Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
The Gradient Model Load Balancing Method
IEEE Transactions on Software Engineering - Special issue on distributed systems
Caching Hints in Distributed Systems
IEEE Transactions on Software Engineering - Special issue on distributed systems
A Trace-Driven Simulation Study of Dynamic Load Balancing
IEEE Transactions on Software Engineering
Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Finding Idle Machines in a Workstation-Based Distributed System
IEEE Transactions on Software Engineering
NEST: a network simulation and prototyping testbed
Communications of the ACM - Special issue on simulation
Guest Editor's Introduction: Distributed Computing Systems
Computer - Distributed computing systems: separate resources acting as one
Data and computer communications: networking and internetworking
Data and computer communications: networking and internetworking
Scalable, Adaptive Load Sharing for Distributed Systems
IEEE Parallel & Distributed Technology: Systems & Technology
Methodical Analysis of Adaptive Load Sharing Algorithms
IEEE Transactions on Parallel and Distributed Systems
Scalable Resource Scheduling: Design, Assessment, Prototyping
ICCSSE '97 Proceedings of the 8th Israeli Conference on Computer-Based Systems and Software Engineering
Analysis of coordinated load sharing for large distributed systems
International Journal of Computers and Applications
Performance analysis of semi-centralized load sharing
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
| Hi-index | 0.00 |
This paper presents a quantitative analysis of an efficient load sharing scheme namely "Dual Layered Load Sharing Scheme (DLLS)" which is highly suitable for large scale distributed systems. The scheme is based on partitioning the system into "independent" clusters called partitions. Load sharing is accomplished at two levels i.e., one within the partition and the other across the partitions. Load sharing within the partition is performed by a variant of a flexible load-sharing algorithm, EFLS, while a utilization rate of partitions is used for the load sharing across the partitions. The primary benefits of decomposing the systems into partitions are better allocations of the tasks to the neighbouring nodes and scalability. The quantitative analysis provides both performance and efficiency measures. NEST simulation tool was used for the implementation and analysis of our DLLS scheme. A detailed comparative study shows a considerable improvement of DLLS over the EFLS under congested communication resources. In particular, it was observed that DLLS achieved a high percentage of close allocation of about 95%, a hit ratio of about 97% and system response time on the average75%.