Integer and combinatorial optimization
Integer and combinatorial optimization
SODA: A Service-On-Demand Architecture for Application Service Hosting Utility Platforms
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Implementation of a service platform for online games
Proceedings of 3rd ACM SIGCOMM workshop on Network and system support for games
Evaluating the Performance of Middleware Load Balancing Strategies
EDOC '04 Proceedings of the Enterprise Distributed Object Computing Conference, Eighth IEEE International
Locality aware dynamic load management for massively multiplayer games
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Dynamic microcell assignment for massively multiplayer online gaming
NetGames '05 Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games
Design and implementation of a novel dynamic load balancing library for cluster computing
Parallel Computing - Heterogeneous computing
An Active Self-Optimizing Multiplayer Gaming Architecture
Cluster Computing
Journal of Parallel and Distributed Computing
Task assignment with work-conserving migration
Parallel Computing
SLA based resource allocation policies in autonomic environments
Journal of Parallel and Distributed Computing
Optimistic load balancing in a distributed virtual environment
Proceedings of the 2006 international workshop on Network and operating systems support for digital audio and video
Service Middleware for Self-Managing Large-Scale Systems
IEEE Transactions on Network and Service Management
Hi-index | 0.00 |
Massively Online Virtual Environments (MOVEs) have been gaining popularity for several years. Today, these complex networked applications are serving thousands of clients simultaneously. However, these MOVEs are typically hosted on specialized server clusters and rely on internal knowledge of the services to optimize the load balancing. This makes running MOVEs an expensive undertaking as it cannot be outsourced to third party hosting providers. This paper details two Integer Linear Programming approaches to optimize the MOVE deployment through load balancing and minimizing the delay experienced by the end-users. Optimization includes assigning MOVE components to resources and replication of components to increase the scalability. One approach assuming full application knowledge of a dedicated MOVE and one with no internal knowledge and geared toward a generic MOVE hosting platform. For both cases an optimizing heuristic is evaluated and the obtained results are compared.