Task analysis Message systems Data models Middleware Dictionaries Job shop scheduling

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Characterizing and Balancing the Workloads of Semi-Containerized Clouds 25

Characterizing and Balancing the Workloads of Semi-Container...

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25th IEEE international Conference on parallel and distributed systems (IEEE ICPADS)

作者： Zhao, Shang Xue, Shuai Chen, Quan Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728125831

With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the Quality-of-Service requirement of online services that requires short response time while maximizing the resource utilization. While applications should be colocated to improve the resource utilization, the co-location results in performance interference between the co-located applications that hurts the QoS of latency-sensitive services. To understand this problem in depth, we characterize and analyze the real business-level application co-location scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).

关键词： Alibaba Trace Co-locations Workload Balance scheduling Strategy

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Reservation and Traffic Intent-Aware Dynamic resource Allocation for FANET

Reservation and Traffic Intent-Aware Dynamic Resource Alloca...

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international Conference on Communication Technology (ICCT)

作者： Lingli Yang Peilin Tao Tong Li Chungang Yang Xinru Mi Ying Ouyang Donghong Wei Qicai Wang The State Key Laboratory on Integrated Services Networks Xidian University Xi'an Shannxi China Institute of Systems Engineering Academy of Military Sciences Beijing China Science and Technology on Communication Networks Laboratory CETC Shijiazhuang Hebei China

ISBN: (纸本)9781665432078

Flying ad hoc network (FANET) is a special network composed of unmanned aerial vehicles (UAVs), which can be regarded as a special mobile ad hoc network. Nodes in FANET move freely at a high speed. In this paper, we propose a distributed time-division multiple access (TDMA) scheduling protocol for FANET, termed as RES-TDMA. This protocol can dynamically allocate time slots by monitoring time slot reservation request packets. By maintaining the traffic table, network nodes can not only obtain time slot allocation information within two hops, but also discover and release time slots in time. In addition, by applying intent-driven network to FANET, the proposed RES-TDMA is with the capabilities of self-analysis and self-configuration according to the traffic intent. Therefore, our proposed protocol reduces the impact of node mobility and improve time slot utilization. We choose typical traffic intent-aware simulation scenarios of the convergent and parallel traffic types to verify the effectiveness and efficiency of our presented RES-TOMA protocol.

关键词： Multiplexing Time division multiple access Protocols Simulation Dynamic scheduling resource management Reliability

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Dynamic Fine-Grained Workload Partitioning for Irregular Applications on Discrete CPU-GPU systems

Dynamic Fine-Grained Workload Partitioning for Irregular App...

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IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Chunhua Xiao Wei Ran Fangzhu Lin Lin Zhang College of Computer Science Chongqing University Chongqing China

As the scale of big data continues to grow and the complexity of data analysis algorithms increases, the desire for greater computing power is increasingly evident. A popular approach is to utilize heterogeneous systems for computation. Discrete CPU-GPU system, which has CPU and GPU on different chips connected through a PCI-e bus, is a typical heterogeneous system. There have been a lot of schemes proposed to improve performance on the discrete CPU-GPU system through partitioning. However, most of them are for regular applications and far from ideal in terms of resource utilization and performance for irregular applications. Heterogeneous computing receives much attention due to its performance potential, and many regular applications are greatly benefited from it, but the acceleration of irregular applications is still a problem to be solved. In this paper, we propose a dynamic fine-grained workload partitioning approach for irregular applications that boosts resource utilization to achieve a better load balance on heterogeneous platforms. The approach monitors the kernel execution of the CPU and GPU at runtime, and finely partitions the workload according to their processing speed, assigning relatively regular data to the GPU and the rest to the CPU. Evaluated with various irregular workloads, our scheme achieves up to 20% performance improvement over the state-of-the-art coarse-grained scheme, and the performance gap is less than 5% in most cases compared to oracle-based partitioning.

关键词： Runtime Data analysis Instruction sets Graphics processing units Load management Dynamic scheduling Heterogeneous networks

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SoCRATES: System-on-Chip resource Adaptive scheduling using Deep Reinforcement Learning

SoCRATES: System-on-Chip Resource Adaptive Scheduling using ...

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international Conference on Machine Learning and Applications (ICMLA)

作者： Tegg Taekyong Sung Bo Ryu EpiSys Science Inc Poway California United States

ISBN: (纸本)9781665443388

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip (SoC) applications, and has shown remarkable promises. In this paper, we introduce SoCRATES (SoC resource AdapTivE Scheduler), an extremely efficient DRL-based SoC scheduler which maps a wide range of hierarchical jobs to heterogeneous resources within SoC using the Eclectic Interaction Matching (EIM) technique. It is noted that the majority of SoC resource management approaches have been targeting makespan minimization with fixed number of jobs in the system. In contrast, SoCRATES aims at minimizing average latency in a steady-state condition while assigning tasks in the ready queue to heterogeneous resources (processing elements). We first show that the latency-minimization-driven SoC applications operate high-frequency job workload and distributed/parallel job execution. We then demonstrate SoCRATES successfully addresses the challenge of concurrent observations caused by the task dependency inherent in the latency minimization objective. Extensive tests show that SoCRATES outperforms other existing neural and non-neural schedulers with as high as 38% gain in latency reduction under a variety of job types and incoming rates. The resulting model is also compact in size and has very favorable energy consumption behaviors, making it highly practical for deployment in future SoC systems with built-in neural accelerator.

