P a r r o t. P r i m e

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1 To appear in World Automation Congress (WAC'98) Prole Aware Retrieval Optimizer for Continuous Media Cyrus Shahabi, Ali Esmail Dashti, and Shahram Ghandeharizadeh Integrated Media Systems Center and Computer Science Department University of Southern California, Los Angeles, California ABSTRACT One of the key components of multimedia systems is a Continuous Media (CM) server that guarantees the uninterrupted delivery of continuous media data (i.e., audio and video). Queries imposed by applications, such as customized news-on-demand, might require the retrieval of one or more continuous objects from the CM server. Traditionally, multimedia systems have opted to guarantee that the CM server can display all the objects in the set to the user with no interruptions and with very strict display timing and ordering among the objects. This results in a single possible retrieval plan. However, for a class of applications, we have observed that depending on the user query, user prole, and session prole, there are a number of exibilities that can be exploited for retrieval optimization, namely: delay, ordering, presentation, and display-quality exibilities. In this paper, we describe a Prole Aware Retrieval Optimizer (Prime) that utilizes these exibilities to improve system performance by reducing retrieval contention at the CM server. KEYWORDS: Retrieval Optimization, Continuous Media Servers, Multimedia Systems INTRODUCTION In many multimedia applications, the result of a query is a set of continuous media objects (i.e., audio or video) that should be retrieved from a continuous media server (CM server), such as Mitra [4], and displayed to the user. The display requirements of such applications can be classied as either: 1) two-pass, or 2) single-pass. In the two-pass paradigm, during the rst pass, a set of objects are identied. Subsequently, the user interactively selects the objects of interest for display. To assist the user, textual and thumbnail information are used to represent dierent objects. Most of current Internet applications use the two-pass paradigm. In the single-pass paradigm, a set of temporal relationships govern the display timing of the objects. The display of the objects is considered to be coherent when all of the temporal relationships This research was supported in part by gifts from Hewlett-Packard, NSF grants EEC (IMSC ERC), IRI , IRI (NYI award), and NIMH grant 1P20MH A1 (USC Brain Project).

2 2 User query (e.g., "show today s news") User Profile Session Profile CM metadata P a r r o t queryscript P r i m e Retrieval schedule Load CM Server Figure 1. System Architecture. are satised. Therefore, after the submission of the request, no user interaction is necessary. That is, the system starts the display as soon as it can guarantee that the CM server can display all of the objects in the set to the user with no interruptions, while satisfying the temporal relationships. Some sample applications that may use the single-pass display paradigm include: customized news-on-demand, customized advertisement, and digital libraries & museums. Applications using the single-pass paradigm can be classied as either: 1) Restricted Presentation Applications (RPA), or 2) Flexible Presentation Applications (FPA). RPA, such as non-linear editing applications, have very strict set of temporal relationships that have to be met. This strict timing requirement results in a single retrieval plan, and hence very limited retrieval optimization is possible. However, in FPA, the set of temporal relationships among the objects is not as strict, yielding a number of equivalent retrieval plans that can be used for retrieval optimization purposes. FPA provides some exibilities in the presentation of the continuous media objects. It is usually the case that the user does not know exactly what he/she is looking for and is only interested in displaying the objects with some criteria (e.g., show me todays news). In this case, depending on the user query, user prole, and session prole, there are a number of exibilities that can be exploited for retrieval optimization. We have identied the following exibilities: 1) delay exibility: which species the amount of delay the user/application can tolerate or require between the display of dierent continuous media clips (i.e., relaxed meet, after, and before relationship [1]), 2) ordering exibility: which refers to the display order of the objects (i.e., to what degree the display order of the objects is important to the user), 3) presentation exibility: which refers to the degree of exibility in the presentation length and presentation startup latency, and 4) display-quality exibility: which species the display qualities acceptable by the user/application, when data is available in multiple formats (e.g., MPEG I, MPEG II, etc.) and/or in hierarchical or layered formats (based on layered compression algorithms) [5]. Flexibilities, in FPA, allow for the construction of multiple retrieval plans per presentation. Subsequently, the best plan is identied as the one which results in minimum contention at the CM server. To achieve this, three steps should be taken: Step 1: gathering the exibilities, Step 2: capturing the exibilities in a formal for-

