Abstract—Due to some inherent defects of
mobile devices, such as limited battery energy, insufficient storage space,
mobile applications are confronted with many challenges in mobility management,
quality of service (QoS) insurance, energy management and security issues,
which has stimulated the emergence of many computing paradigms, such as Mobile
Cloud Computing (MCC), Fog Computing, etc. These computation paradigms allow to
offload some tasks to the cloud for execution, which makes task scheduling
crucial both at the mobile device and in the mobile cloud. In this paper, we
models this problem as an energy consumption optimization problem, while taking
into account task dependency, data transmission and some constraint conditions
such as response time deadline and cost, and further solve it by genetic
algorithms. A series of simulation experiments are conducted to evaluate the
performance of the algorithm and the results are efficient and acceptable.
CONCLUSION
Task scheduling in MCC is known as an NP-hard problem, which
has attracted lots of attention in the past few years. We in this paper models
this problem as an energy consumption optimization problem, while taking into
account task dependency, data transmission and some constraint conditions such
as response time deadline and cost, and further solve it by genetic algorithms.
For the future work, we will test the performance of algorithms with much
larger task graphs and devise more efficient heuristic algorithms to solve this
task scheduling problem.
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REFERENCES
[1] Conti, Marco, et al. : Research challenges towards the
Future Internet. Computer Communications, 34(18), 2115–2134 (2011).
[2] Kumar, Karthik, and Y. H. Lu. :Cloud Computing for Mobile
Users: Can Offloading Computation Save Energy? Computer , 43(4), 51-56 (2010).
[3] Satyanarayanan, Mahadev, et al.: The Case for
VM-Based Cloudlets in Mobile Computing. IEEE Pervasive Computing, 8(4), 14-
23(2009).