Data mining: concepts and techniques
Data mining: concepts and techniques
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Approximation algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Job Scheduling Under the Portable Batch System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Core Algorithms of the Maui Scheduler
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Improved Approximation Algorithms for Metric Facility Location Problems
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
Choosing Replica Placement Heuristics for Wide-Area Systems
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
GATES: A Grid-Based Middleware for Processing Distributed Data Streams
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
QoS-Aware Replica Placement for Content Distribution
IEEE Transactions on Parallel and Distributed Systems
Dynamic placement for clustered web applications
Proceedings of the 15th international conference on World Wide Web
Greedy is Good: On Service Tree Placement for In-Network Stream Processing
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Survey of Current Directions in Service Placement in Mobile Ad-hoc Networks
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Towards Efficient Service Placement and Server Selection for Large-Scale Deployments
AICT '08 Proceedings of the 2008 Fourth Advanced International Conference on Telecommunications
An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
A QoS-Aware Heuristic Algorithm for Replica Placement
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Efficient application placement in a dynamic hosting platform
Proceedings of the 18th international conference on World wide web
Distributed facility location algorithms for flexible configuration of wireless sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Scalable service migration in autonomic network environments
IEEE Journal on Selected Areas in Communications
IEEE Communications Magazine
Constrained mirror placement on the Internet
IEEE Journal on Selected Areas in Communications
Routing of multipoint connections
IEEE Journal on Selected Areas in Communications
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Many emerging online stream processing services require the consideration of quality of service (QoS), which is highly dependent on the placement of services at various hosts. This paper investigates the QoS-aware placement problems of stream processing services under different contexts. On condition that the client demands are stable, the QoS-aware placement problem aiming to minimize the cost when servers are CPU-uncapacitated, is equivalent to the set cover problem, and can be solved by a greedy algorithm with approximation factor O(log驴n), where n is the number of clients. However, when CPU capacity constraints on servers are taken into account, the QoS-aware placement problem cannot be approximated unless P=NP. Therefore, we propose two heuristic algorithms: (1) ISCA (Iterated Set Cover-based Algorithm) and (2) KBA (Knapsack-Based Algorithm). We also consider the placement problem of client demands increasing over time. Two objectives, called extension factor and system lifetime, are proposed for demand increment-blind and increment-aware models respectively. Both of them can be solved by extending ISCA and KBA. The experimental results show that ISCA and KBA have distinct effects on different demand sizes. ISCA is more efficient when client demands are relatively small, while KBA performs better for larger demands.