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100 Term Table 2: List of terms used repeatedly in this study and their respective definitions Definition Units Bdisk Block size on disk MB Tsvr Server observation time interval second ξ The number of disks in the system n The number of concurrent streams piodisk Probability of missed deadline by reading or writing RDr Average disk read bandwidth during Tsvr (no bandwidth allocation for writing) MB/s preq The threshold of probability of missed deadline, it is the worse situation that client can endure. RDw Average disk write bandwidth during Tsvr (no bandwidth allocation for reading) MB/s tseek(j) RDr(j) µtseek (j) σtseek (j) β Seek time for disk access j, where j is an index for each disk access during a Tsvr Disk read bandwidth for disk access j (no bandwidth allocation for writing) Mean value of random variable tseek(j), where j is an index for each disk access during a Tsvr Standard deviation of random variable tseek(j) Relationship factor between RDr and RDw ms MB/s ms ms tseek The average disk seek time during Tsvr ms µtseek Mean value of random variable tseek ms σtseek α Standard deviation of random variable tseek Mixed-load factor, the percentage of reading load in the system ms m1 The number of movies existed in HYDRA D rs i The amount of data that movie i is consumed during Tsvr MB µ rs i σ rs i n rs i m2 D ws i µ ws i σ ws i n ws ENTERPRISE INFORMATION SYSTEMS VI Mean value of random variable D rs i Standard deviation of random D rs i The number of retrieving streams for movie i The number of different recording devices The amount of data that is generated by recording device i during Tsvr Mean value of random variable D ws i Standard deviation of random D ws i The number of recording streams by recording device i i Nmax The maximum number of streams supported in the system S buf min The minimum buffer size needed in the system MB drives is platter location dependent. The outermost zone provides up to 30% more bandwidth than the innermost one. iv support flexible service requirements (see Section 4.1 for details), which should be configurable by Video-on-Demand (VOD) service providers based on their application and customer requirements. As discussed in Section 3, a double buffering scheme is employed in HYDRA. Therefore, two buffers are necessary for each stream serviced by the system. Before formally defining the MSB problem, we outline our framework for service requirements in the next section. Table 4 lists all the parameters and their definitions used in this paper. 4.1 Service Requirements Why do we need to consider service requirements in our system? We illustrate and answer this question with an example. Assume that a VOD system is deployed in a fivestar hotel, which has 10 superior deluxe rooms, 20 deluxe rooms and 50 regular rooms. There are 30 MB MB MB MB movies stored in the system, among which five are new releases that started to be shown in theaters during the last week. Now consider the following scenario. The VOD system operator wants to configure the system so that (1) the customers who stay in superior deluxe rooms should be able to view any one of the 30 movies whenever they want, (2) those customers that stay in deluxe rooms should be able to watch any of the five new movies released recently at anytime, and finally (3) the customers in the regular rooms can watch movies whenever system resources permit. The rules and requirements described above are formally a set of service constraints that the VOD operator would like to enforce in the system. We term these type of service constraints service requirements. Such service requirements can be enforced in the VOD system via an admission control mechanism. Most importantly, these service requirements will affect the server buffer requirement. Next, we will describe how to formalize the memory configuration problem and find the minimal buffer size in a streaming media system.
E.g., DV Camcorder Camera Packets (e.g., RTP) Data Stream Recorder Data sources produce packetized realtime data streams (e.g., RTP) Microphone Internet (WAN) Recording Admission Control Node Coordination Packet Router LAN Environment Haptic Sensor Packets Display / Renderer Playback (Data is transmitted directly from every node) Node 0 Aggregation Aggregation Node 1 Aggregation Node N Mem. Mgmt Mem. Mgmt Mem. Mgmt Scheduler Scheduler Scheduler B5 B1 B2 B0 B6 B3 B4 B7 Figure 3: HYDRA: Data Stream Recorder Architecture. Multiple source and rendering devices are interconnected via an IP infrastructure. The recorder functions as a data repository that receives and plays back many streams concurrently 4.2 MSB Problem Formulation 4.2.1 Stream Characteristics and Load Modeling Given a specific time instant, there are m1 movies loaded in the HYDRA system. Thus, these m1 movies are available for playback services. The HY- DRA system activity is observed periodically, during a time interval Tsvr. Each movie follows an inherent bandwidth consumption schedule due to its com- pression and encoding format, as well as its specific content characteristics. Let Drs i denote the amount of data that movie i is consuming during Tsvr. Furthermore, let µ rs i and σrs i denote the mean and standard deviation of Drs i , and let nrs i represent the number of retrieval streams for movie i. We assume that there exist m2 different recording devices which are connected to the HYDRA system. These recording devices could be DV camcorders, microphones or haptic sensors as shown in Figure 3. Therefore, in terms of bandwidth characteristics, m2 types of recording streams must be supported by the recording services in the HYDRA system. Analogous with the retrieval services, Dws i denotes the amount of data that is generated by recording device i during time interval Tsvr. Let µ ws i and σ ws i denote the mean MEMORY MANAGEMENT FOR LARGE SCALE DATA STREAM RECORDERS and standard deviation of D ws i and let n ws i represent the number of recording streams generated by recording device i. Consequently, we can compute the total number of concurrent streams n as m1 � � n = i=1 n rs m2 i + i=1 n ws i (1) Thus, the problem that needs to be solved translates to finding the combination of < nrs 1 ...nrs m1 ,nws 1 ...nws m2 >, which n. Hence, Nmax can be computed as maximizes m1 � Nmax = max(n) = max( � n rs m2 i + n ws i ) (2) i=1 i=1 under some service requirements described below. Note that if the double buffering technique is employed, and after computing Nmax, we can easily obtain the minimum buffer size S buf min as S buf min = 2BdiskNmax (3) where Bdisk is the data block size on the disks. Note that in the above computation we are considering the worst case scenario where no two data streams are sharing any buffers in memory. 4.2.2 Service Requirements Modeling We start by assuming the example described in Section 4.1 and following the notation in the previous section. Thus, let nrs 1 , ..., nrs 30 denote the number of retrieval streams corresponding to the 30 movies in the system. Furthermore, without loss of generality, we can choose nrs 1 , ..., nrs 5 as the five newly released movies. To enforce the service requirements, the operator must define the following constraints for each of the corresponding service requirements: C1: nrs 1 , ..., nrs 30 ≥ 10. C2: nrs 1 , ..., nrs 5 ≥ 20. Note that we do not define the constraint for the third service requirement because it can be automatically supported by the statistical admission model defined in the next section. The above constraints are equivalent to the following linear constraints: C1: nrs 1 , ..., nrs 5 ≥ 30. C2: nrs 6 , ..., nrs 30 ≥ 10. These linear constraints can be generalized into the following linear equations: �m1 j=1 ars ij nrs j + �m2 k=1 aws ik nws k ≤ bi nrs j ≥ 0 nws ≥ 0 k n rs j and nws k are integers 101 (4)
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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Page 61 and 62: 48 ENTERPRISE INFORMATION SYSTEMS V
Page 67 and 68: ASSESSING EFFORT PREDICTION MODELS
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Page 111: Data Rate [bytes/sec] 1600000 14000
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Page 119 and 120: PART 2 Artificial Intelligence and
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Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
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152 ENTERPRISE INFORMATION SYSTEMS
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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