Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
R × W: a scheduling approach for large-scale on-demand data broadcast
IEEE/ACM Transactions on Networking (TON)
Synchronizing a database to improve freshness
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Managing periodically updated data in relational databases: a stochastic modeling approach
Journal of the ACM (JACM)
Optimal crawling strategies for web search engines
Proceedings of the 11th international conference on World Wide Web
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Time-Critical On-Demand Data Broadcast: Algorithms, Analysis, and Performance Evaluation
IEEE Transactions on Parallel and Distributed Systems
Multicriteria Optimization
Efficient Monitoring Algorithm for Fast News Alerts
IEEE Transactions on Knowledge and Data Engineering
Maintaining coherency of dynamic data in cooperating repositories
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
WIC: a general-purpose algorithm for monitoring web information sources
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Monitoring an Information Source Under a Politeness Constraint
INFORMS Journal on Computing
A Dual Framework and Algorithms for Targeted Online Data Delivery
IEEE Transactions on Knowledge and Data Engineering
New models and algorithms for throughput maximization in broadcast scheduling
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Processing flows of information: From data stream to complex event processing
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
Architectures and middleware for event delivery face scalability challenges in providing up-to-date events to meet clients' specifications. The use of proxy middleware is a common practice for increasing scalability. Proxies can aggregate client specifications, taking advantage of similar needs to reduce communication overhead, workload on event sources (e.g., sensors) and network traffic. However, in a setting with thousands of clients, event sources, and constrained resources, proxies cannot always satisfy all client needs. A proxy is interested in maximizing completeness by capturing as many events as possible. However, due to constraints on resources, event delivery may not be fully current, resulting in a delay in delivering events. In many cases, these two dimensions cannot be directly related and we propose a flexible design framework that can suit different changing needs of designers. We start by casting the event delivery trade-off as a bi-objective optimization problem. The conventional definition of a solution to multi-objective problems is a Pareto set and we analyze the solution complexity. The offline optimal solution serves us in evaluating the design of run-time heuristic solutions, allowing a flexible framework that suits both designs that emphasize completeness and those that emphasize latency. To demonstrate the proposed design framework, we offer four greedy local policies and analyze their performance using both real-world traces and synthetic data to demonstrate the use of the proposed design framework.