Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud

2021 ◽  
Author(s):  
Chunlin Li ◽  
Qianqian Cai ◽  
Luo Youlong
Keyword(s):  
Load Balancing ◽  
Data Placement ◽  
Capacity Limitation ◽  
Geographically Distributed ◽  
Distributed Cloud

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

2021 ◽  
Vol 224 ◽  
pp. 107050
Author(s):  
Chunlin Li ◽  
Jun Liu ◽  
Weigang Li ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Data Placement ◽  
Cloud Systems ◽  
Distributed Cloud

Load Balancing to Increase the Consistency of Replicas in Data Grids

2012 ◽  
pp. 42-57
Author(s):  
Ghalem Belalem ◽  
Naima Belayachi ◽  
Radjaa Behidji ◽  
Belabbes Yagoubi
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Data Grids ◽  
Distributed Resources ◽  
Data Set ◽  
Large Scale Systems ◽  
Consistency Management ◽  
Geographically Distributed ◽  
Replication Technique ◽  
Improve Service Quality

Data grids are current solutions to the needs of large scale systems and provide a set of different geographically distributed resources. Their goal is to offer an important capacity of parallel calculation, ensure a data effective and rapid access, improve the availability, and tolerate the breakdowns. In such systems, however, these advantages are possible only by using the replication technique. The use of this technique raises the problem of maintaining consistency of replicas of the same data set. In order to guarantee replica set reliability, it is necessary to have high coherence. This fact, however, penalizes performance. In this paper, the authors propose studying balancing influence on replica quality. For this reason, a service of hybrid consistency management is developed, which combines the pessimistic and optimistic approaches and is extended by a load balancing service to improve service quality. This service is articulated on a hierarchical model with two levels.

scholarly journals Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12
Author(s):  
Xicheng Tan ◽  
Liping Di ◽  
Yanfei Zhong ◽  
Nengcheng Chen ◽  
Fang Huang ◽  
...  
Keyword(s):  
Service Management ◽  
Multiple Perspectives ◽  
Distributed Environment ◽  
Integration Algorithm ◽  
Research Activities ◽  
Execution Strategy ◽  
Geographically Distributed ◽  
Model Execution ◽  
Distributed Cloud

To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.

scholarly journals Analysis of Effective Load Balancing Techniques in Distributed Environment

2020 ◽  
Author(s):  
Anju Shukla ◽  
Shishir Kumar ◽  
Harikesh Singh
Keyword(s):  
Distributed Computing ◽  
Load Balancing ◽  
Performance Optimization ◽  
Resource Selection ◽  
High Performance ◽  
Distributed Environment ◽  
Distributed Resources ◽  
Heterogeneous Services ◽  
Future Extension ◽  
Geographically Distributed

Computational approaches contribute a significance role in various fields such as medical applications, astronomy, and weather science, to perform complex calculations in speedy manner. Today, personal computers are very powerful but underutilized. Most of the computer resources are idle; 75% of the time and server are often unproductive. This brings the sense of distributed computing, in which the idea is to use the geographically distributed resources to meet the demand of high-performance computing. The Internet facilitates users to access heterogeneous services and run applications over a distributed environment. Due to openness and heterogeneous nature of distributed computing, the developer must deal with several issues like load balancing, interoperability, fault occurrence, resource selection, and task scheduling. Load balancing is the mechanism to distribute the load among resources optimally. The objective of this chapter is to discuss need and issues of load balancing that evolves the research scope. Various load balancing algorithms and scheduling methods are analyzed that are used for performance optimization of web resources. A systematic literature with their solutions and limitations has been presented. The chapter provides a concise narrative of the problems encountered and dimensions for future extension.

Geographically Distributed Cloud-Based Collaborative Application

2012 ◽  
pp. 248-274 ◽  
Author(s):  
Bogdan Solomon ◽  
Dan Ionescu ◽  
Cristian Gadea ◽  
Marin Litoiu
Keyword(s):  
Response Times ◽  
Multimedia Content ◽  
Single Server ◽  
Web Content ◽  
Web Based ◽  
Geographically Distributed ◽  
Distributed Execution ◽  
User Demand ◽  
Distributed Cloud ◽  
Collaborative Application

The amount of multimedia content on the Internet has been growing at a remarkable rate, and users are increasingly looking to share online media with colleagues and friends on social networks. Several commercial and academic solutions have attempted to make it easier to share this large variety of online content with others, but they are generally limited to only sending Web links. At the same time, existing products have not been able to provide a scalable system that synchronizes disparate Web content sources among many users in real-time. Such a goal is especially desired in order to provide the benefits of cloud deployments to collaborative applications. Many Web-based applications cannot predict the number of connections that they may need to handle. As such, applications must either provision a higher number of servers in anticipation of more traffic, or be faced with a degradation of the user experience when a large number of clients connect to the application. Cloud-based deployments can alleviate these issues by allowing the application’s server base to auto scale based on the user demand. A cloud deployment can also employ servers in different geographic locations in order to offer better latency and response times to its clients. Moving a collaborative application from using a single server to a cloud and then to a distributed cloud is not a trivial matter, however. This chapter will show our experience with how such a transition can be performed, and will present the architectural changes that had to be implemented at the server and cloud level in order to create a distributed execution that resides in the cloud.

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