Load balancing in MapReduce environments for data intensive applications

2011 ◽  
Author(s):  
Yang Liu ◽  
Maozhen Li ◽  
Nasullah Khalid Alham ◽  
Suhel Hammoud ◽  
Mahesh Ponraj
Keyword(s):  
Load Balancing ◽  
Data Intensive ◽  
Data Intensive Applications

Priority Based Load Balancing in Cloud for Data Intensive Applications

2012 ◽  
Vol 5 (4) ◽  
pp. 200-206
Author(s):  
E. Iniya Nehru ◽  
S. Sujatha ◽  
P. Seethalaks ◽  
N. Sridharan
Keyword(s):  
Load Balancing ◽  
Data Intensive ◽  
Data Intensive Applications

scholarly journals Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

2004 ◽  
Vol 12 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Johan Parent ◽  
Katja Verbeeck ◽  
Jan Lemeire ◽  
Ann Nowe ◽  
Kris Steenhaut ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Heterogeneous Systems ◽  
Machine Learning Techniques ◽  
Parallel Applications ◽  
Communication Overhead ◽  
Data Intensive ◽  
Multi Agent ◽  
State Coordination ◽  
Data Intensive Applications

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

scholarly journals User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
P. Suresh ◽  
P. Balasubramanie
Keyword(s):  
Load Balancing ◽  
User Satisfaction ◽  
Scheduling Algorithm ◽  
Efficient Utilization ◽  
Grid Environment ◽  
Data Intensive ◽  
Scheduling Model ◽  
User Demand ◽  
Overall Performance ◽  
Data Intensive Applications

Grid computing is a collection of computational and data resources, providing the means to support both computational intensive applications and data intensive applications. In order to improve the overall performance and efficient utilization of the resources, an efficient load balanced scheduling algorithm has to be implemented. The scheduling approach also needs to consider user demand to improve user satisfaction. This paper proposes a dynamic hierarchical load balancing approach which considers load of each resource and performs load balancing. It minimizes the response time of the jobs and improves the utilization of the resources in grid environment. By considering the user demand of the jobs, the scheduling algorithm also improves the user satisfaction. The experimental results show the improvement of the proposed load balancing method.

scholarly journals 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1709
Author(s):  
Agbotiname Lucky Imoize ◽  
Oluwadara Adedeji ◽  
Nistha Tandiya ◽  
Sachin Shetty
Keyword(s):  
Wireless Communication ◽  
Psychological Health ◽  
Future Research ◽  
Agriculture Education ◽  
Social Psychological ◽  
Research Issues ◽  
Data Intensive ◽  
Wireless Communication Network ◽  
Data Intensive Applications

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

scholarly journals EZIOTracer

2021 ◽  
Vol 55 (1) ◽  
pp. 88-98
Author(s):  
Mohammed Islam Naas ◽  
François Trahay ◽  
Alexis Colin ◽  
Pierre Olivier ◽  
Stéphane Rubini ◽  
...  
Keyword(s):  
Analysis Framework ◽  
Comprehensive Understanding ◽  
Kernel Space ◽  
Data Intensive ◽  
Storage Performance ◽  
Performance Requirements ◽  
Memory Footprint ◽  
Extreme Performance ◽  
Data Intensive Applications ◽  
Kernel Level

Tracing is a popular method for evaluating, investigating, and modeling the performance of today's storage systems. Tracing has become crucial with the increase in complexity of modern storage applications/systems, that are manipulating an ever-increasing amount of data and are subject to extreme performance requirements. There exists many tracing tools focusing either on the user-level or the kernel-level, however we observe the lack of a unified tracer targeting both levels: this prevents a comprehensive understanding of modern applications' storage performance profiles. In this paper, we present EZIOTracer, a unified I/O tracer for both (Linux) kernel and user spaces, targeting data intensive applications. EZIOTracer is composed of a userland as well as a kernel space tracer, complemented with a trace analysis framework able to merge the output of the two tracers, and in particular to relate user-level events to kernel-level ones, and vice-versa. On the kernel side, EZIOTracer relies on eBPF to offer safe, low-overhead, low memory footprint, and flexible tracing capabilities. We demonstrate using FIO benchmark the ability of EZIOTracer to track down I/O performance issues by relating events recorded at both the kernel and user levels. We show that this can be achieved with a relatively low overhead that ranges from 2% to 26% depending on the I/O intensity.

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