scholarly journals Load Balancing Scheme for Effectively Supporting Distributed In-Memory Based Computing

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 546
Author(s):  
Kyoungsoo Bok ◽  
Kitae Choi ◽  
Dojin Choi ◽  
Jongtae Lim ◽  
Jaesoo Yoo
Keyword(s):  
Big Data ◽  
Load Balancing ◽  
Real Time ◽  
Data Distribution ◽  
Data Access ◽  
Memory Systems ◽  
Performance Evaluations ◽  
Digital Data ◽  
Data Migration ◽  
Load Balancer

As digital data have increased exponentially due to an increasing number of information channels that create and distribute the data, distributed in-memory systems were introduced to process big data in real-time. However, when the load is concentrated on a specific node in a distributed in-memory environment, the data access performance is degraded, resulting in an overall degradation in the processing performance. In this paper, we propose a new load balancing scheme that performs data migration or replication according to the loading status in heterogeneous distributed in-memory environments. The proposed scheme replicates hot data when the hot data occurs on the node where a load occurs. If the load of the node increases in the absence of hot data, the data is migrated through a hash space adjustment. In addition, when nodes are added or removed, data distribution is performed by adjusting the hash space with the adjacent nodes. The clients store the metadata of the hot data and reduce the access of the load balancer through periodic synchronization. It is confirmed through various performance evaluations that the proposed load balancing scheme improves the overall load balancing performance.

Conceptualization, Operationalization, and Validation of the Digital Data Stream Readiness Index

2018 ◽  
Vol 26 (4) ◽  
pp. 92-112 ◽  
Author(s):  
Elisabetta Raguseo ◽  
Federico Pigni ◽  
Gabriele Piccoli
Keyword(s):  
Big Data ◽  
Real Time ◽  
Value Creation ◽  
Data Stream ◽  
Organizational Readiness ◽  
Digital Data ◽  
Measurement Instruments ◽  
Scholarly Research ◽  
Four Dimensions ◽  
Significant Attention

This article describes how in their search for value creation, companies are investing considerable resources in so-called “Big Data” initiatives. A peculiar aspect of these initiatives is the increasing availability of real-time streams of data. Successfully leveraging these streams to extract value is emerging as a critical competence for the modern firm. Despite the significant attention received, scholarly research on Digital Data Stream (DDS) remains insufficient. More importantly, there are no specialized definitions and measurement instruments that can move the field forward by initiating a cumulative research tradition. This article can provide clarification on key definitions, differentiating DDS from Big Data. Drawing on the organizational readiness concept, the DDS readiness index develops as a measure of organizational readiness to exploit real-time digital data. This article will conceptualize, define, operationalize and validate the index. By identifying the four dimensions of mindset, skillset, dataset and toolset as the elements of the DDS readiness index and discussing its managerial and research implications

scholarly journals Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

2019 ◽  
Vol 9 (1) ◽  
pp. 4162-4171
Keyword(s):  
Big Data ◽  
Load Balancing ◽  
Response Time ◽  
Support Vector Regression ◽  
Virtual Machine ◽  
Data Centers ◽  
Migration Time ◽  
Support Vector ◽  
Load Balancer ◽  
Cloud User

The process of analyzing big data and other valuable information is a significant process in the cloud. Since big data processing utilizes a large number of resources for completing certain tasks. Therefore, the incoming tasks are allocated with better utilization of resources to minimize the workload across the server in the cloud. The conventional load balancing technique failed to balance the load effectively among data centers and dynamic QoS requirements of big data application. In order to improve the load balancing with maximum throughput and minimum makespan, Support Vector Regression based MapReduce Throttled Load Balancing (SVR-MTLB) technique is introduced. Initially, a large number of cloud user requests (data/file) are sent to the cloud server from different locations. After collecting the cloud user request, the SVR-MTLB technique balances the workload of the virtual machine with the help of support vector regression. The load balancer uses the index table for maintaining the virtual machines. Then, map function performs the regression analysis using optimal hyperplane and provides three resource status of the virtual machine namely overloaded, less loaded and balanced load. After finding the less loaded VM, the load balancer sends the ID of the virtual machine to the data center controller. The controller performs migration of the task from an overloaded VM to a less loaded VM at run time. This in turn assists to minimize the response time. Experimental evaluation is carried out on the factors such as throughput, makespan, migration time and response time with respect to a number of tasks. The experimental results reported that the proposed SVR-MTLB technique obtains high throughput with minimum response time, makespan as well as migration time than the state -of -the -art methods.

