rmcc: a restful mobile cloud computing framework for exploiting adjacent service-based mobile...
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RMCC: A RESTful Mobile Cloud Computing Framework for Exploiting Adjacent Service-based Mobile Cloudlets
Saeid Abolfazli (PhD)
Center for Mobile Cloud Computing ResearchUniversity of Malaya
Dec 2014
Presented in IEEE CloudCom’14 Conference, Singapore15-19 December 2014
Motivation
• Trend: Mobile Everywhere
• However: Intrinsic Resource Poverty
=
Constraint CPUShort Battery Life Small Storage
State-of-the-art: Mobile Cloud Computing
• Leverage cloud-based resources
• Augment mobile devices
• Perform resource-intensive task remotely
• Major issues with tradition augmentation frameworks:
1. WAN latency
2. Partitioning overhead
3. Portability
RMCC main idea and use cases
• Use ASMobiC: Adjacent (one-hop) service-based mobile cloudlets as computing server
Resource sharing Incentive:- Financial benefits (at least electricity bill)- Reputation- Reputation-based mutual benefits
Feasible Use cases- Distributed analysis of sensitive/confidential/enterprise data- Online real-time OCR in hospital- E-learning in group- On-campus scientific computing- On-road navigation- Real-time computing for smart city
RMCC Design Considerations & Significance
• Service-oriented architecture (loose coupling)
• Separation of responsibilities (simple and convenient)
• No code offloading (less data transfer)
• REST web services (less overhead, stateless)
• Arbitrated by MNO (mobile network operators)
• Centralized/decentralized mode (flexible security)
• Asynchronous
• Internet-free
• Green Computing
RMCC Architecture
• Main components:
Mobile Service Consumer
Mobile Service Provider
Trusted Service Governor
Evaluation
Methods:
1- Mathematical Modeling (Statistical Modeling)
2- Benchmarking
Evaluation Metrics and tools:
1- Application Execution Time (ms) - > Auto-logging
2- Mobile Consumed Energy (mJ) -> Power Tutor 1.4
Entity Specification
Mobile Service Consumer HTC Nexus One, Android-based
Wireless Access Point Cisco Linksys WRT 54G
Mobile Service Provider 1 Samsung Galaxy S2
Mobile Service Provider 2 Dell Laptop XPS 14x
Mobile Service Provider 3 Acer Laptop
Centralized Server Dell OptiPlex 990
Database SQL Server
Number of Workload 30
Statistical modelling
Via Linear Regression Model
• Generate: Independent Replication Method
• Train regression model using measured dataset to derive regression equation.
• Derive model of time and energy via algorithm complexity (Big-O) and regression equation.
• Validate using split-sample approach
• Generate time and energy data.
• Synthesize the results
Results
Execution Time(ms)
Energy Consumption (mJ)
Results Continues
Execution Time
Energy consumption
Results: Comparative View
Method Time Saving Energy Saving
Statistic 85.14% 72.20%
Benchmarking 87% 71.45%
• Leveraging ASMobiCs is significantly beneficial
• 86% time and 72% energy savings
• Resource allocation
• MSC and MSP Mobility
• Adaptive communication
• Incentive
• Security & Privacy
• Monitoring & Billing
• Fault tolerance
Conclusions and Future Works
