Presented in IEEE WoWMoM'12, San Francisco, USA.

1. Exploiting an Elastic 2-Tiered Cloud Architecture for Rich Mobile Applications
M. Reza Rahimi, Nalini Venkatasubramanian
Motivation and Problem Statement Space-Time Workflow
• Smart Phones have limited resources such as • To model mobile applications on 2-Tier Cloud
battery, memory and computation. Tier 1: Public Cloud architecture, workflow concept from SOA will be used.
• One of the main bottlenecks in ensuring mobile (+) Scalable and Elastic • It consists of number of Logical and Precise steps known
(-) Price, Delay
QoS is the level of wireless connectivity offered by as a Function.
last hop access networks such as 3G and Wi-Fi. • Functions could be composed together in different
• 3G exhibits: patterns. k
 Wide area ubiquitous connectivity. Tier 2: Local Cloud
 But long delay and slow data transfer. (+) Low Delay, Low
F1 F2 F3 F1
RTT:
• Wi-Fi exhibits: Power,
Almost Free 3G Access ~290ms SEQ LOOP
 low latencies/delays (-) Not Scalable and
Point
Elastic
 scalability issues; as the number of users Wi-Fi Access
1 F3 P1 F3
increases the latency and packet losses Point
F1 F4 F1 F4
increase causing a decrease in application F2 F2
RTT: 1 P2
performance. ~80ms
• 2-Tier Cloud Architecture could increase the QoS AND: CONCURRENT FUNCTIONS XOR: CONDITIONAL FUNCTIONS
of Mobile Applications.
• We extend this concept to Space-Time Workflow to
model mobile applications.
• In this optimization problem our goal is to maximize the
minimum saving of power, price and delay of the
mobile applications.
Cloud Resource Allocation for Mobile Applications (CRAM)
• Optimal resource allocation for mobile
applications is NP-Hard, so heuristic is System Evaluation and Future Directions
needed.
• CRAM uses the combination of two main • 2 different classes of mobile applications Mobile Applications Ecosystem
best practices in heuristic: will be considered:
 Simulated Annealing (Catching Global
Optima)  Intensive Computing and Storage, Single User Multiple Users Single User Multiple Users
 Greedy Approach(Catching Local  Streaming Application,
Simulated
Optima) Annealing
• Application profiling and simulation will be Non-Streaming
• It uses the following observation for used to evaluate CRAM. Applications
pervasive environment that: near user • When the number of users is high CRAM
resources usually have better QoS. could achieve 85% of optimal solution.
• Need Efficient way to retrieve information • In future CRAM will be extended to use
of services on cloud in specific region. efficiently user trajectory.
Example Query: “Retrieve all MPEG to Single User
AVI decoder services in distance R of
mobile user “
• R-Tree is an efficient way to answer these Streaming
queries. Applications Multiple Users
R2 R1 R
S2 S8
S1
R1 R2
S6
S4
R3
R3 R4 R5 R6
R5 R4
S3
R6 S1 S1 S2 S5 Low Computation High Computation
S5
S8 S3 S4 S7
or Storage or Storage
S7 S9
S11 S11 S6 S9
S10 S10
School of Information & Computer Sciences, University of California, Irvine.