3 3 mat, and Step 3: using the exibilities for optimization. In our system architecture, Figure, the rst two steps are carried out by the Prole Aware User Query Combiner (Parrot). It takes as input the user query, user prole, CM server metadata, and session prole (e.g., type of display monitor) to generate a query script. This query script would capture all the exibilities and requirements in a formal manner. The query script is then submitted to the Prole Aware Retrieval Optimizer (Prime) which in turn would use it to generate the best retrieval plan for the CM server. This paper is a short version of [7] and its focus is on the formal denition of the query script and the process of using this information to optimize continuous media data retrieval from the CM server. In this paper, we do not consider design of Parrot and the process of the query script generation. These issues are part of our future research. It is the responsibility of Prime to determine how the query script should be imposed against the CM server to reduce contention. The number of IO's to retrieve continuous media objects from the CM server is xed, however, dierent retrieval plans inuence retrieval contention at the server. Prime nds a retrieval plan such that it minimizes contention at the CM server, in order to improve system performance. This process consists of accepting a query script and then nding the best retrieval plan to be scheduled on the CM server. Therefore, Prime is concerned with three issues: 1) search space, 2) cost model, and 3) a strategy to search for the best retrieval plan. The search space is dened by the query script. The number of correct retrieval plans determines the size of this search space. The cost model is a set of metrics used to evaluate each correct retrieval plan that is being considered. The search strategy explores the search space for the best retrieval plan based on the dened metrics. The CM server, utilized by Prime, guarantees the uninterrupted display of the continuous media objects. There has been a number of studies describing the design and implementation of such servers, see [8, 2]. We ignore the detail architecture of the CM server and conceptualize it as a server bandwidth, termed R CM. The rest of this short paper is organized as follows. We start by presenting a formal denition of the query-script received by Prime. After that, we consider the search-space, the cost-model, and the search-strategies, while optimizing for one query script at a time. Finally, we conclude and describe a number of possible extensions to our research. QUERY SCRIPT We are considering four types of exibilities that might be tolerable by user/application submitting a request: delay, ordering, presentation-time, and display-quality. To capture the delay exibility, we dene a minimum and a maximum tolerable delay between the nishing time of one object and the start time of the subsequent object (T Min Delay and TDelay). Max For ordering exibility, we dene three variations: 1) Unordered-Object-Retrieval (UOR), 2) Suggested-Object-Retrieval (SOR), and 3) Ordered-Object-Retrieval (OOR). To illustrate, consider a query script q that references n continuous media objects, q = fo 1 ; o 2 ; :::; o n g. UOR imposes no ordering

