Mobile Cloud Computing: A Comparison of
Application Models
Group #6Chandra Shekhar Jammi(95167373)Venkata Sri Krishnakanth Pulla(95911880)Prashant Tiwari (22721608)
Introduction• Cloud computing offers benefits like increased
storage, increased processing capacity, flexibility and mobility, reduced cost etc.
• In Mobile arena, numerous constraints hinder actual realization of above mentioned benfits.
• This gives rise to many research opportunities.• We discuss some progress in this domain so far.
What is Mobile Cloud Computing
• A combination of cloud computing, wireless infrastructure, portable devices, location based services have given rise to it.
• Mobile cloud computing is a model for transparent elastic augmentation of mobile device’s capability.
• Using ubiquitous wireless access to cloud storage and computing resources.
• context-aware dynamic adjusting of offloading in respect to change in operating conditions,
• Preserve available sensing and interactivity capabilities of mobile devices
Challenges and Issues• Existing cloud computing tools consider process
parallelizing on massive data volume, large data storage and flexible VM management.
• Mobile Cloud Computing requires computation and storage offloaded to the crowd respecting user interactivity in every possible way.
• Addressing mobile constraints is the only way.
Challenges in this domain:• How to abstract complex underlying
heterogeneous technology.
• How to model parameters like low energy capacity and intermittent dis-connectivity that influence performance and interactivity of the application.
• Integrate processing and storage on cloud considering privacy and security. • Finally,
Constraints for Mobile Devices: Low Processing Capabilities,
Less available energy, Low Memory, Data rates and plans, Intermittent Dis-connectivity, Smaller Display etc.
Types of Mobile Application • Offline Applications: Fat Clients with presentation
and business logic processed locally.
• Data downloaded from backend.
• Advantages: Well Integrated, Optimized Performance, Availability :even without network connectivity.
• Disadvantages: No Portability, Complex Code.
• Online Applications: Only presentation layer at the client. All processing done online.
• Assume constant connectivity with backend.
• Platform issues; Web Technology to rescue.
• Advantages: Multiplatform, Direct and Instantaneous Accessibility to better services.
• Disadvantages: Excessive latency for real time responsiveness, no access to device features, sometimes difficult to maintain sessions for a long time.
** Urgent Need to Address Dynamic Environment of Mobiles!
Novel Application Models for Cloud Computing
• Augmented execution: Tackle limitation of computation , memory and battery.
• Systems present ;based on Cloudlets; employ Dynamic VM Synthesis.
• Cloudlets->Trusted, Resource Rich cluster of computers, well connected to Internet and available to nearby mobiles.
• Offers low latency, one-hop high bandwidth wireless access.
Fig. 1. CloneCloud categories for augmented execution.
Dynamic Virtual Machine Synthesis
Fig 2: Dynamic VM synthesis.
Elastic Partitioned/ Modularized Application
• Due to heterogeneous changing environment, dynamic partitioning and remote execution of applications preferred.
• Calling the Cloud: Middleware to dynamically distribute layers of application between device and server, optimizing latency, data transfer and cost.
• Uses AlfredO framework, which is based on R-OSGI that allows decomposition of Java Application in software modules.
• MAUI: Code offload to improve battery life. Profiling Information of methods used to predict future invocations.
Fig 3: Reference Architecture for Elastic Applications
Weblets-> platform independent and can be executed transparently on different computing infrastructures including mobile devices or IaaS (Infrastructure as a Service) cloud providers such as Amazon EC2 and S3 .
Application Mobility(Move Application Within Hosts During
Execution)
• Internet Suspend/Resume:– VM encapsulates
distinct execution& user customization state.DFS transfers state.
– Migrate complete VM; More BW and Time
• JADE: – Adaptive Application
Mobility Solution.
– Based on Java
– Interoperability amongst Mobiles based on different H/W.
Ad-hoc Mobile Cloud
Helpful when:•No or weak Internet Connection or Access to Big Cloud Providers.•Helps create computing communities•Reduces Data Transfer towards/from network.
Eg: The Hyrax Project which uses Hadoop Framework on Mobile Devices for sharing data and computation.
Fugure 4: A Mobile Cloud
Topics for Exploration• Programming Abstraction: Hide Complexity of
Cloud, or have middleware in place.
• Cost Model: Consider parameters like module execution time, resource consumption, battery level, monetary cost, security, bandwidth etc.
• Adaptation: Two extreme approaches; Laissez-Fair Adaptation and Application Transparent Adaptation.
Topics for Exploration Contd…• Trust, Security and Privacy: Low control over data,
potential data loss.
– Unlimited Computational Resources to non- trusted elements.
• Cloud Integration: Data Persistence vs Data Availability.
– Shifting modules to cloud &coordination not easy.
Clone-Cloud
Motivation• With the increasing use of mobile devices, mobile
applications with richer functionalities are becoming ubiquitous
• But mobile devices are limited by their resources for computing and power consumption
• Cloud – the place for abundant resources• Clouds provide opportunity to do huge
computations quickly and accurately
So why not use cloud for mobile computations??