关键词： Scalability Stochastic processes Reinforcement learning Transforms Minimization Energy efficiency System-on-chip

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Compressive Sensing based Predictive Online scheduling with Task Colocation in Cloud Data Center

Compressive Sensing based Predictive Online Scheduling with ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yunhin Chan Ke Luo Xu Chen School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

With the growing size of the cloud data center, the high scheduling efficiency over massive-scale cloud servers is hard to achieve, particularly when the scheduler requires the full real-time cloud resource information for decision making. Moreover, most data centers only run latency-critical online services, resulting in low resource utilization. To solve these problems, we propose a Compressive Sensing based Predictive Online scheduling (CSPOS) algorithm. To mitigate the bottleneck of transferring massive resource information of all cloud servers to the scheduler, we propose to transfer sampled data from a small subset of servers to the scheduler and recover the full cloud resource information by compressive sensing. We then propose a predictive online learning algorithm that efficiently colocates the online services and batch jobs, in order to boost the resource utilization of the data center. Our experiments show that the CSPOS model achieves outstanding scheduling efficiency under various settings and is able to greatly increase the resource usage of a data center. We also illustrate that the running time of the CSPOS model is very small and has negligible effects on the scheduling system.

关键词： Data centers Prediction algorithms scheduling Servers resource management Task analysis Compressed sensing

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Gecko: Guaranteeing Latency SLO on a Multi-Tenant distributed Storage System

Gecko: Guaranteeing Latency SLO on a Multi-Tenant Distribute...

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international Conference on parallel and distributed systems (ICPADS)

作者： Zhenyu Leng Dejun Jiang Liuying Ma Jin Xiong University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Meeting tail latency Service Level Objective (SLO) as well as achieving high resource utilization is important to distributed storage systems. Recent works adopt strict priority scheduling or constant rate limiting to provide SLO guarantee but cause under-utilization resources. To address this issue, we first analyze the relationship between workload burst and latency SLO. Based on burst patterns and latency SLOs, we classify tenants into two categories: Postponement-Tolerable tenant and Postponement-Intolerable tenant. We then explore the opportunity to improve resource utilization by carefully allocating resources to each tenant type. We design Rate-Limiting-Priority scheduling algorithm to limit the impact of high priority tenants on low priority ones. Meanwhile, we propose Postponement-Aware scheduling algorithm which allows Postponement-Intolerable tenants to preempt system capacity from Postponement-Tolerable tenants. This helps to increase resource utilization. We propose a latency SLO guarantee framework Gecko. Gecko guarantees multi-tenant latency SLOs via combining the two proposed scheduling algorithms together with an admission control strategy. We evaluate Gecko with real production traces and the results show that Gecko admits 44% more tenants on average than state-of-the-art techniques meanwhile guaranteeing latency SLO.

关键词： Limiting scheduling algorithms Conferences Admission control Production Control systems resource management

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Hybrid Job scheduling in distributed systems based on Clone Detection

Hybrid Job Scheduling in Distributed Systems based on Clone ...

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： Uddalok Sen Madhulina Sarkar Nandini Mukherjee Asansol Engineering College Asansol West Bengal India Government College of Engineering and Textile Technology Berhampore West Bengal India Jadavpur University Kolkata India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

In order to propose an efficient scheduling policy in a large distributed heterogeneous environment, resource requirements of newly submitted jobs should be predicted prior to the execution of jobs. An execution history can be maintained to store the execution profile of all jobs executed earlier on a given set of resources. The execution history stores the actual CPU cycle consumed by the job as well as the resource details where it is executed. A feedback-guided job-modeling scheme can be used to detect similarity between the newly submitted jobs and previously executed jobs. It can also be used to predict resource requirements based on this similarity. However, efficient resource scheduling based on this knowledge has not been dealt with. In this paper, we propose a hybrid, scheduling policy of new jobs, which are independent of each other, based on their similarity with history jobs. Here we focus on exact clone jobs only i.e. its identical job is found in execution history and predicted resource consumption is same as exact resource consumption. We also endeavor to deal with two conflicting parameters i.e., execution cost and make span of jobs. A comparison with other existing algorithms is also presented in this paper.

关键词： Cloning History resource management Heuristic algorithms Complexity theory Dynamic scheduling Uncertainty

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