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Smart data structures

Jonathan Eastep, David Wingate, Anant Agarwal
2011 Proceedings of the 8th ACM international conference on Autonomic computing - ICAC '11  
Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel  ...  Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.  ...  Smart Data Structures take an online, adaptive approach to balancing tradeoffs. Through Reinforcement Learning, our design is robust to changes in effective machine performance.  ... 
doi:10.1145/1998582.1998587 dblp:conf/icac/EastepWA11 fatcat:nn5edmoxjvdlhl7sdwbgnfy5wa

Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

Amanpreet Kaur, Associate Professor, Chandigarh Engineering College, Landran (Mohali), Bikrampal Kaur, Parminder Singh, Mandeep Singh Devgan, Harpreet Kaur Toor
2020 International Journal of Information Technology and Computer Science  
In cloud computing, Deep Learning (DL) techniques can be used to achieve QoS such as improve resource utilization and throughput; while reduce latency, response time and cost, balancing load across machines  ...  Load balancing is a significant aspect of cloud computing which is essential for identical load sharing among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs  ...  Hsiao et al. (2013) solved the problem of load imbalance by presenting a novel fully distributed algorithm for load rebalancing in comparison to centralized approach used in Hadoop distributed file system  ... 
doi:10.5815/ijitcs.2020.03.02 fatcat:ncsbiikxw5aldkrqgjr47q6s6e

Workstation capacity tuning using reinforcement learning

Aharon Bar-Hillel, Amir Di-Nur, Liat Ein-Dor, Ran Gilad-Bachrach, Yossi Ittach
2007 Proceedings of the 2007 ACM/IEEE conference on Supercomputing - SC '07  
We present three different reinforcement learning algorithms, which generate a dynamic policy by changing the number of concurrent running jobs according to the job types and machine state.  ...  On multi-core servers, the average throughput improvement is approximately 40%, which hints at the enormous improvement potential of such a tuning mechanism with the gradual transition to multi-core machines  ...  We argue that machine learning based techniques can be used to monitor the environment and tune the system, allowing operators of distributed systems to focus on service-level tradeoffs and release them  ... 
doi:10.1145/1362622.1362666 dblp:conf/sc/Bar-HillelDEGI07 fatcat:yu6l5wmqizf67oaulgihtqaqua

Cloud based computational intelligence approaches to machine learning and big data analytics: literature survey

D Venkata Siva Reddy, R Vasanth Kumar Mehta
2018 International Journal of Engineering & Technology  
to Cloud based approaches it provides an ease of set up or testing and is economical.Thus there is a demand for cloud computing and machine learning techniques with Hadoop or Spark.Mainly we are focusing  ...  It has to overcome many challenges.  ...  Machine learning approaches are capable to find out and demonstrate the different prototypes hidden in the data. Labeled data for machine learning is often very difficult and expensive to obtain.  ... 
doi:10.14419/ijet.v7i1.9.9817 fatcat:hnxptotwgzachn3kx2ykoj76xa

A Survey of Load Balancing Techniques for Data Intensive Computing [chapter]

Zhiquan Sui, Shrideep Pallickara
2011 Handbook of Data Intensive Computing  
Such data is often processed concurrently on a distributed collection of machines to ensure reasonable completion times.  ...  Our focus is a survey of the frameworks, APIs, and schemes used to load balance processing of voluminous data on a collection of machines while processing large data volumes in settings such as analytics  ...  Machine learning algorithms are widely used in the load balancing area. Genetic algorithm tends to be the most convenient for static load.  ... 
doi:10.1007/978-1-4614-1415-5_6 fatcat:aj7uycimt5f65aeku64lbc7chm

Experiments with a Machine-centric Approach to Realise Distributed Emergent Software Systems

Roberto Rodrigues Filho, Barry Porter
2016 Proceedings of the 15th International Workshop on Adaptive and Reflective Middleware - ARM 2016  
Modern distributed systems are exposed to constant changes in their operating environment, leading to high uncertainty.  ...  We argue for a machine-centric approach to this problem, in which the desired behaviour is autonomously learned and emerges at runtime from a large pool of small alternative components, as a continuous  ...  Roberto Rodrigues Filho would like to thank his sponsor, CAPES Brazil, for the scholarship grant BEX 13292/13-7.  ... 
doi:10.1145/3008167.3008168 dblp:conf/middleware/FilhoP16 fatcat:dg26wnm73jefzkr636gsirt7oe

Dynamic Task Scheduling using Trained Neural Network and Genetic Algorithm

Suhani Kumari, Himanshu Yadav, Chetan Agrawal
2019 IJARCCE  
First was TLBO (Teacher Learning Based Optimization) genetic algorithms which find the correct position for the process to execute.  ...  Result shows that proposed GNNLB model has overcome various evaluation parameters on different scale as compared to previous approaches adopt by researchers.  ...  As a result, existing load balancing schemes developed for cloud data centers cannot be applied directly and it is very necessary to redesign a cost-ware load balancing algorithm for the fog cloud system  ... 
doi:10.17148/ijarcce.2019.8525 fatcat:kivwy3bv7zf5hluh57mxen7o54

