Name
Abstract | ||
|---|---|---|
Computation offloading frameworks partition an application's execution between a cloud server and the mobile device to minimize its completion time on the mobile device. An important component of an offloading framework is the partitioning algorithm that decides which tasks to execute on mobile device or cloud server. The partitioning algorithm schedules tasks of a mobile application for execution either on mobile device or cloud server to minimize the application finish time. Most offloading frameworks partition parallel applications devices using an optimization solver which takes a lot of time. We show that by allowing duplicate execution of selected tasks on both the mobile device and the remote cloud server, a polynomial algorithm exists to determine a schedule that minimizes the completion time. We use simulation on both random data and traces to show the savings in both finish time and scheduling time over existing approaches. Our trace-driven simulation on benchmark applications shows that our algorithm reduces the scheduling time by 8 times compared to a standard optimization solver while guaranteeing minimum makespan. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 14TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) | Mobile Cloud, Application Offloading, Code Partitioning, Mobile System, Optimization, Task Scheduling |
Field | DocType | ISSN |
Job shop scheduling,Computer science,Scheduling (computing),Server,Computation offloading,Real-time computing,Schedule,Mobile device,Solver,Mobile telephony,Distributed computing | Conference | 2331-9852 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arani Bhattacharya | 1 | 21 | 7.10 |
| Ansuman Banerjee | 2 | 170 | 41.69 |
| Pradipta De | 3 | 30 | 9.02 |