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

More documents

Recommendations

Info

40 CHAPTER 4. PERFORMANCE ENGINEERING OF EVENT-BASED SYSTEMS If ˜W includes a single delivery path ⃗w = (ni1 , n i2 , ..., n im ), an approximation for L t ( ˜W ) can be computed as follows: L t ({ ⃗w}) = ( m ∑ r=1 R CP U t,i r + m∑ r=1 ) m−1 ∑ R I/O t,i r + Rt,Q(i NET r,i r+1) (4.13) r=1 If ˜W includes multiple delivery paths { ⃗w1 , ⃗w 2 , ..., ⃗w h }, we have: ∑ h k=1 L t ({ ⃗w 1 , ⃗w 2 , ..., ⃗w h }) = L t({ ⃗w k }) h (4.14) Definition 9 (Max Mean Delivery Latency) If ˜W ⊆ W t , the max mean delivery latency L t,MAX ( ˜W ) of an event of type e t over the set of delivery paths ˜W is defined as max delivery latency time it takes to deliver an event of type e t over a randomly chosen path from ˜W . If ˜W includes a single delivery path ⃗w = (ni1 , n i2 , ..., n im ), than L t,MAX ( ˜W ) = L t ( ˜W ) If ˜W includes multiple delivery paths { ⃗w1 , ⃗w 2 , ..., ⃗w h }, we have: L t,MAX ({ ⃗w 1 , ⃗w 2 , ..., ⃗w h }) = max(L t ({ ⃗w 1 }), L t ({ ⃗w 2 }), ...L t ({ ⃗w n })) (4.15) 4.1.5 Performance Model Construction and Evaluation If approximate results are not enough and accurate performance prediction is required, a more detailed performance model must be built. One possibility is to model the system using a queueing network, where system nodes and networks are represented as queues and events are represented as jobs served at the queues. Modeling the system in this way would result in a nonproduct form queueing network. This is because every time an event arrives at a system node, it might be forwarded to multiple other nodes, resulting in forking of multiple asynchronous tasks. Even though extended queueing networks make it possible to model the forking of asynchronous tasks, existing analysis techniques for this type of model (for example [91]) are rather restrictive and only provide approximate results. An alternative approach is to model the system using a QPN, where system nodes and networks are represented as queueing places and events are represented as tokens. The forking of asynchronous tasks is much easier to model in this case. Whenever an event is forwarded to multiple system nodes, a transition can be used to create an instance of the event, i.e., an event token, at each of the queueing places corresponding to the target nodes. Modeling the system using QPNs provides a number of important benefits. QPN models provide excellent expressiveness and allow the integration of hardware and software aspects of system behavior into the same model [125]. This can be exploited to model the individual system nodes at a higher level of detail, capturing both hardware and software contention aspects. Furthermore, the knowledge of the structure and behavior of QPNs can be exploited for fast and efficient simulation [127]. This, on the one hand, ensures that models of realistically sized systems can be analyzed. On the other hand, it allows us to have service times with non-exponential distributions, thus improving the model’s representativeness. Figure 4.3 shows a QPN model of the system topology in Figure 4.2. In this model, we ignore the network, assuming that network delays are negligible. We will later show how the model can be extended to include contention for network resources. Each system node is modeled using a nested QPN (represented as a subnet place). The latter can be made as detailed as required to accurately capture the internal behavior of the node. Events are modeled using tokens and transitions are used to move events among nodes as they are routed in the system. Every system node has a single output transition. Event publications are modeled using timed transitions. We will use the following notation:
4.1. MODELING METHODOLOGY FOR EBS 41 Figure 4.3: High-Level System Model. π i is the subnet place corresponding to node n i . ϕ i is the output transition of place π i . φ k is the timed transition used to model event publications by publisher p k . To distinguish between events with different resource consumption and routing behavior, a separate token color x t,k is defined for every combination of event type e t and publisher p k that publishes events of this type (λ t,k > 0). The token color x t,k is defined for place π i , if and only if events of type e t , published by publisher p k , visit the place, i.e., λ t,k i > 0. Every timed transition φ k has a separate firing mode ηk t for each