From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 1 From UML to Performance Models: High Level View Dorina C. Petriu Gordon Gu Carleton University well formed annotated UML models introduction to LQN high-level view of the transformation

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 2 Well-formed annotated UML model key use cases described by representative scenarios  frequently executed, have performance constraints resources used by each scenario resource types: active or passive, physical or logical, hardware or software  examples: processor, disk, process, software server, lock, buffer quantitative resource demands must be given for each scenario step  how much, how many times? workload intensity for each scenario open workload: arrival rate of requests for the scenario closed workload: number of simultaneous users

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 3 Software architecture and deployment Retrieve SDiskIO DEserver Client Server server client > 1..k DEclient > 1..n DEclient > ClientCPU > Ethernet > ServerCPU DEserver > Sdisk >

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 4 Scenario with performance annotations ClientRetrieveTSDiskIOT request document wait_S accept request read request update logfile wait_D write to logfile parse request get document read from disk send document recycle thread receive document > {PAdemand=(‘msrd’, ’mean’,(220/$cpuS,’ms’))} > {PAdemand=(‘msrd’,’mean’, ( /$cpuS,’ms’))} > {Papopulation = $Nusers} > {PAdemand=(‘asmd’,’mean’ (0.5,’ms’)), PAextOp=(‘net1’,1) PArespTime= (‘req’,’mean’, (1,’sec’), (‘pred’,’mean’,$RespT)}} > {PAdemand=(‘asmd’, ’mean’, (1.5,’ms’))} > {PAdemand=(‘msrd’,’mean’, (35/$cpuS,’ms’))} > {PAdemand=(‘msrd’,’mean’, (25/$cpuS,’ms’))} > {PAdemand=(‘msrd’,’mean’, ($cdS,’ms’)), PAextOp=(‘readDisk’, $DocS’)} > {PAdemand=(‘msrd’,’mean’, (0.70,’ms’)), PAextOp=(‘writeDisk’, $RP’)} > {PAdemand=(‘msr’,’mean’, (170/$cpuS,’ms’))} > {PAdemand=(‘msrd’,’mean’, ($scdC/$cpuS,’ms’)), PAextOp=(‘net2’,$DocS’)} > {PAdemand=(‘msrd’,’mean’, ($gcdC/$cpuS,’ms’))}

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 5 Layered Queueing Network (LQN) model Advantages of LQN modeling models software tasks (rectangles) and hardware devices (circles) represents nested services (a server is also a client to other servers) software components have entries corresponding to different services arcs represent service requests (synchronous and asynchronous) multi-servers used to model components with internal concurrency What can we get from the LQN solver Service time (mean, variance) Waiting time Probability of missing deadline Throughput Utilization clientE DBWriteDBRead DB DKWriteDKRead Client CPU ClientT Disk DB CPU DB Disk

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 6 UML -> LQN Transformations: Mapping the structure Comp <<PAhost>> XCPU <<PAhost>> XCPU (3) <<PAresource>> Ydisk (4) <<PAresource>> CompT XCPU (5) <<PAhost>> XCPU <<PAresource>> ThreadT XCPU (6) Thread Comp <<PAhost>> XCPU Comp <<PAresource>> 1..n (1) Comp <<PAresource>> 1..n (1) Active <<PAresource>> (2) <<PAresource>> Active <<PAresource>> (2) XCPU <<PAhost>> XCPU (3) XCPU <<PAhost>> XCPU (3) << Ydisk (4) << Ydisk (4) <<PAresource>> (5) <<PAhost>> XCPU > <<PAresource>> (6) Thread > CompT Active

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 7 UML->LQN Transformation: Mapping the Behavior Client CPU e1 [ph1] Client CPU Server e2 [ph1, ph2] Server

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 8 Mapping software architecture and physical devices to LQN b) Mapping physical resources (processors and I/O devices) to LQN devices a) Mapping software architecture to LQN tasks DEclientT RetrieveT SDiskIOT Retrieve SDiskIO DEserver Client Server server client > 1..k DEclient > 1..n DEclientT RetrieveT SDiskIOT Client CPU Server CPU Sdisk DEclient > ClientCPU > Ethernet > ServerCPU DEserver > Sdisk >

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 9 Effect of communication network DEclientT RetrieveT SDiskIOT ClientC PU Server CPU Sdisk DEclient > ClientCPU > Ethernet > ServerCPU DEserver > Sdisk > net1 net2 dummy CPU Ethernet

From UML to Performance Models 29 June 2004 Dorina C. Petriu, Gordon Gu page 10 Groups of scenario steps to LQN entries DEclientT RetrieveT SDiskIOT request document wait_r read request update logfile wait_d parse request get document accept request send document write to logfile read from disk send document entry clientE phase 2 entry retrieveE phase 1 entry retrieveE ph 2 entry write phase 1 entry read phase 1 DEclientT Client CPU Sdisk Ethernet clientE [ph2] net1 dummy CPU net1E RetrieveT sever CPU retrieveE [ph1,ph2] net2 net2E SDiskIOT write [ph1] read [ph1]