IRIS: an object-oriented database management system
Readings in object-oriented database systems
Persistent Caching: An Implementation Technique for Complex Objects with Object Identity
IEEE Transactions on Software Engineering
Object-oriented analysis and design with applications (2nd ed.)
Object-oriented analysis and design with applications (2nd ed.)
Genetic algorithms for product design
Management Science
Visual modeling with Rational Rose 2000 and UML (2nd ed.)
Visual modeling with Rational Rose 2000 and UML (2nd ed.)
Replacement policies for a proxy cache
IEEE/ACM Transactions on Networking (TON)
Characterizing reference locality in the WWW
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Accelerating Dynamic Web Content Generation
IEEE Internet Computing
Using Genetic Algorithms to Schedule Flow Shop Releases
Proceedings of the 3rd International Conference on Genetic Algorithms
Characteristics of WWW Client-based Traces
Characteristics of WWW Client-based Traces
A survey of Web cache replacement strategies
ACM Computing Surveys (CSUR)
WebLogic: The Definitive Guide
WebLogic: The Definitive Guide
Cost-aware WWW proxy caching algorithms
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
Cooperative Cashing? An Economic Analysis of Document Duplication in Cooperative Web Caching
Information Systems Research
SOA Performance Enhancement Through XML Fragment Caching
Information Systems Research
| Hi-index | 0.00 |
Modern applications are built using object-oriented (OO) principles. Unfortunately, OO technologies, though they add tremendous functionality and flexibility to applications, require significant computational power, which greatly affects the performance and scalability of the applications. Caching objects in memory is a popular way to solve this problem, but the available memory in systems is limited and not sufficient to store each instance of every enterprise application object. Deciding which instances of which objects should be cached is a two-stage process. The first stage is selecting the objects, and the second stage is selecting the object instances. The second stage, typically referred to as a replacement policy, occurs at run-time and has been widely studied. The focus of this paper is the first stage: selecting objects whose instances are candidates for caching at run-time. In current systems, this critical step is performed in an ad hoc manner. We build a nonlinear mathematical model for this problem, develop a feasible solution procedure based on genetic algorithms, and demonstrate its effectiveness by comparing the solution quality to lower bounds obtained using a Lagrangian-relaxation-based method.