An Adaptive Learning System based on Cloud Computing: Implementation and Evaluation of BDS

Yi Liao, Lei Huang, Hang Zhou, Bo Li
2016 International Journal of Grid and Distributed Computing  
This algorithm is applied in the cloud computing system to optimize each node to coordinate the operation of the BDS server.  ...  This paper focuses on investigating three distributed solutions proposed for load balancing: an active clustering algorithm, a random algorithm and a honey bee foraging algorithm.  ...  To this end, loads are transferred in accordance with a load-balancing mechanism that dynamically migrates virtual machines when the system load is not balanced.  ... 
doi:10.14257/ijgdc.2016.9.8.33 fatcat:2kv57hyafrhcxdvdgdfnwumilu

Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM [chapter]

Prasanna Balaprakash, Yuri Alexeev, Sheri A. Mickelson, Sven Leyffer, Robert Jacob, Anthony Craig
2015 Lecture Notes in Computer Science  
We therefore developed machine-learning-based loadbalancing algorithm. It involves fitting a surrogate model to a small number of load-balancing configurations and their corresponding runtimes.  ...  Compared with the current practice of expert-knowledge-based enumeration over feasible configurations, the machine-learning-based load-balancing algorithm requires six times fewer evaluations to find the  ...  In [18] , the authors carried out an experimental comparison of eleven static load-balancing algorithms for heterogeneous distributed computing systems.  ... 
doi:10.1007/978-3-319-17353-5_7 fatcat:4a5gzcpdbvcllm6qi53pyr4bnu

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during  ...  We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.  ...  An Ant Colony Optimization algorithm was proposed by Li et al. [9] for load balancing addressing the distribution of the load across the system and minimizing the makespan.  ... 
doi:10.35940/ijitee.c8836.029420 fatcat:4sng5fetizggvchpnjwrvmz4s4

Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

João Nascimento, André Maitelli, Carla Maitelli, Anderson Cavalcanti
2021 Sensors  
machine learning.  ...  Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification  ...  The results of this research confirmed the feasibility of applying machine-learning to the diagnostic of operation conditions in sucker-rod pumping systems.  ... 
doi:10.3390/s21134546 fatcat:mbdq6dhffjhnref76uxgdghryu

A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Miftah Al Karim, Jonathan Currie, Tek-Tjing Lie
2016 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)  
Here multiple machine learning algorithms have been implemented to provide the secondary control where a primary control scheme is insufficient to maintain a stable voltage after a sudden change in the  ...  Based on different contingencies the proposed method would suggest different machine learning algorithms which are previously trained with similar data.  ...  This paper proposes a distributed machine learning algorithm architecture, established on the synchronous generators in order to provide a secondary control scheme under different scenarios.  ... 
doi:10.1109/isgt-asia.2016.7796454 fatcat:bdv5u2zjm5g5zfpoi5bxq6xz2i

Container Scheduling Techniques: A Survey and Assessment

Imtiaz Ahmad, Mohammad Gh. AlFailakawi, Asayel AlMutawa, Latifa Alsalman
2021 Journal of King Saud University: Computer and Information Sciences  
, meta-heuristics and machine learning.  ...  The survey is structured around classifying the scheduling techniques into four categories based on the type of optimization algorithm employed to generate the schedule namely mathematical modeling, heuristics  ...  Acknowledgment The authors would like to thank the anonymous reviewers for their invaluable comments and constructive criticism to improve the quality of the manuscript.  ... 
doi:10.1016/j.jksuci.2021.03.002 fatcat:ntx6dmydynaqxioolkx64n2ota

Self-Organizing Networks for 5G and Beyond: A View from the Top

Andreas G. Papidas, George C. Polyzos
2022 Future Internet  
the strong reliance on machine learning (ML) and artificial intelligence (AI).  ...  We analyze SON applications' rationale and operation, the design and dimensioning of SON systems, possible deficiencies and conflicts that occur through the parallel operation of functions, and describe  ...  SON Application Machine Learning Technique Algorithms That Can Be Used Q-learning Mobility Load Balancing (MLB) RL or DRL, UL, SL Fuzzy Q-learning Dynamic Programming K-means clustering Polynomial regression  ... 
doi:10.3390/fi14030095 fatcat:ouwujylbgfhljajookgvyvt7yy

Scalable Learning Paradigms for Data-Driven Wireless Communication [article]

Yue Xu, Feng Yin, Wenjun Xu, Chia-Han Lee, Jiaru Lin, Shuguang Cui
2020 arXiv   pre-print
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.  ...  On the other hand, we discuss the learning algorithms and model training strategies performed at each individual node from a local perspective.  ...  SCALABLE MACHINE LEARNING ALGORITHMS While the scalable learning frameworks specify how distributed nodes communicate and coordinate with each other from a global view, the machine learning algorithms  ... 
arXiv:2003.00474v1 fatcat:kd6plphwgbfvbdyylcn4jk24uq
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