Thread scheduling using ant colony optimization: An intelligent scheduling approach towards minimal information leakage

Anjaria, Kushal; Mishra, Arun

doi:10.1016/j.kijoms.2017.08.003

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…Hybrid recommendation systems: hybrid recommendation systems allow different recommendation systems to complement each other and can improve novelty while alleviating the cold start problem to some extent. Most of the previous related studies use auxiliary information of items, mainly textual information such as abstracts of papers [14], item descriptions, and reviews. Many approaches are used by hybrid recommender systems for document modeling, such as Latent Dirichlet Allocation (LDA), Stacked Denoising Autoencoders (SDA), or Convolutional Neural Network (CNN) [15].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization of an Intelligent Music‐Playing System Based on Network Communication

Zhang

2021

Complexity

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Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization of an Intelligent Music‐Playing System Based on Network Communication

Zhang

2021

Complexity

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“…The tasks in this category are scheduled during compilation time, as shown in figure (2), so the resulting overheads during the system operation in terms of switching between tasks, cache emptying and resources obtaining are very few compared to the dynamic category. Low overheads are considered as one of the features of this category.…”

Section: Multiprocessor Schedulingmentioning

confidence: 99%

“…Full migration scheduling applies to all types of tasks. [2][23] [4] In terms of the nature of events, scheduling is classified into two categories: 2.1.8 Event driven scheduling:…”

Section: Multiprocessor Schedulingmentioning

confidence: 99%

“…In this scenario, the performance of the proposed method was compared with the default method in terms of context switching overhead based on the data shown in the table (2).…”

Section: Context Switching Scenariomentioning

confidence: 99%

“…In this scenario, the performance of the EELB method was compared with the Default method in terms of scheduling overheads based on the data shown in the table (2). Figure (7) shows scheduling overheads scenario results when working on two, four and eight cores.…”

Section: Scheduling Overheads Scenariomentioning

confidence: 99%

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Development of Energy- Efficient Load Balancing Scheduling Algorithm in Real-time Systems

نتيفه¹,

حجازية²

2022

Journal of Engineering Sciences and Information Technology

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Real- time systems are considered as one of the most important topics that have attracted the attention of researchers in various scientific and technical fields due to their widespread in many fields of communication, informatics, operating systems and automatic control systems. The basic idea in real- time systems is the execution of a set of tasks that are assigned to CPUs, within a time constraint associated with each of them, and this time constraint is called a deadline. Task scheduling in real- time systems is the main factor in determining the success or failure of the system. Therefore, many studies have imposed suggestions and hypotheses for improving scheduling, according to the underlined platform, whether it is single- processor, multi- processor, or multi- core processor. Researchers have recently focused on studying and providing solutions to scheduling problems, taking into account energy consumption since energy consumption today plays an important role in determining the efficiency and reliability of the system. Energy consumption can be saved either statically by turning the processor into sleep mode or dynamically by changing the frequency of the processor cores and thus reducing power consumption. However, a slight increase in frequency leads to executing more tasks in less time and thus can lead to an increase in the efficiency of the real- time system by reducing tasks which may miss its deadline at the time of execution. In this research, a new method has been proposed to control the processor frequency in an optimal manner that makes a balanced trade- off between power consumption and execution of tasks in real- time systems.

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Advanced Ant Colony Optimization in Healthcare Scheduling

Behmanesh

Rahimi

Zandieh

et al. 2020

Evolutionary Computation in Scheduling

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Thread scheduling using ant colony optimization: An intelligent scheduling approach towards minimal information leakage

Cited by 9 publications

References 22 publications

[Retracted] Optimization of an Intelligent Music‐Playing System Based on Network Communication

[Retracted] Optimization of an Intelligent Music‐Playing System Based on Network Communication

Development of Energy- Efficient Load Balancing Scheduling Algorithm in Real-time Systems

Advanced Ant Colony Optimization in Healthcare Scheduling

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