4 4 constraint on the display of the n objects (e.g., no ranking expression). SOR suggests an ordering for the n objects; however, this ordering is not restrictive, rather it is given with some condence (e.g., the ranking expression is taken with some condence). It is expected that a large number of queries imposed on multimedia applications to be of this type. OOR requires the display of the n objects in a specic order, and it is necessary to satisfy this ordering (e.g., the ranking expression is used to order the display). We show that UOR and OOR are special cases of SOR. TABLE I. Terms used repeatedly and their corresponding denitions. Term Denition o j An object identier; no ordering is associated with the subscript. l(o j ) The length of the object o j intime intervals. MinBand(o j ) Function returns the minimum bandwidth requirement of object o j. s(o j ) It is the time at which the display of o j starts. f(o j ) It is the time at which the display of the object o j nishes, f(o j ) = s(o j ) + l(o j ). Band(k) Available bandwidth at time interval k, 0 Band(k) 1 (normalized by the CM server bandwidth, R CM ) (o j ; i) Function returns the condence value of displaying object o j in the ith position. r(q) It is the time at which the query script is released to Prime. s(p) It is the time at which the rst object of the retrieval plan is displayed, s(p) = s(o j ) where P os(o j ; p) = 1. P os(o j ; p) Function returns the position of object o j in the retrieval plan p. f(p) It is the time interval at which the last object of the retrieval plan nishes its display, f(p) = f(o j ) where P os(o j ; p) = n. T Response The time elapsed from the release time of the query script r(q) to the nish time of the plan f(p), T Response (p) = f(p)? r(q). T Latency The time elapsed from the release time of the query script r(q) to the start time of the retrieval plan s(p), T Latency (p) = s(p)? r(q). (i;i+1) (p) Delay between the completion time of object o r at position i, and starting time of object o s at position i + 1, 8i : 1 i (n? 1). To capture the presentation-time exibility, we dene two variables: 1) the presentation length, T Length, as the the total time to display all objects in a request including the delays between them, and 2) the presentation start-up latency, T, as the time elapsed from the submission of the request to the start of the presentation. Furthermore, we dene a minimum presentation length, TLength, Min and a maximum presentation length, TLength, Max as well as a minimum startup latency time, T, Min and a maximum startup latency time, T, Max tolerated by the user and the application. To capture the display-quality exibility, we dene a function C that returns a set of m acceptable display bandwidths for an object o, (C(o) = fc 1 (o); c 2 (o); ::; c m (o)g). We assume that this function returns information on all display formats that are available on the CM server and that are acceptable to the user for object o. The three ordering variations we are considering (U OR, SOR, and OOR), require

5 5 the retrieval of n objects with the constraints that: 1) delay-exibility: delay,, between the nishing time of an object and the start time of the subsequent object is bounded by T Min Delay and T Max Delay (T Min Delay T Delay), Max 2) presentation-exibility: i) presentation length, T Length, is bounded by T Min Length and T Max Length (T Min Length T Length TLength), Max ii) startup latency, T, is bounded by T Min and T Max (T Min T T) Max 3) display-quality: for all objects in the request, there are enough system resources to satisfy one of the bandwidths requirements (c 1 ; c 2 ; ::; c m ) returned by C. Using Table as a reference, consider the following denition for a query script: Denition 1: A query script q submitted to the optimizer (Prime) consist of the following parameters: n objects o j 2 O (O is the set of all continuous media objects available on the CM server) for 8j : 1 j N, where N = joj. T Max Delay & T Min Delay, T Max Length & T Min Length, and T Max & T Min. Condence level threshold, 0 1 (minimum acceptable condence in the object ordering). Function (o j ; i), 8i; j : 1 i; j n, where 0 (o j ; i) 1. Function C(o j ) = fc k (o j )j8j; k : 1 j n and 1 k m; 0 < c k (o j ) 1g, where 8j : 1 j n; C(o j ) 6= ;. SEARCH SPACE Prime searches dierent correct retrieval plans permitted by the query script to nd the best retrieval schedule. A retrieval plan p, whether correct or not, consists of n tuples < o j ; i > (8i; j : 1 i; j n), where o j is one of the n objects to be displayed from the query script and i is one of the n possible positions. The search space consists of all of the correct retrieval plans, where the correctness of a retrieval plan depends on the display order being considered with the given condence threshold. Denition 2: A retrieval plan p consists of n tuples < o j ; i >, 8i; j : 1 i; j n. This retrieval plan is said to be correct i: 8j : 1 j n, 1 P os(o j ; p) n (all of the objects in the query script q are considered in the retrieval plan p). 8i; j : 1 i; j n, if i 6= j then P os(o i ; p) 6= P os(o j ; p) (two objects do not occupy the same position). P n j=1 (o j ;P os(oj;p)) (p) = (the average condence level of the retrieval plan n is above the given threshold). The above query script and retrieval plan denitions capture all three variants of the ordering exibilities. It naturally captures SOR, because the query script denition allows the optimizer to consider dierent retrieval plans as long as their average condence level (p) is above the condence threshold. To capture UOR, it is important to generate all the n! possible retrieval plans. To achieve this, the value of (p) has to be independent of the ordering, constant for all permutations, and always greater than the threshold. This can be accomplished by setting = 0 and xing (o j ; i) 0 for all i and j. This setup would allow all possible retrieval

