Stochastic Load Balancing for Virtual Resource Management in Datacenters

2020 ◽  
Vol 8 (2) ◽  
pp. 459-472 ◽  
Author(s):  
Lei Yu ◽  
Liuhua Chen ◽  
Zhipeng Cai ◽  
Haiying Shen ◽  
Yi Liang ◽  
...  
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Stochastic Load

Mobile Resource Management Load Balancing Strategy

2015 ◽  
Vol 22 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Krisztián Pándi ◽  
Hassan Charaf
Keyword(s):  
Resource Management ◽  
Load Balancing

scholarly journals Monitoring and Resource Management in P2P Grid-Based Web Services

2012 ◽  
Vol 1 (2) ◽  
pp. 71-84
Author(s):  
Djafri Laouni ◽  
Mekki Rachida
Keyword(s):  
Fault Tolerance ◽  
Resource Management ◽  
Load Balancing ◽  
Grid Computing ◽  
Grid Infrastructure ◽  
P2p Computing ◽  
P2p Systems ◽  
Processing Power ◽  
Service Load ◽  
High Bandwidth

Grid computing has recently emerged as a response to the growing demand for resources (processing power, storage, etc.) exhibited by scientific applications. However, as grid sizes increase, the need for self-organization and dynamic reconfigurations is becoming more and more important. Since such properties are exhibited by P2P systems, the convergence of grid computing and P2P computing seems natural. However, using P2P systems (usually running on the Internet) on a grid infrastructure (generally available as a federation of SAN-based clusters interconnected by high-bandwidth WANs) may raise the issue of the adequacy of the P2P communication mechanisms. Among the interesting properties of P2P systems is the  volatility of  peers  which  causes  the  need  for  integration  of  a  service fault tolerance. And service Load balancing,   As a solution, we proposed a mechanism of fault tolerance and model of Load balancing  adapted to a grid P2P model, named SGRTE (Monitoring and Resource Management, Fault Tolerances and Load Balancing).DOI: 10.18495/comengapp.12.071084

Resource Scheduling Techniques in Utility Computing

2016 ◽  
pp. 1159-1179
Author(s):  
Inderveer Chana ◽  
Tarandeep Kaur
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Resource Scheduling ◽  
Utility Computing ◽  
On Demand ◽  
Geographically Dispersed ◽  
Optimal Resource ◽  
Dynamic Resource ◽  
Characteristic Features

Utility Computing offers on-demand services from a shared pool of resources and can be envisaged to be a benchmark in the IT development. The capability to provide on-demand services involves management of large number of resources that are geographically dispersed and thus poses a number of resource management and scheduling challenges in the domain of resource heterogeneity, dynamic resource locations and load balancing. Proficient resource allocations and efficient scheduling helps in achieving optimal resource utilization and hence enhances the performance of the system. This paper evaluates existing resource management systems, listing their key characteristic features and highlighting the factors that make the existing systems excel upon each other. It also discusses various resource scheduling techniques currently available and characterizes the techniques based on Quality of Service (QoS) parameters supported by them along with the classification on basis of their operating environment and further extends towards load balancing and energy efficiency support if available.

Resource Management in Real Time Distributed System with Security Constraints

2012 ◽  
pp. 230-251
Author(s):  
Sarsij Tripathi ◽  
Rama Shankar Yadav ◽  
Ranvijay ◽  
Rajib L. Jana
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Real Time ◽  
Distributed System ◽  
Research Work ◽  
Tracking Systems ◽  
Global Village ◽  
Future Directions ◽  
The World ◽  
Work Done

The world has become a global village. Today applications are developed which require sharing of resources dispersed geographically to fulfill the need of the users. In most cases applications turn out to be time bound thus leading to Real Time Distributed System (RTDS). Online Banking, Online Multimedia Applications, Real Time Databases, and Missile tracking systems are some examples of these types of applications. These applications face many challenges in the present scenario particularly in resource management, load balancing, security, and deadlock. The heterogeneous nature of the system exacerbates the challenges. This paper provides a widespread survey of research work reported in RTDS. This review has covered the work done in the field of resource management, load balancing, deadlock, and security. The challenges involved in tackling these issues is presented and future directions are discussed.

Ant colony optimization algorithm for load balancing in grid computing

2012 ◽  
Author(s):  
Ku Ruhana Ku-Mahamud ◽  
Aniza Mohamed Din
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Grid Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Computing System ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Grid Resource ◽  
Grid Resource Management

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. The proposed algorithm is known as the enhance ant colony optimization (EACO). The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job.Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization. Experimental results show that EACO produced better grid resource management solution.

SLA-aware Stochastic Load Balancing in Dynamic Cloud Environment

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Sounak Banerjee ◽  
Sarbani Roy ◽  
Sunirmal Khatua
Keyword(s):  
Load Balancing ◽  
Cloud Environment ◽  
Stochastic Load

Resource Management in Real Time Distributed System with Security Constraints

2011 ◽  
Vol 2 (2) ◽  
pp. 38-58 ◽  
Author(s):  
Sarsij Tripathi ◽  
Rama Shankar Yadav ◽  
Ranvijay ◽  
Rajib L. Jana
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Real Time ◽  
Distributed System ◽  
Research Work ◽  
Global Village ◽  
Future Directions ◽  
The World ◽  
Heterogeneous Nature ◽  
Work Done

The world has become a global village. Today applications are developed which require sharing of resources dispersed geographically to fulfill the need of the users. In most cases applications turn out to be time bound thus leading to Real Time Distributed System (RTDS). Online Banking, Online Multimedia Applications, Real Time Databases, and Missile tracking systems are some examples of these types of applications. These applications face many challenges in the present scenario particularly in resource management, load balancing, security, and deadlock. The heterogeneous nature of the system exacerbates the challenges. This paper provides a widespread survey of research work reported in RTDS. This review has covered the work done in the field of resource management, load balancing, deadlock, and security. The challenges involved in tackling these issues is presented and future directions are discussed.

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