A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems

Memeti, Suejb; Pllana, Sabri; Binotto, Alécio Pedro Delazari; Kołodziej, Joanna; Brandić, Ivona

doi:10.1145/3230905.3230906

Cited by 9 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another category, machine learning techniques are used in job scheduling. 28 A job scheduling service platform using Reinforcement Learning methods is presented in Orhean et al 29 for scheduling dependent tasks. These methods are not suitable for complex systems with a wide range of variable states despite their reduced time.…”

Section: Job Scheduling Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

View full text Add to dashboard Cite

Summary In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

show abstract

Section: Job Scheduling Algorithmsmentioning

confidence: 99%

“…A low ENU shows that the employed replication algorithm is better at locating the files in suitable places. 24 The ENU, a value between 0 and 1, is calculated by Equation (28).…”

Section: Mjt =mentioning

confidence: 99%

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…The local scheduler allocates each task with identity number. The LS runs a script having an ID with range that is changed from the default LS scripts with End-point Management [9], [10].…”

Section: A Local Scheduler (Ls)mentioning

confidence: 99%

“…In this each part of the GS sets priority to the jobs. By using a mapping such priorities are associated with global priorities [9,10].…”

Section: B Global Scheduler (Gs)mentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Search Based Localization in Mobile Computational Grid

Sahu

Singh

Manju³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In recent years, the number of cell phones in society has increased drastically and they are getting popular due to their computational ability and adaptability. Resource provisioning is important, but still remains NP-hard problem in mobile computational grid (MCG). Once the jobs are assigned to the MCG, the main challenge is how to identify the correct resource according to the job's requirement and use them to execute the sub-jobs. The heuristic methods such as Min-Min, Max-Min, and HEFT can be used to select appropriate resources from the MCG that is assigned for job execution. Since the computational nodes are static and mobile in nature, the performance of such heuristics is not as expected. Such heuristics suffers from low throughput and low speedup. The process of localization is used in a wireless sensor network with good results. The proposed model uses heuristics and localization process for optimizing the quality of service parameter localization, normalized speedup, and throughput in MCG, with the concept of grid nodes available in MCG. The observation shows significant improvement in the quality of service parameter localization, normalized speedup, and throughput in MCG. The proposed model HGLA and MIN-MIN, MAX-MIN, and HEFT are compared with respect to localization, speedup, and throughput. The results reveal that the proposed model shows better performance over MIN-MIN, MAX-MIN, and HEFT.

show abstract

Finding Significant Project Issues with Machine Learning

Vajjhala

Strang²

2023

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems

Cited by 9 publications

References 34 publications

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Heuristic Search Based Localization in Mobile Computational Grid

Finding Significant Project Issues with Machine Learning

Contact Info

Product

Resources

About