The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.

1.

Ahani, Ghafour

et al.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Yuan, Di

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Task offloading is a key component in mobile edge computing. Offloading a task to a remote server takes communication and networking resources. An alternative is device-to-device (D2D) offloading, where a task of a device is offloaded to some device having computational resource available. The latter requires that the devices are within the range of each other, first for task collection, and later for result gathering. Hence, in mobility scenarios, the performance of D2D offloading will suffer if the contact rates between the devices are low. We enhance the setup to base station (BS) assisted D2D offloading, namely, a BS can act as a relay for task distribution or result collection. However, this would imply additional consumption of wireless resources. The associated cost and the improvement in completion time of task offloading compose a fundamental trade-off. For the resulting optimization problem, we mathematically prove the complexity, and propose an algorithm using Lagrangian duality. The simulation results demonstrate not only that the algorithm has close-to-optimal performance, but also provide structural insights of the optimal trade-off.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Yuan, Di

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Caching popular contents at edge devices is an effective solution to alleviate the burden of the backhaul networks. Earlier investigations commonly neglected the storage cost in caching. More recently, retention-aware caching, where both the downloading cost and storage cost are accounted for, is attracting attention. Motivated by this, we address proactive and retention-aware caching problem with the presence of user mobility, optimizing the sum of the two types of costs. More precisely, a cost-optimal caching problem for vehicle-to-vehicle networks is formulated with joint consideration of the impact of the number of vehicles, cache size, storage cost, and content request probability. This is a combinatorial optimization problem. However, we derive a stream of analytical results and they together lead to an algorithm that guarantees global optimum with polynomial-time complexity. Numerical results show significant improvements in comparison to popular caching and random caching.

Southwest Jiaotong Univ, Inst Mobile Commun, Chengdu, Sichuan, Peoples R China..

Ahani, Ghafour

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. Komar Univ Sci & Technol, Dept Comp Engn, Sulaymaniyah, Iraq..

Fan, Pingzhi

Southwest Jiaotong Univ, Inst Mobile Commun, Chengdu, Sichuan, Peoples R China..

Yuan, Di

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Caching popular files at user equipments (UEs) provides an effective way to alleviate the burden of the back-haul networks. Generally, popularity based caching is not a system-wide optimal strategy, especially for mobility scenarios. Motivated by this observation, an optimal caching problem with respect to user mobility is investigated. To be specific, a cost-optimal caching problem (COCP) for device-to-device (D2D) networks is formulated, in which the impact of user mobility, cache size, and total number of encoded file segments are considered. Compared with the related studies, our investigation guarantees that the collected segments are non-overlapping, takes into account the cost of downloading from the network, and provides a rigorous complexity analysis. For problem solving, we first prove that the optimal caching placement of one user, giving other users' caching placements, can be derived in polynomial time. Then, based on this proof, a fast yet effective caching placement algorithm for all users is developed. Simulation results verify the effectiveness of this algorithm by comparing it to conventional caching algorithms.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.

Fan, Pingzhi

Yuan, Di

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.