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통신 프로토콜의 성능평가. SILLA UNIVERSITY . 차 례. 성능분석 방법론 통신 시스템 성능평가 - 채널 할당 통신 프로토콜 성능평가 통신 망 성능평가 . SILLA UNIVERSITY . Performance Analysis Methodology using Petri nets. Stochastic Petri Nets (SPN) Stochastic Rewards Nets (SRN) Markov Reward Model (MRM) . SILLA UNIVERSITY .
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SILLA UNIVERSITY 차 례 성능분석 방법론 통신 시스템 성능평가 - 채널 할당 통신 프로토콜 성능평가 통신 망 성능평가
SILLA UNIVERSITY Performance Analysis Methodology using Petri nets Stochastic Petri Nets (SPN) Stochastic Rewards Nets (SRN) Markov Reward Model (MRM)
SILLA UNIVERSITY Performance Analysis Methodology • Modeling with SRN • 3-stage SRN Analysis Method • Modeling the system with a compact SRN • Generating state space underlying MRM • Solving the generated MRM for the measures • being solved by Stochastic Petri Nets Package(SPNP) : stages 2 and 3
SILLA UNIVERSITY Performance Analysis Methodology SRN 모델로부터 도달성 그래프 생성 도달성 그래프를 Markov Reward Model로 변환 Markov Reward Model의 수치적 해 구함 구해진 해의 기대값으로 성능지표 값 계산 SPNP ; SRN 해를 구하기 위한 소프트웨어 도구
SILLA UNIVERSITY Stochastic Reward Nets (SRN) by associating reward rates with the marking of the SPN. It allows the automated generation of Markov Reward Model(MRM) Marking dependency : permits the rate or probability of a transition to be a function of the number of tokens. Guard (enabling) function : allows the firing of transition based on the global structure of the net.
SILLA UNIVERSITY Stochastic Reward Nets (SRN) • Structural characteristics • Priorities • Guards • Variable cardinality arcs • Marking dependency • Stochastic Characteristics • Allow definition of reward rates in terms of net level entities • Automatically generate the reward rates for the marking
Wireless network : Handoff Model lamh #mu t-new m lamh n queue t-serv t-hoff Figure : SRN model of the handoff methods
Handoff methods • No priority model : m=N, n=N • Reservation channels for handoff calls: m=N-h, n=N • Queueing of handoff calls: m=N, n=c+m • Reservation and queueing of handoff calls: m=N-h, n=N+m
Handoff: General Distribution ln #m1 #m2 #m3 lh que que1 que2 Figure : SRN model of the channel holding time
Table : Enabling function If (#(que)+#(que1)+#(que2) <ena(X)) return(1); else return(0);
Analysis techniques • limitation of using SRNs: only allowing exponential distribution • phase type approximation to handle general distribution • Convolution: Erlang, sum of exponentials • Mixture: Hyperexponential • approximate the channel holding time bye the sum of exponentials • can be done by moment matching methods using the 1st, 2nd, 3rd, moments of ch. Holding time and those of the sum of exp. • Handling state largeness: aggregation method,
Develop Advanced Modeling Techniques • state space based model • Largeness problem • decomposition/ iteration • parallel/distributed generation • FSPN • Non-Markovian space • Phase type expansions • Markov renewal theory • Discrete event simulation
SILLA UNIVERSITY Modeling- SRN model of Hard handoff
SILLA UNIVERSITY Reachability Graph for the SRN model (pqn,pqh,pcall,ch_pool)
SILLA UNIVERSITY CTMC derived from RG
성능평가 • 성능분석 단계 • SRN 모델링 • 도달성 그래프 • 연속시간 마르코프 체인 • 성능지표와 reward rate
Ready to send Ready to receive Send message Receive message Buffer full Wait for ack. Message received Process 1 Process 2 Receive ack. Send ack. Buffer full Ack. received Ack. sent Fig. 9. A simplified model of a communication protocol.
성능평가 • 입력 큐를 고려 안한 경우(단일 호) • 평균 응답시간 • 처리율
성능평가 • 입력 큐를 고려 한 경우(다중호) • 평균 지연 시간 = 평균 응답시간+입력 큐의 평균 지연시간 • 처리율
