EDM: An Endurance-Aware Data Migration Scheme for Load Balancing in SSD Storage Clusters

2014 ◽  
Author(s):  
Jiaxin Ou ◽  
Jiwu Shu ◽  
Youyou Lu ◽  
Letian Yi ◽  
Wei Wang
Keyword(s):  
Load Balancing ◽  
Data Migration

LOAD BALANCING IN HOME-BASED SOFTWARE DSMS

2001 ◽  
Vol 12 (03) ◽  
pp. 307-324
Author(s):  
WEISONG SHI ◽  
ZHIMIN TANG
Keyword(s):  
Load Balancing ◽  
Dynamic Scheduling ◽  
Critical Issue ◽  
Research Area ◽  
Data Migration ◽  
Loop Scheduling ◽  
Scientific Applications ◽  
Task Migration ◽  
Dynamic Task ◽  
Home Based

Load balancing is a critical issue for achieving good performance in parallel and distributed systems. However, this issue is neglected in the research area of software DSMs in the past decade. Based on the observation that scientific applications can be classified into two categories: iterative and non-iterative, we propose two dynamic scheduling schemes for these two cases respectively in this paper. For iterative scientific applications, a dynamic task migration technique is proposed which characterizes itself with integrating computation migration and data migration together. An affinity-based self scheduling (ABS) is proposed for non-iterative scientific applications, which take both the static and dynamic processor affinity into consideration when scheduling. The target experiment platform is a state-of-the-art home-based DSM system named JIAJIA. Performance evaluation results show that the novel task migration scheme improves the performance ranging from 36% to 50% compared with a static task allocation scheme in a metacomputing environment, and performs better than traditional task (computation-only) migration approach about 12.5% for MAT, and 37.5% for SOR and EM3D. Higher resource utilization is achieved via the new task migration scheme too. In comparison with other loop scheduling schemes, the ABS achieves the best performance among all scheduling schemes in a metacomputing environment because of the reduction of synchronization overhead and the great improvement of waiting time resulting from load imbalance.

Data migration in cloud computing using honey encryption

2018 ◽  
Vol 7 (2.8) ◽  
pp. 230
Author(s):  
K Ravindranadh ◽  
Mallarapu Sai Kiran ◽  
B Durga Sai Pavan Kumar ◽  
D Priyanka
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Privacy Protection ◽  
Security And Privacy ◽  
Data Migration ◽  
Data Confidentiality

Cloudcomputing provides variouskindsof servicesfor storingdata, load balancing betweencloudsandprovides an infrastructure for developing applicationsandmanaging them.Duetovariousattractive services provided by various cloudservice providers, users migrating their data from  their storage systemtocloudserviceprovider.Whilemigratingdatatoa cloudservice provider there will be  security  and privacy  protection concerns arises.Byconsideringthese concerns we are  proposing a secure privacy protection  migration usinghoney-encryptioncryptographicalgorithmfordata whichis outsourceddatatocloudandweareusingmigrationprotocolwhile migratingdatafromexisting serverstoragesystemtocloud server  storage systemwhichensures dataintegrityand data confidentiality.

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.

State-Full Virtual Machine Live Data Migration For Improved Load Balancing

2018 ◽  
Vol 50 (001) ◽  
pp. 107-114
Author(s):  
M. SHAIKH ◽  
A. SHAIKH ◽  
M. A. MEMON ◽  
A. A.. SHAH ◽  
R.H. SHAH
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Data Migration

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Sign in / Sign up

Export Citation Format

Share Document