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 .

• 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

• 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

• 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

• 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

• 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

• 4.PhM Patterns Characterization

• 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

• 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.