Digital Data Streams: Creating Value from the Real-Time Flow of Big Data

2016 ◽  
Vol 58 (3) ◽  
pp. 5-25 ◽  
Author(s):  
Federico Pigni ◽  
Gabriele Piccoli ◽  
Richard Watson
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Streams ◽  
Digital Data ◽  
The Real ◽  
Time Flow

scholarly journals Resilience enhancement of container-based cloud load balancing service

2018 ◽  
Author(s):  
Dongsheng Zhang
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Real Time ◽  
Significant Role ◽  
Current Load ◽  
Web Traffic ◽  
Computing Environments ◽  
Load Balancing Algorithm ◽  
Load Balancer ◽  
Computing Infrastructure

Web traffic is highly jittery and unpredictable. Load balancer plays a significant role in mitigating the uncertainty in web environments. With the growing adoption of cloud computing infrastructure, software load balancer becomes more common in recent years. Current load balancer services distribute the network requests based on the number of network connections to the backend servers. However, the load balancing algorithm fails to work when other resources such as CPU or memory in a backend server saturates. We experimented and discussed the resilience evaluation and enhancement of container-based software load balancer services in cloud computing environments. We proposed a pluggable framework that can dynamically adjust the weight assigned to each backend server based on real-time monitoring metrics.

scholarly journals Improved Load Balancing on Software Defined Network-based Equal Cost Multipath Routing in Data Center Network

2018 ◽  
Vol 10 (3) ◽  
pp. 157 ◽  
Author(s):  
Ramadhika Dewanto ◽  
Rendy Munadi ◽  
Ridha Muldina Negara
Keyword(s):  
Standard Deviation ◽  
Load Balancing ◽  
Real Time ◽  
Data Center ◽  
Multipath Routing ◽  
Software Defined Network ◽  
Network Congestion ◽  
Available Bandwidth ◽  
Lower Load ◽  
Load Balancer

Equal Cost Multipath Routing (ECMP) is a routing application where all available paths between two nodes is utilized by statically mapping each path to possible traffics between source and destination hosts in a network. This configuration can lead to congestion if there are two or more traffics being transmitted into paths with overlapping links, despite the availability of less busy paths. Software Defined Networking (SDN) has the ability to increase the dynamicity of ECMP by allowing controller to monitor available bandwidths of all links in the network in real-time. The measured bandwidth is then implemented as the basis of the calculation to determine which path a traffic will take.  In this research, a SDN-based ECMP application that can prevent network congestion is made by measuring available bandwidth of each available paths beforehand, thus making different traffics transmitted on non-overlapped paths as much as possible. The proposed scheme increased the throughput by 14.21% and decreased the delay by 99% in comparison to standard ECMP when congestion occurs and has 75.2% lower load standard deviation in comparison to round robin load balancer.

scholarly journals A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment

2020 ◽  
Vol 25 (6) ◽  
pp. 771-782
Author(s):  
Gutta Sridevi ◽  
Midhunchakkravarthy
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Real Time ◽  
Parameters Estimation ◽  
Computing Environment ◽  
Estimation Model ◽  
Cloud Computing Environment ◽  
The Real ◽  
Multi Level ◽  
Load Balancer

As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.

scholarly journals Caring for (Big) Data: An Introduction to Research Methodologies and Ethical Challenges in Digital Migration Studies

2021 ◽  
pp. 1-21
Author(s):  
Marie Sandberg ◽  
Luca Rossi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Activity ◽  
Data Access ◽  
Digital Data ◽  
Data Sets ◽  
Ethical Challenges ◽  
Migration Studies ◽  
Critical Approach ◽  
Migration Research

AbstractDigital technologies present new methodological and ethical challenges for migration studies: from ensuring data access in ethically viable ways to privacy protection, ensuring autonomy, and security of research participants. This Introductory chapter argues that the growing field of digital migration research requires new modes of caring for (big) data. Besides from methodological and ethical reflexivity such care work implies the establishing of analytically sustainable and viable environments for the respective data sets—from large-scale data sets (“big data”) to ethnographic materials. Further, it is argued that approaching migrants’ digital data “with care” means pursuing a critical approach to the use of big data in migration research where the data is not an unquestionable proxy for social activity but rather a complex construct of which the underlying social practices (and vulnerabilities) need to be fully understood. Finally, it is presented how the contributions of this book offer an in-depth analysis of the most crucial methodological and ethical challenges in digital migration studies and reflect on ways to move this field forward.

Load Balancing for Distributed Processing of Real-time Spatial Big Data Stream

2017 ◽  
Vol 44 (11) ◽  
pp. 1209-1218
Author(s):  
Susik Yoon ◽  
Jae-Gil Lee
Keyword(s):  
Big Data ◽  
Load Balancing ◽  
Real Time ◽  
Data Stream ◽  
Distributed Processing ◽  
Spatial Big Data
Sign in / Sign up

Export Citation Format

Share Document