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| Content Provider | ACM Digital Library |
|---|---|
| Author | Varshavsky, Alexander Caceres, Ramon Seshan, Srinivasan Javed, Umar Han, Dongsu Pang, Jeffrey |
| Abstract | Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is handoff. Although handoffs are necessary for mobile devices to maintain connectivity, they can also cause short-term disruptions in application performance. Thus, applications could benefit from the ability to predict impending handoffs with reasonable accuracy, and modify their behavior to counter the performance degradation that accompanies handoffs. In this paper, we study whether attributes relating to the cellular network conditions measured at handsets can accurately predict handoffs. In particular, we develop a machine learning framework to predict handoffs in the near future. An evaluation on handoff traces from a large US cellular carrier shows that our approach can achieve 80% accuracy - 27% better than a naive predictor. |
| Starting Page | 65 |
| Ending Page | 70 |
| Page Count | 6 |
| File Format | |
| ISSN | 01635980 |
| DOI | 10.1145/2094091.2094109 |
| Journal | ACM SIGOPS Operating Systems Review (OPSR) |
| Volume Number | 45 |
| Issue Number | 3 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1975-04-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Measurement Wireless Handoff 3g Prediction Mobility Umts |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Information Systems |
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