Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A tutorial on support vector regression
Statistics and Computing
Support vector regression for link load prediction
Computer Networks: The International Journal of Computer and Telecommunications Networking
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Characterizing, modeling, and generating workload spikes for stateful services
Proceedings of the 1st ACM symposium on Cloud computing
Identification, Modelling and Prediction of Non-periodic Bursts in Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
CloudCmp: comparing public cloud providers
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Multi-model prediction for enhancing content locality in elastic server infrastructures
HIPC '11 Proceedings of the 2011 18th International Conference on High Performance Computing
Empirical prediction models for adaptive resource provisioning in the cloud
Future Generation Computer Systems
SO-1SR: towards a self-optimizing one-copy serializability protocol for data management in the cloud
Proceedings of the fifth international workshop on Cloud data management
Hi-index | 0.00 |
Workload bursts have become notorious for rendering numerous web information systems unavailable. While cloud computing has the potential to alleviate this problem by offering computing resources on an on-demand basis, important challenges remain in finding the right resource control strategies to scale resources cost-effectively and to overcome the initialization lag associated with resource acquisition. An effective strategy involves predicting workload demand in advance so that resources can be provisioned in a timely manner, but not all prediction approaches are made equal. We argue that while most existing approaches show promising results in predicting average workload, they fail to predict workload bursts that are inherently irregular. This paper formulates a new event-aware strategy to more effectively predict workload bursts by exploiting prior knowledge associated with scheduled events. We evaluate our approach by comparing it to state-of-the-art methods in workload prediction using real-world datasets from the online auction domain, and we show that event-aware prediction is superior to other approaches in terms of burst prediction accuracy.