TY - JOUR
T1 - Optimal joint scheduling and cloud offloading for mobile applications
AU - Mahmoodi, S. Eman
AU - Uma, R. N.
AU - Subbalakshmi, K. P.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.
AB - Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.
KW - Joint scheduling-offloading
KW - computation offloading
KW - mobile cloud computing
KW - scheduling
UR - http://www.scopus.com/inward/record.url?scp=85067123591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067123591&partnerID=8YFLogxK
U2 - 10.1109/TCC.2016.2560808
DO - 10.1109/TCC.2016.2560808
M3 - Article
AN - SCOPUS:85067123591
VL - 7
SP - 301
EP - 313
JO - IEEE Transactions on Cloud Computing
JF - IEEE Transactions on Cloud Computing
IS - 2
M1 - 7463066
ER -