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In Java computing context, ADAJ (Adaptive Distributed Application in Java) provides a both execution and programming system for irregular distributed applications. From the execution point of view, load balancing is an important issue and requires special information policies. Traditionnally only the computer load is taken into account.Consequently, the load balancing is made by being unaware of the relations between objects. The objects are moved of a computer on the other without knowing the consequences in term of communication. To avoid that problem we introduce in that paper two mechanims: a relation observation mechanism of distributed objects and a computer load observation. The dynamical observation information provided by these mechanisms will be able to allow a general load balancing mechanism to have an intelligent adaptative object redistribution strategy. The proposed observation tools are entirely designed in Java. Some experiment results which concerns relation observations and overhead measurements are presented.

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This paper presents a detailed study of the mechanism to design a compiler of Smali language to generate optimized Android applications. Smali language; which includes the dex bytecode; is the assembly language under Android OS, it is generated from the Java source code. The phases of designing the target compiler are described and the structure of files that are the input and output of the compiler are explained.

This paper presents the effect of saving Android application execution time on saving energy consumed by optimized applications. An algorithm for optimizing instructions on a Smali code-level proposes to provide execution time. The Smali optimization algorithm relies on replacing high execution times instructions with lower execution times ones and equivalent in behavior. MySMALI compiler is designed to support the proposed optimization algorithm and applied on Android applications. Optimized APK files are generated for optimized applications. Measurements of APKs execution times are taken. Measurements prove that the percentage of optimization in execution time is approximately 26.27%. The paper provides code-level estimates of the energy consumption of Android applications. A programmatic method about reading operating system files is applied to determine resource consumption by the applications. Energy measurements are also recorded by a power monitor (PowerTutor) for Android-based mobile platforms. The measurements of resources (Memory, CPU, Disk) consumption prove that the optimized compiler helps to save the consumption percentage of Android applications about 19.9%. The memory consumed is provided by the optimized compiler to approximately 20000 Kbyte and 31.7 KB size of files. The time that the optimized process of application consumes from the CPU time is reduced from 26% to 5%. The results demonstrate that the providing execution times of applications can save energy consumed to approximately 8.4%, and can save the power consumption by up to 14%.

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