A high performance cell scheduling algorithm in broadband multicast switching systems

The continuous increase in network traffic has sharply increased the demand for high-performance packet processing systems. For a high-performance packet processing system based on multi-core processors, the packet scheduling algorithm is critical because of the significant role it plays in load distribution, which is related to system throughput, attracting intensive research attention. However, it is not an easy task since the canonical flow-level packet scheduling algorithm is vulnerable to traffic locality, while the packet-level packet scheduling algorithm fails to maintain cache affinity. In this paper, we propose an adaptive throughput-first packet scheduling algorithm for DPDK-based packet processing systems. Combined with the feature of DPDK burst-oriented packet receiving and transmitting, we propose using Subflow as the scheduling unit and the adjustment unit making the proposed algorithm not only maintain the advantages of flow-level packet scheduling algorithms when the adjustment does not happen but also avoid packet loss as much as possible when the target core may be overloaded Experimental results show that the proposed method outperforms Round-Robin, HRW (High Random Weight), and CRC32 on system throughput and packet loss rate.

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A High-Performance Scheduling Algorithm Based on Packet Sto

10.1109/skg.2005.14 ◽

2005 ◽

Author(s):

Jing Qu ◽

Ximing Hu ◽

Peng Yi ◽

Xingming Zhang ◽

Binqiang Wang

Keyword(s):

High Performance ◽

Scheduling Algorithm

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Improved Model of Scheduling Algorithm

Mathematical Problems of Computer Science ◽

10.51408/1963-0027 ◽

2018 ◽

pp. 104-106

Author(s):

Artur Vardanyan

Keyword(s):

High Performance ◽

Cluster Computing ◽

Scheduling Algorithm ◽

Great Influence ◽

Cluster Management ◽

Task Scheduling Algorithm ◽

Long Time ◽

Improved Model ◽

First In First Out

Cluster computing is becoming increasingly practical for high performance computing research and development. A computer cluster is a set of connected computers that work together so that, they can be viewed as a single system. Clusters offer a scalable means of linking computers together to provide an expansive environment for hosting enterprise applications. As the number of nodes in cluster configurations grows, the cluster administration becomes more challenging. We need to study the challenges of cluster management and to provide a solution. To have an effective cluster management we need to have an effective task scheduling algorithm. With the explosive growth of information, the demand on computing is sharply increasing. Due to a large number of computing tasks, the scheduling algorithm is an important part of cluster computing and has a great influence on the quality of claster service. In cluster computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. This paper provides an overview of an improved scheduling algorithm that shortens the execution time of tasks and increases the resource utilization.

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ROA-CONS: Raccoon Optimization for Job Scheduling

Symmetry ◽

10.3390/sym13122270 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2270

Author(s):

Sina Zangbari Koohi ◽

Nor Asilah Wati Abdul Hamid ◽

Mohamed Othman ◽

Gafurjan Ibragimov

Keyword(s):

User Satisfaction ◽

High Performance ◽

Job Scheduling ◽

Response Times ◽

Scheduling Algorithm ◽

Desktop Computer ◽

Network Heterogeneity ◽

Parallel Job Scheduling ◽

Scheduling Method ◽

And Performance

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

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High Performance CGM-based Parallel Algorithms for the Optimal Binary Search Tree Problem

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2016100104 ◽

2016 ◽

Vol 8 (4) ◽

pp. 55-77 ◽

Cited By ~ 2

Author(s):

Vianney Kengne Tchendji ◽

Jean Frederic Myoupo ◽

Gilles Dequen

Keyword(s):

Parallel Algorithms ◽

Execution Time ◽

High Performance ◽

Scheduling Algorithm ◽

Search Tree ◽

Binary Search ◽

Coarse Grained ◽

Binary Search Tree ◽

Parameter Dependent ◽

The Impact

In this paper, the authors highlight the existence of close relations between the execution time, efficiency and number of communication rounds in a family of CGM-based parallel algorithms for the optimal binary search tree problem (OBST). In this case, these three parameters cannot be simultaneously improved. The family of CGM (Coarse Grained Multicomputer) algorithms they derive is based on Knuth's sequential solution running in time and space, where n is the size of the problem. These CGM algorithms use p processors, each with local memory. In general, the authors show that each algorithms runs in with communications rounds. is the granularity of their model, and is a parameter that depends on and . The special case of yields a load-balanced CGM-based parallel algorithm with communication rounds and execution steps. Alternately, if , they obtain another algorithm with better execution time, say , the absence of any load-balancing and communication rounds, i.e., not better than the first algorithm. The authors show that the granularity has a crucial role in the different techniques they use to partition the problem to solve and study the impact of each scheduling algorithm. To the best of their knowledge, this is the first unified method to derive a set of parameter-dependent CGM-based parallel algorithms for the OBST problem.

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