Performance analysis and prediction of physically mobile systems

1. Performance analysis and prediction of physically mobile systems • Point view: • Computational devices including Mobile phones are expanding. • Different infrastructure from traditional systems. • Mobile systems require connectivity, dynamicity and resource availability. • Logical mobility (software mobility) and physical mobility • (hardware mobility). • Authors present a methodology for modeling performance of physically mobile systems.

2. Introduction • Mobile phones interact with other phones (peer to peer mode e.g. BLUETOOTH) and with fixed network backbone e.g. Cellular Connection in dynamic way. • Mobile phones offer all functionalities . • Mobile phones resources are very limited (little memory, • Limited energy) • Software engineering for distributed systems is inadequate • To deal with dynamically environment for mobile systems

3. Authors methodology • They suggest a methodology for modeling performance of physically mobile systems as following: • 1. Modeling of the application • 2. layered queuing network (LQN) generation and performance analysis: • (a) Meta-LQN generation • (b) LQN models generation • (c) physical mobility (PhM) pattern characterization • 3. Results interpretation

4. 1)modeling of application • a) use-case diagram • b) component diagram • C) sequence diagram • Physical mobility description: • To describe the physical mobility (PhM) patterns we use UML State Diagrams where each node represents a context and the arrows among states represent the probability that the user will be moving from the starting context to the destination one

5. 2.Meta-LQN Generation: • 1. the operational profile • 2. the scheduling policy of software components • 3. the loop repetition factors and behavioral alternative probabilities • 4. the host demand

6. 3.LQN Models Generation • • it identifies the hardware components in the deployment • diagram and instantiates an LQN devices for each of them; • • it adapts the meta-LQN model according to the software • components reachable/visible in the location. • • it adds LQN tasks to LQN device interconnections according to the Deploy association in the deployment diagram. • • it adds additional LQN tasks to LQN device interconnections according to the resource name executing the external operation the additional task

7. 4.PhM Patterns Characterization

8. Analysis Scope and Results Interpretation • Predictive performance analysis becomes of primary importance in the context of mobile applications, given the • dynamicity of these systems and the often scarce resources involved. The performance indices of interests in mobile applications are mainly service response time and device utilization. • The utilization of devices, in particular of the bandwidth • in wireless networks, can be extremely useful for the • identification of the hardware performance bottlenecks and • to evaluate potential alternatives, including the strengthening of the hardware

9. Technical review • 1)good new methodology, but still reqiures improvement • 2)all environments of mobile systems are not considered like (connection effort, handover, etc…) • 3)verr short description of methodology • 4)the mathematical formulas are not illustrated well, just within the figure.