On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models

Frequency Domain Hybrid-ARQ Chase Combining for Broadband MIMO CDMA Systems

In this paper, we consider high-speed wireless packet access using code division multiple access (CDMA) and multiple-input multiple-output (MIMO). Current wireless standards, such as high speed packet access (HSPA), have adopted multi-code transmission and hybrid-automatic repeat request (ARQ) as major technologies for delivering high data rates. The key technique in hybrid-ARQ, is that erroneous data packets are kept in the receiver to detect/decode retransmitted ones. This strategy is refereed to as packet combining. In CDMA MIMO-based wireless packet access, multi-code transmission suffers from severe performance degradation due to the loss of code orthogonality caused by both interchip interference (ICI) and co-antenna interference (CAI). This limitation results in large transmission delays when an ARQ mechanism is used in the link layer. In this paper, we investigate efficient minimum mean square error (MMSE) frequency domain equalization (FDE)-based iterative (turbo) packet combining for cyclic prefix (CP)-CDMA MIMO with Chase-type ARQ. We introduce two turbo packet combining schemes: i) In the first scheme, namely "chip-level turbo packet combining", MMSE FDE and packet combining are jointly performed at the chip-level. ii) In the second scheme, namely "symbol-level turbo packet combining", chip-level MMSE FDE and despreading are separately carried out for each transmission, then packet combining is performed at the level of the soft demapper. The computational complexity and memory requirements of both techniques are quite insensitive to the ARQ delay, i.e., maximum number of ARQ rounds. The throughput is evaluated for some representative antenna configurations and load factors to show the gains offered by the proposed techniques.Comment: Submitted to IEEE Transactions on Vehicular Technology (Apr 2009