Performance Modeling and Benchmarking of Event-Based ... - DVS

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86 CHAPTER 5. BENCHMARKING OF EVENT-BASED SYSTEMS Intr. Message Destination Type Prop. Description 1 2 callForOffers Topic (HQ) TextMsg P, T, D Call for offers sent from DC to SPs (XML). offer Queue (DC) TextMsg P, T Offer sent from SP to DC (XML). pOrder Queue (SP) TextMsg P, T Order sent from DC to SP (XML). pOrderConf Queue (DC) TextMsg P, T Order confirmation sent from SP to DC (XML). invoice Queue (HQ) TextMsg P, T Order invoice sent from SP to HQ (XML). pShipInfo Queue (DC) TextMsg P, T Shipment from SP registered by RFID readers upon arrival at DC. pShipConf Queue (SP) TextMsg P, T Shipment confirmation sent from DC order Queue (DC) ObjectMsg P, T Order sent from SM to DC. orderConf Queue (SM) ObjectMsg P, T Order confirmation sent from DC to SM. shipDep Queue (DC) TextMsg P, T Shipment registered by RFID readers upon leaving DC. statInfo- Queue (HQ) StreamMsg NP, NT Sales statistics sent from DC to HQ. OrderDC shipInfo Queue (SM) TextMsg P, T Shipment from DC registered by RFID readers upon arrival at SM. shipConf Queue (DC) ObjectMsg P, T Shipment confirmation sent from SM to DC. statInfo- ShipDC to SP (XML). Queue (HQ) StreamMsg NP, NT Purchase statistics sent from DC to HQ. 3 priceUpdate Topic (HQ) MapMsg P, T, D Price update sent from HQ to SMs. 4 inventoryInfo Queue (SM) TextMsg P, T Item movement registered by RFID readers in the warehouse of SM. 5 statInfoSM Queue (HQ) ObjectMsg NP, NT Sales statistics sent from SM to HQ. 6 product- Announcement Topic (HQ) StreamMsg NP, NT, ND 7 creditCardHL Topic (HQ) StreamMsg NP, NT, ND New product announcements sent from HQ to SMs. Credit card hotlist sent from HQ to SMs. Table 5.1: Message Types Used in The Interactions - (N)P=(Non-)Persistent; (N)T=(Non-)Transactional; (N)D=(Non-)Durable
5.1. SPECJMS2007 - A STANDARD BENCHMARK 87 Intr. Message Sizing Parameters Message m 1 m 2 b 1 No of order lines sent to DC 2 No of purchase order lines sent to SP orderConf 0.0565 na 1.7374 statInfoOrderDC 0.0153 na 0.1463 shipInfo 0.0787 na 0.8912 shipDep 0.0787 na 0.7222 order 0.0565 na 1.4534 shipConf 0.0202 na 0.7140 callForOffers 0.1785 na 0.8094 offer 0.2489 na 0.9414 pOrder 0.2498 na 1.1076 pShipConf 0.0827 na 0.7612 statInfoShipDC 0.0831 na 0.7681 pOrderConf 0.2410 na 1.3494 invoice 0.1942 na 1.1211 pShipInfo 0.0827 na 0.7279 3 Message has fixed size priceUpdate na na 0.2310 4 No of registered items leaving warehouse inventoryInfo 0.0970 na 0.5137 5 No of cash desks & sales lines statInfoSM 0.0139 0.3650 0.9813 6 No of new products announced productAnnouncement 0.0103 na 0.1754 7 No of credit cards in hot list creditCardHL 0.0166 na 0.1846 Table 5.2: Parameters for Message Size Calculation Group a b c d Type Pub/Sub Pub/Sub P2P P2P Properties NP NT ND P T D NP NT P T Table 5.3: Message Groups Note that although the modeled scenario has a single physical HQ location, the benchmark allows multiple HQ instances to exist each with its own set of queues. The goal is to avoid the HQ queues becoming a bottleneck when scaling the number of SMs, DCs and SPs. It is assumed that messages sent to the HQ are distributed evenly among the HQ instances. Multiple HQ instances are considered as separate servers within the same physical location. For each interaction, the interaction rate specifies the rate at which the interaction is initiated by every physical instance of its initiating location, SM for Interaction 1, DC for Interaction 2, etc. We denote the interaction rates as λ i , 1 ≤ i ≤ 7. Since multiple HQ instances are not considered as separate physical locations, it follows that the rates of Interactions 3, 6 and 7 which are initiated by the HQ are interpreted as rates over all HQ instances as opposed to rates per HQ instance. Interaction 2 uses a set of topics representing the different product families offered by suppliers. These topics help to distribute the callForOffers messages sent by DCs. Suppliers subscribe to all topics corresponding to groups of products they offer so that they receive all relevant callForOffers messages. We denote the set of product families as Π = {P F 1 , P F 2 , P F 3 , . . . , P F |Π| }. The probability that a SP offers products from a given product family P F i ∈ Π is a configurable workload parameter and will be denoted as ρ. Every SP subscribes to ρ · |Π| product families and thus |Ψ SP | · ρ · |Π| subscriptions exist overall. The number of subscribers that subscribe to a given product family is denoted as ζ = |Ψ SP | · ρ. In the following, we show how the message throughput, in terms of the number of messages sent and received per unit of time, can be broken down according to the type of messaging (P2P vs. pub/sub) and the message delivery mode (persistent vs. non-persistent, transactional vs. non-transactional, durable vs. non-durable). To this end, we group messages as shown in
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Performance Modeling and Benchmarki
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priorities. Finally, we validated o
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4.3.4 Tool Extension . . . . . . .
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5.7 Proportions of the Interactions
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List of Acronyms Acronym λ t,k j A
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Acronym SPEC-OSG SPE SPN SQL ST SUT
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Chapter 1 Introduction 1.1 Motivati
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PC Server 310 PC Server 310 PC Serv
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1.3. APPROACH AND CONTRIBUTIONS OF
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1.4. THESIS ORGANIZATION 7 • Stan
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Chapter 2 Background In this chapte
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2.1. EVENT-BASED SYSTEMS 11 is gene
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2.2. TECHNOLOGY PLATFORMS OF EVENT-
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2.3. INTRODUCTION TO QUEUEING PETRI
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2.3. INTRODUCTION TO QUEUEING PETRI
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Chapter 3 Related Work In this chap
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3.2. BENCHMARKING OF EVENT-BASED SY
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3.2. BENCHMARKING OF EVENT-BASED SY
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3.4. CONCLUDING REMARKS 31 hierarch
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Chapter 4 Performance Engineering o
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Bibliography [1] J. Abbott, K. B. M
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BIBLIOGRAPHY 153 [197] K. Sachs, S.
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BIBLIOGRAPHY 155 [228] P. Tran, P.
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Erklärung Hiermit erkläre ich, di
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