A High-Performance Scheduling Algorithm Based on Packet Sto

2005 ◽  
Author(s):  
Jing Qu ◽  
Ximing Hu ◽  
Peng Yi ◽  
Xingming Zhang ◽  
Binqiang Wang
Keyword(s):  
High Performance ◽  
Scheduling Algorithm

scholarly journals An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems

2021 ◽  
Vol 13 (3) ◽  
pp. 78
Author(s):  
Chuanhong Li ◽  
Lei Song ◽  
Xuewen Zeng
Keyword(s):  
Packet Loss ◽  
High Performance ◽  
Packet Scheduling ◽  
Scheduling Algorithm ◽  
Processing System ◽  
System Throughput ◽  
Packet Processing ◽  
Research Attention ◽  
Continuous Increase ◽  
Packet Scheduling Algorithm

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.

scholarly journals Improved Model of Scheduling Algorithm

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.

scholarly journals ROA-CONS: Raccoon Optimization for Job Scheduling

Symmetry ◽  
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.

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

2016 ◽  
Vol 8 (4) ◽  
pp. 55-77 ◽  
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.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

A Novel Approach in Scheduling Of the Real- Time Tasks In Heterogeneous Multicore Processor with Fuzzy Logic Technique For Micro-grid Power Management

2018 ◽  
Vol 9 (1) ◽  
pp. 80
Author(s):  
Lavanya Dhanesh ◽  
P. Murugesan
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Power Management ◽  
Execution Time ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Multicore Processor ◽  
Heterogeneous Multicore ◽  
Micro Grid ◽  
Low Performance

Scheduling of tasks based on real time requirement is a major issue in the heterogeneous multicore systemsfor micro-grid power management . Heterogeneous multicore processor schedules the serial tasks in the high performance core and parallel tasks are executed on the low performance cores. The aim of this paper is to implement a scheduling algorithm based on fuzzy logic for heterogeneous multicore processor for effective micro-grid application. Real – time tasks generally have different execution time and dead line. The main idea is to use two fuzzy logic based scheduling algorithm, first is to assign priority based on execution time and deadline of the task. Second , the task which has assigned higher priority get allotted for execution in high performance core and remaining tasks which are assigned low priority get allotted in low performance cores. The main objective of this scheduling algorithm is to increase the throughput and to improve CPU utilization there by reducing the overall power consumption of the micro-grid power management systems. Test cases with different task execution time and deadline were generated to evaluate the algorithms using  MATLAB software.

Expected Cost Based Greedy Scheduling Algorithm for Out-Tree Task Graphs

2014 ◽  
Vol 556-562 ◽  
pp. 3431-3437 ◽  
Author(s):  
Jian Jun Zhang ◽  
Tian Hong Wang ◽  
Yu Zhuo Wang
Keyword(s):  
High Performance ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Heterogeneous Environments ◽  
Task Graphs ◽  
Schedule Length ◽  
Current Task ◽  
Greedy Scheduling Algorithm ◽  
Greedy Scheduling

Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.

Sign in / Sign up

Export Citation Format

Share Document