event type e t published by the publisher it represents: η t k : ∅ → π HP (k){1 ′ x t,k } (4.16) This definition specifies that whenever the transition fires in mode ηk t , no tokens are removed from any place and only one x t,k token is deposited in place π HP (k). The firing delay ρ(ηk t ) of this mode is set to the reciprocal of the rate at which events of type e t are published by publisher p k : ρ(ηk t ) = 1/λt,k . Since the firing delays of timed transitions in QPNs are assumed to be exponentially distributed, the above approach to modeling event publications results in Poisson event arrivals. If, however, the distribution of the time between successive event publications is not exponential, a different approach can be used. Instead of a timed transition, a queueing place and an immediate transition are used to model event publications as shown in Figure 4.4. The queueing place has an integrated queue with an Infinite Server (IS) scheduling strategy. The queue service time distribution is equal to the distribution of the time between successive event publications. For every event type e t published by the publisher, there is a single event token x t,k in the initial marking of the place. After an event token is served at the IS queue, the immediate transition fires, moving the token back to the queue and depositing a copy of it in the system node the publisher is connected to. The latter corresponds to an event publication.
Page 1:
Performance Modeling and Benchmarki
Page 4 and 5: priorities. Finally, we validated o
Page 6 and 7: 4.3.4 Tool Extension . . . . . . .
Page 8 and 9: 5.7 Proportions of the Interactions
Page 11 and 12: List of Acronyms Acronym λ t,k j A
Page 13 and 14: Acronym SPEC-OSG SPE SPN SQL ST SUT
Page 15 and 16: Chapter 1 Introduction 1.1 Motivati
Page 17 and 18: PC Server 310 PC Server 310 PC Serv
Page 19 and 20: 1.3. APPROACH AND CONTRIBUTIONS OF
Page 21 and 22: 1.4. THESIS ORGANIZATION 7 • Stan
Page 23 and 24: Chapter 2 Background In this chapte
Page 25 and 26: 2.1. EVENT-BASED SYSTEMS 11 is gene
Page 27 and 28: 2.2. TECHNOLOGY PLATFORMS OF EVENT-
Page 35 and 36: 2.3. INTRODUCTION TO QUEUEING PETRI
Page 37 and 38: 2.3. INTRODUCTION TO QUEUEING PETRI
Page 39 and 40: Chapter 3 Related Work In this chap
Page 41 and 42: 3.2. BENCHMARKING OF EVENT-BASED SY
Page 43 and 44: 3.2. BENCHMARKING OF EVENT-BASED SY
Page 45 and 46: 3.4. CONCLUDING REMARKS 31 hierarch
Page 47 and 48: Chapter 4 Performance Engineering o
Page 49 and 50: 4.1. MODELING METHODOLOGY FOR EBS 3
Page 51 and 52: 4.1. MODELING METHODOLOGY FOR EBS 3
Page 53: 4.1. MODELING METHODOLOGY FOR EBS 3
Page 57 and 58: 4.2. PERFORMANCE MODELING PATTERN 4
Page 87 and 88: 4.3. EXTENSIONS OF QPNS 73 ,-).$%&'
Page 89 and 90: 4.4. CONCLUDING REMARKS 75 Without
Page 91 and 92: Chapter 5 Benchmarking of Event-Bas
Page 93 and 94: 5.1. SPECJMS2007 - A STANDARD BENCH
Page 105 and 106:
5.1. SPECJMS2007 - A STANDARD BENCH
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
5.2. CASE STUDY I: SPECJMS2007 101
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
5.3. JMS2009-PS - A PUBLISH /SUBSCR
Page 127 and 128:
5.3. JMS2009-PS - A PUBLISH /SUBSCR
Page 129 and 130:
5.4. CASE STUDY II: JMS2009-PS 115
Page 131 and 132:
5.4. CASE STUDY II: JMS2009-PS 117
Page 133 and 134:
Chapter 6 Performance Modeling of E
Page 135 and 136:
6.2. MODELING SPECJMS2007 121 Scena
Page 137 and 138:
6.2. MODELING SPECJMS2007 123 BASE*
Page 139 and 140:
6.2. MODELING SPECJMS2007 125 1‘O
Page 141 and 142:
6.2. MODELING SPECJMS2007 127 Sc. 1
Page 143 and 144:
6.2. MODELING SPECJMS2007 129 Perce
Page 145 and 146:
6.2. MODELING SPECJMS2007 131 Serve
Page 147 and 148:
6.3. CONCLUDING REMARKS 133 the mod
Page 149 and 150:
Chapter 7 Conclusions and Outlook W
Page 151 and 152:
7.1. ONGOING AND FUTURE WORK 137 JA
Page 153 and 154:
7.1. ONGOING AND FUTURE WORK 139 th
Page 155 and 156:
Bibliography [1] J. Abbott, K. B. M
Page 157 and 158:
BIBLIOGRAPHY 143 [29] BiCEP Researc
Page 159 and 160:
BIBLIOGRAPHY 145 [61] G. Cugola and
Page 161 and 162:
BIBLIOGRAPHY 147 [92] R. Henjes, M.
Page 163 and 164:
BIBLIOGRAPHY 149 [128] S. Kounev an
Page 165 and 166:
BIBLIOGRAPHY 151 [162] Object Compu
Page 167 and 168:
BIBLIOGRAPHY 153 [197] K. Sachs, S.
Page 169 and 170:
BIBLIOGRAPHY 155 [228] P. Tran, P.
Page 171:
Erklärung Hiermit erkläre ich, di
show all

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

Create successful ePaper yourself

Delete template?

Save as template?