CloneCloud
• CloneCloud envisions at an architecture that uses cloud to do computations that consume resources badly on mobiles.
• It uses the “connectivity” abilities of the mobile devices as a substitute for computational abilities.
• It believes in the intuition that “as long as execution on the cloud is significantly faster than execution on the mobile device, paying the cost for sending the relevant data and code from the device to the cloud and back may be worth it.”
• It aims at finding the right spots in an application automatically where the execution can be partitioned and migrated to the cloud.
CloneCloud• This model is primarily applicable to application
layer VMs (virtual machines) since the instruction sets in it are bytecodes and hence provide hardware and OS independence
• It exhibits opportunistic but conservative consistency
• The partitioning component for finding migration points uses – static analysis to find the constraints and the dynamic profiling for building the cost model for execution and migration
• It finally uses an optimizer that uses the above constraints and cost models to derive the partitions
Partitioning• The partitioning mechanism yields the
partitions in the application that are optimal at execution time and energy consumption
• It is run multiple time under different conditions and objective functions – stores all partitions in a database
• At run time, the execution picks a partition among these and modifies the executable before invocation
• It has three components – static analyzer, dynamic profiler and optimization solver
Partitioning
Fig 5: Partitioning Analysis Framework
Static Analyzer• The static analyzer identifies the legal partitions of
the application executable according to a set of constraints
• Migration is restricted to the method entry and exit points
• Two more restrictions for simplicity– Migration is allowed only at the boundaries of
application methods but not core system library methods
– Migration is allowed at the VM-layer method boundaries but not native method boundaries
• Example…
Static Analyzer - example
Fig 6: Flow Diagram in a Static Analyzer
Static Analyzer - constraints
• Three properties of any legal partition– Methods that access specific features of a
machine must be pinned to the machine
– Methods that share native state must be collocated at the same machine.
– Prevent nested migration.
Dynamic Profiler• Profiler collects data that will be used to
construct the cost model• Currently using randomly chosen set of inputs– Future work is to explore symbolic-execution-based
techniques since randomly chosen inputs may not explore all execution paths
• Each execution is run once on mobile device and once on the clone in the cloud
• Profiler outputs set of executions S and a “profile tree”, for both mobile device and the clone
• Example…
Dynamic Profiler- example
Fig 7: Example of Dynamic Profiler
Profile tree
• One node for each method invocation• Every non-leaf node also has a leaf child called its
residual node• Residual node holds residual cost which represents the
cost of running the body of code excluding the costs of the methods called by it
• Computation cost Cc(i, l); l=0 on mobile device and filled from T, l=1 on the clone and filled from T’
• Migration cost Cs(i); sum of a suspend/resume cost and the transfer cost
Dynamic Profiler• For energy consumption model, we do the
energy measurements with off-board equipment.
• CPU activity (processing/idle), display state (on/off), and network state (transmitting or receiving/idle), and translate them to a power value using func P
• Cc(i, 0)= P(CPUOn, ScrOn, NetIdle)* T[i]• Cc(i,1)= P(CPUIdle, ScrOn, NetIdle)• Cs(i) = <Cs(i) value from time model> *
P(CPUOn, ScrOn, NetOn)
Optimization Solver• It aims at picking up application methods to migrate
to the clone from the mobile device, so as to minimize the expected cost of the partitioned application.
• Decision variable R(m) m= method in the application.
• R(m)=1 -> partitioner places a migration point at the entry point of the method.
• But not all partitioning choices for R(.) are legal
Optimization problem
• Using the decision variables R(.), the auxiliary decision variables L(.), the method sets VM and VNatC for all classes C defined during static analysis, and the relations I, DC and TC
Cost of a partition
• The cost of a legal partition R(.) of execution E is given by
• Optimization objective is to choose R() so as to minimize C(E)
Distributed ExecutionTwo unique features of CloneCloud
Thread granularity migration:migration operates at the granularity of a
thread
Native-Everywhere: enables migrated threads to use native non-
virtualized hardware(GPUs, Cryptographic accelerators etc.)
Migration Overview
Suspend and capture Thread migrator suspends migrant thread
Captures its state, passes it to node manager
Node manager transfers the capture to clone
Resume and Merge Clone's thread migrator captures and
packages the thread state
Node manager transfers the capture back to the mobile device
Migrator in the original process is given the capture for resumption
Object Mapping
Evaluation
Evaluation
Related Work MAUI: Making Smartphones Last Longer with
Code Offload
-does not support virtualized methods calling native methods
-requires programmers to annotate methods as REMOTABLE
-requires a solver to be running at the server at runtime (as opposed asynchronous solving in CloneCloud)
Future Work CloneCloud focuses on migrating at
execution points where no native state need to be migrated
CloneCloud does not virtualize access to native resources that are not virtualized already and are not available on clone
No concurrency
Trust worthiness of clone
Conclusion A design that achieves basic augmented
execution of mobile applications on the cloud
Prototype delivers up to 20x speed up and 20x energy reduction
Programmer involvement is not required Opens up a path for a rich research agenda in
hybrid mobile-cloud systems
Thanks