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Adaptive Offloading Inference for Delivering Application in Pervasive Computing Environments

Adaptive Offloading Inference for Delivering Application in Pervasive Computing Environments. Presented by Jinhui Qin. Outline :. Introduction ( 3 slides) Proposed approach ( 1 slide) System overview ( 3 slides) Design and algorithms ( 5 slides) Performance evaluation ( 5 slides)

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Adaptive Offloading Inference for Delivering Application in Pervasive Computing Environments

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  1. Adaptive Offloading Inference for Delivering Application in Pervasive Computing Environments Presented by Jinhui Qin

  2. Outline : • Introduction ( 3 slides) • Proposed approach ( 1 slide) • System overview ( 3 slides) • Design and algorithms ( 5 slides) • Performance evaluation ( 5 slides) • Conclusion (1 slide)

  3. Motivation • It is challenging to deliver complex applications on mobile devices • resource-constrained • Limitations for existing approach • Degrading an application’s fidelity • Adaptation efficiency is limited by coarse-grained approaches • Expensive to rewrite an application Introduction (Slide 1 of 3)

  4. Main idea of AIDE • AIDE • Adaptive Infrastructure for Distributed Execution • A fine-grained runtime offloading system • Main idea • Dynamically partitioning the application during runtime. • Offloading part of the application execution to a powerful nearby surrogate device. Introduction (Slide 2 of 3)

  5. Key problems • Which objects should be offloaded? • When to trigger the offloading action? • OLIE solves above problems Introduction (Slide 3 of 3)

  6. OLIE , the proposed approach • Makes intelligent offloading decisions • Timely triggering of adaptive offloading • Intelligent selection of an application partitioning policy • Using Fuzzy Control model • Only focus on relieving the memory constraint • Enables AIDE to deliver resource-intensive applications with minimum overhead Proposed approach (Slide 1 of 1)

  7. System overview Triggering offloading action and making offloading decisions. Transforming method invocations to offloaded objects into remote invocations. System overview (Slide 1 of 3)

  8. Program (Java) execution information Class: A; Memory: 5KB; AccessFreq: 10; Location: surrogate; isNative: false; InteractionFreq: 12 BandwidthRequirement: 1KB System overview (Slide 2 of 3)

  9. OLIE making offloading decisions • Monitoring • Tracking the amount of free space in the Java heap, obtained from the JVM garbage collector • Bandwidth and delay are estimated by periodically invoking the ping system utility. • Making offloading decisions • The new target memory utilization • Classes to be offloaded • Classes to be pulled back System overview (Slide 3 of 3)

  10. OLIE design and algorithms • Goal • To relieve the memory constraint with minimum overhead • Migration cost • Remote data access delay • Remote function invocation delay Design and algorithms (Slide 1 of 5)

  11. Triggering of adaptive offloading • A generic fuzzy inference engine based on fuzzy logic theory • Based on the Fuzzy Control model • Typical Decision-making rule specifications, e.g. If(AvailMem is low) and (AvailBW is high) Then NewMemSize := low; If(AvailMem is low) and (AvailW is moderate) Then NewMemSize := average; • Membership functions define the mappings between the numerical value and the linguistic values Design and algorithms (Slide 2 of 5)

  12. Value mappings AvaiMembelongs to the set of linguistic value low is 100%. AvaiMembelongs to the linguistic value low is linear decreasing function from 100% to 0%. AvaiMembelongs to the linguistic value either low or moderate, but with different confidence probabilities. Design and algorithms (Slide 3 of 5)

  13. Intelligent partitioning selection • All nodes with isNative=true are merged into one node N to form the first partition set • The coalescing process • Examines each of the neighbors of N and selects ones and merged into the first partition set based on several policies, • OLIE_MB (BandwithRequirment) • Minimize the wireless network transmission load • OLIE_ML (interactionFreq) • Minimize the interaction delay • OLIE_Combined (BandwithRequirment, interactionFreq, memory) • Keep most active classes • Have largest interactionFreq and BandwithRequirment • Offload most inactive classes • Have smallest interactionFreq and largest amount of memory Design and algorithms (Slide 4 of 5)

  14. Decision-making algorithm Mi: Memory size for Java class i; EG = {N0, N1, …., Nn}: execution graph; CMT: the maximum memory size for a class node; NewMemoryUtilization := -1; while (offloading service is on) while (no significant changes happen) perform executions and update EG accordingly; while (Mi > CMT) create a new node to represent class i; //make the adaptive offloading triggering decision SetLingVar(); //set numerical values for all input linguistic variables fuzzify(); // map the numerical values to the linguistic values FuzzyInferencEngine(); //checking rules and updating NewMemoryUtilization defuzzify(); // map the linguistic values to the numerical values if (NewMemoryUtilization == -1) then offloading is not triggered; else //make the partitioning decision merge all non-offloadable classes into a node N; while (size(EG) > 1) merge (N, one of its neighbors NBj); if ( current cut is better) bestPos = NBj; Partitionmobiledevice = {N0, …, NbestPos}; Partitionsurrogate = { NbestPost+1, …, Nn}; Design and algorithms (Slide 5 of 5)

  15. Performance evaluation • Using extensive trace-driven simulations • App. execution traces • Executed on a Linux desktop machine • By querying an Instrumented JVM • Trace file records • Method invocations • Data field accesses • Object creations and deletions • Wireless network traces • The Ping system utility on an IBM Thinkpad • IEEE 802.11 WaveLAN network card Performance evaluation (Slide 1 of 5)

  16. Simulator • Only consider average RTT for small packets (about 2.4ms on average) • Remote function invocation overhead, RTT/2 • Remote data access overhead, RTT • Migration overhead, Memoryclasses to be migrated current available bandwidth Performance evaluation (Slide 2 of 5)

  17. Compared approaches • Random and LRU • Both use one simple fixed policy • availMemory < 5% totalMemory && newMemoryUtilization < 80% totalMemory • Random algorithm keeps randomly selected classes. • LRU algorithm offloads least recently used classes according to the AccessFreq of each class Performance evaluation (Slide 3 ofl 5)

  18. Experimental applications Performance evaluation (Slide 4 of 5)

  19. Offloading Overhead (second) Time (second) Migration Remote Data Remote Access Function Call DIA Biomer JavaNote Results Performance evaluation (Slide 5 of 5)

  20. Conclusion OLIE relieves memory constraints for mobile devices with much lower overhead than other common approaches Major contributions Identifying two key decision-making problems Applying the Fuzzy Control model to OLIE Proposing three policies for selecting application partitions Conclusion Conclusion (Slide 1 of 1)

  21. Question?

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