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Adaptive Offloading for Pervasive Computing

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jane
19 diapositives · 359 Views · Sep 20, 2014

This document explores adaptive offloading techniques for executing complex applications on resource-constrained mobile devices, such as PDAs. It highlights the challenges of managing resource limitations and presents solutions, including rewriting applications based on device capacity and utilizing an offloading inference engine. This engine triggers the right offloading processes at the optimal time, accounting for current resource status, to ensure efficient program execution and minimal overhead. The approach employs fuzzy control models for decision-making and generates effective application partition plans.

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Adaptive Offloading for Pervasive Computing
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  1. Adaptive Offloading for Pervasive Computing AmiNSaremi 7/6/2005

  2. Introduction • Pervasive Computing • Challenge: run complex applications on resource-constrained mobile device such as PDA. • Solutions • rewrite applications according to the resource capacity of each mobile device • application-based or system-based adaptations • Adaptive Offloading

  3. Decision Making Problems for Adaptive Offloading • The offloading inference engine should trigger offloading at the right time and offload the right program objects to achieve low offloading overhead and efficient program execution. • adaptive offloading triggering • efficient application partitioning

  4. Solution Overview • Our assumptions • the application is written in an object-oriented languages • the user’s environment contains powerful surrogates and plentiful wireless bandwidth

  5. Offloading inference engine does not require any prior knowledge about an application’s execution pattern or the runtime environment’s resource status • offloading inference engine employs the Fuzzy Control model as the basis for the offloading triggering inference module • selects an effective application partitioning from many possible partition plans • Memory constraint or CPU speed

  6. Distributed Offloading Platform • application execution monitoring • Application execution graph • Each graph node represents a Java class • memory size, AccessFreq, Location, IsNative • Each graph edge represents the interactions between the objects of two classes • InteractionFreq, BandwidthRequirement

  7. ResourceMonitoring • mobile device, the surrogate, and the wireless network • available memory in the Java heap, wireless bandwidth and delay • Candidate Partition Plan Generation

  8. Surrogate Discovery • Transparent RPC Platform

  9. Adaptive Offloading Inference Engine • Overhead of offloading • transferring objects between the mobile device and the surrogate • performing remote data accesses and function invocations over a wireless network • Offloading Triggering Inference • examines the current resource and the available resources • Decides whether offloading should be triggered • decides what level of resource utilization

  10. simple threshold-based approach • “if the current amount of free memory on the mobile device is less than 20% of its total memory, then trigger offloading and offload enough program objects to free up at least 40% of the mobile device’s memory.” • Fuzzy Control model • linguistic decision-making rules provided by system or application developers • membership functions • generic fuzzy inference engine based on fuzzy logic theory

  11. Offloading rules • if (AvailMem is low) and (AvailBW is high) then NewMemSize := low; • if (AvailMem is low) and (AvailBW is moderate) then NewMemSize := average; • if (AvailMem is high) and (AvailBW is low) then NewMemSize := high; • If any of these rules is matched by the current system conditions, the offloading inference engine triggers offloading • offloading memory size • current memory consumption - new memory utilization

  12. Mappings between numerical and linguistic values for each linguistic variable

  13. Application Partition Selection • considering the target memory utilization on the mobile device • multiple offloading requirements • minimizing wireless bandwidth overhead • minimizing average response time • minimizing total execution time • For each neighbor node Vk of Vi • B i,k to denote the total amount of data traffic transferred between Vi and Vk, • F i,k to define a total interaction number, • MS k to represent the memory size of Vk.

  14. For cost metrics Ck and Cl : Ck >=Cl if and only if • Splitting Large Classes

  15. Trace-Driven Simulation Experiments • For comparison • algorithm least recently used (LRU) • Split Class • Fuzzy Trigger • our approach

  16. References • Xiaohui Gu, Alan Messer, Ira Greenberg, Dejan Milojicic, Klara Nahrstedt, “Adaptive Offloading for Pervasive Computing”, IEEE Pervasive Computing Magazine 2004. • X. Gu, K. Nahrstedt, A. Messer, I. Greenberg, and D. Milojicic, “Adaptive Offloading Inference for Delivering Applications in Pervasive Computing Environment”, Proc. of IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), Dallas-Fort Worth, Texas, March 2003.