219 research outputs found
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
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