Efficient Monitoring in Actor-based Mobile Hybrid Cloud Framework. Kirill Mechitov, Reza Shiftehfar, and Gul Agha

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1 Efficient Monitoring in Actor-based Mobile Hybrid Cloud Framework Kirill Mechitov, Reza Shiftehfar, and Gul Agha

2 Motivation: mobile cloud Mobile apps Huge variety Many developers/organizations Increasingly complex and resource-heavy Enabling custom cloud-based optimization requires: Skilled developer time Keeping up with rapid changes in both mobile and cloud environments Manual vs. automatic App developer implements cloud-based optimization Or let the system manage it 2

3 Motivation: hybrid cloud Public cloud Affordable, efficient, scalable Privacy & security issues Private cloud Full control over code & data More expensive/less efficient Hybrid cloud Combine both public & private clouds benefits Avoid some of the drawbacks 3

4 Mobile Cloud Computing (MCC) Cloud Computing Access to virtually unlimited, elastic computing and storage Efficient, scalable, affordable Cloud + Mobile Enable new functionality Remove mobile device limitations Improve performance Reduce energy consumption How to implement? Use code offloading Credit: cloudcomputingdoc.com 4

5 Background: code offloading Full VM emulation Run mobile device emulator in the cloud Universal solution, but expensive Application partitioning Run some components on the mobile device and some in the cloud How to partition? Let the app developer do it Let the system do it, statically Let the system do it, dynamically 5

6 IMCM: Illinois Mobile Cloud Manager Code offloading: Automatic Dynamic Fine-grained Parallel Supports: Hybrid cloud with multiple cloud spaces Provides: Policy-based control by cloud provider, app developer, user 6

7 IMCM publications ICT-BDCS 2015 Performance optimization in IMCM Best paper award CCGrid 2016 (submitted) Energy-aware optimization 7

8 IMCM framework Max app performance Min mobile energy consumption Min cloud cost Min network data usage Application Target Goal Org/App/User Policy Application Policy Access Restrictions User preferences Policy Manager Application actions Network parameters User context Application profiling Energy estimator System Properties Elasticity Manager Offloading Plan Target goal Profiled exec Profiled comm System Monitor Decision Maker Application Component Distribution 8

9 Today: efficient monitoring in IMCM Max app performance Min mobile energy consumption Min cloud cost Min network data usage Application Target Goal Org/App/User Policy Application Policy Access Restrictions User preferences Policy Manager Application actions Network parameters User context Application profiling Energy estimator System Properties Elasticity Manager Offloading Plan Target goal Profiled exec Profiled comm System Monitor Decision Maker Application Component Distribution 9

10 IMCM System Monitor Monitoring is used for: Performance estimation Energy consumption estimation Security policies Efficiency is key If overhead is too high, there is no point in using MCC Our approach Implement monitoring at the actor system level Shift overhead costs to relatively infrequent actions Focus on distributed application behavior, not local code 10

11 Actor model Programming model Natural concurrency Decentralization No shared state Scalability Location transparency -> Transparent migration Implementation: SALSA* Full actor semantics Lightweight actors Migration support Portability (Java-based) 11

12 What is an actor? Behavior (code) State Thread Actor name (address) Mailbox 12

13 What can an actor do? Send message Receive message (execute method) Create a new actor Migrate to a different location 13

14 SALSA actors module demo; behavior HelloWorld { /* The act(...) message handler is invoked automatically and is used to bootstrap salsa programs. */ void act( String[] argv ) { standardoutput<-print( "Hello" standardoutput<-print( "World!" ); } } 14

15 IMCM System Monitor What to monitor? Application actions At the level of actor primitives (create, migrate, send/receive) System actions Offloading decisions, costs System/environment conditions Performance, energy, connection speed, resource availability When to notify? Changes in monitored parameters Changes in org/user/system policies Violation of existing policies 15

16 Application actions The actor system is involved when: Actor sends a message Message arrives Message handler is executed Message handler finishes execution Actor creation initiated (local) Actor creation completed (local or remote) Migration is initiated (local) Migration request received (remote) Migration completed (remote) Can intervene and control app behavior at these points without modifying application code 16

17 System actions The actor system is involved in: Offloading decision (when, where, and which actors) System-initiated migration System-initiated actor creation 17

18 Metadata: actor attributes Describe properties of actors Creating user Creating actor Current location Etc. Two types of attributes: Static do not change at runtime E.g., creating actor Dynamic can change E.g., location 18

19 Metadata: dynamically updated structures Message send graph Call graph Actor creation graph For permission/restriction inheritance Actor name knowledge set Who can send messages to this actor Actor semantics: actor names cannot be guessed 19

20 Action control system Requirements Fine granularity Support policies defined by organization, developer, and end-user Covers all possible app and system actions Action control system based on: The requested action The specified policy Attributes of the requester Attributes of the recipient Attributes of the resource 20

21 Authorization framework model The authorization model includes the following parties: Owner organization (cloud space and/or data) Developers of the application that uses cloud resources End-users of the application Who defines authorization policy? The organization that owns data and cloud resources (organizationwide policy) The application developer can further restrict these organization-wide accesses (application-specific policy) The application user (user-specific policy) 21

22 Example: satellite ISP customer privacy policy 22

23 Policy definition & enforcement Authorization policy Hard (immutable) E.g., cloud provider policy Soft (customizable) E.g., user-defined policy Grammar to define rules Attribute based Static & dynamic binding Rule structure: <Subject, Object, Action, Sign, Type> 23

24 Implementation: action monitoring Rules from multiple applicable policies are merged Organization, developer, end-user policies Hard policy rules take precedence over soft policy rules Rules checked for every invocation of each app and system action, so must be efficient Sort rules to prioritize early DENY decision Sort rules to prioritize static-only checks Cache lookups to dramatically speed up most frequent actions Invalidate cache on dynamic attribute change Use dynamic metadata to speed up lookups 24

25 Implementation: heuristics In typical applications, most of the time is spent in the message handler Actor migration is quite infrequent Local actor creation is much more frequent than remote actor creation Local message sends are much more frequent than remote message sends 25

26 Example: face recognition app 26

27 Example: face recognition app Components with different computation, bandwidth, energy characteristics Some data inputs/outputs may be restricted to specific systems or users 27

28 Example: face recognition app Hard policy Different types of users: General public Law enforcement 28

29 Example: face recognition app Soft policy Different user expectations: Type 1: No additional privacy concerns Type 2: Privacy concerned users Type 3: Extremely cautious users Type 1 Type 2 Type 3 29

30 Example: face recognition app Policy: information security for sensitive data Protect access to database of faces located on private cloud Rule examples for actors that can access the database: Must be created only at <location> Cannot migrate away from <location> Cannot send messages outside <application> Actor creation inherits restrictions 30

31 Example: face recognition app Sample private cloud rules for restricted code and data access E.g., no send, migration from private to public cloud 31

32 Evaluation System Monitor overhead: WIP IMCM overhead (includes monitor): acceptable IMCM performance overall: awesome! Significant speedups vs. mobile-only sequential and parallel execution 32

33 Some benchmark results Raw speedup: inherent parallelism gain Offload speedup: including the cost of offloading 33

34 Face recognition app speedup 34

35 IMCM overhead 35

36 IMCM framework summary Bridge the gap between mobile application and cloud computing Separate application logic from component distribution decisions Allow dynamic distribution of components based on run-time changes Support policy-based definition of security/privacy constraints 36

37 Questions? Thank you! 37

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