6 6 plans. In case of OOR, it is important to allow only one retrieval plan. This can be accomplished by setting the threshold to the highest value, = 1, and making the condence evaluator (o j ; i) return 1 when the object is being displayed at the correct order and 0 otherwise. Therefore, a correct query script setup would result in capturing the appropriate query variation. As dened above, the search space consists of all the correct retrieval plans. However, in general, nding the complete search space seems to be an intractable problem. As part of our future research, we plan on either proving that this problem is NPcomplete, and/or nding an optimal algorithm that generates the entire search space for all or special cases of and. Intuitively, to generate the search space, it is necessary to check all n! retrieval plan permutations and make sure that the plan condence level (p) is greater than the given threshold. OOR and UOR are two special cases, where the search space can be found easily. In the more general case, namely SOR, we employ a heuristic to nd a subset of the search space. This heuristic nds the partial search space by rst sorting the objects at each position by their con- dence level. Next, the heuristic prunes all permutations that do not seem promising. The running time of the heuristic is O(n 2 log n), which is dominated by the sorting time of the n lists. COST MODEL We optimize for the response time (T Response ) and the latency time (T Latency ) of the query script that is being considered. However, before discussing these optimization metrics, consider the denitions given in Table and the following retrieval schedule denition: Denition 3: Retrieval schedule (p) of retrieval plan p consists of n triplets: < o j ; i; s(o j ) >, where o j is the object being displayed, i is the position of the object in the plan, and s(o j ) is the start time of object o j. This retrieval schedule is correct i: 8i; j : 1 i; j n, 9 a triplet for each o j. 8i; j : 1 i n, Band(k) MinBand(o j ), where s(o j ) k f(o j ). 8i; j : 1 i (n? 1), T Min Delay i;i+1(p) T Max Delay. T Min max Length f(p)? s(p) T T Min max s(p) T. Length. The time required to nd a retrieval schedule for a retrieval plan is termed Scheduling time, T Schedule. This time depends on the media server load, the query script parameters (e.g., n, TDelay, Max and TDelay), Min the data structures used for conceptualizing them, and the implementation optimizations used. When scheduling the retrieval plans, we are implicitly considering T Response and T Latency as two major optimizations metrics. This is due to the fact that we try to schedule a given retrieval plan as soon as possible, and hence minimizing both metrics. It is possible to apply three other metrics that we believe are relevant to the type of applications we are considering, namely retrieval plan: average delay ( Avg ),

7 7 delay variance ( V ar ), and condence level ((p)), see Table for denitions. For each schedule, we calculate all of the metrics, and Prime may choose to optimize for one or more of these metrics. The order by which these metrics are applied may aect the selected retrieval plan and is application dependent. In this study, we assume that T Response is considered for the rst optimization level, see [7] for details. SEARCH STRATEGY The objective of Prime is to nd the best retrieval plan in the search space, where best is dependent on the metrics used. This optimization problem is somewhat similar to the bin-packing problem. Bin-packing strives to t a set of variable size items into a set of xed size bins. The objective is to minimize the number of bins lled. Binpacking is shown to be NP-complete [3] and a good heuristic devised for bin-packing is First Fit Decreasing (FFD). In our case, each object can be considered as an item and the bins are discrete time intervals over the system bandwidth. Note that here, bins have variable sizes as a function of the system load. Minimizing response time is consistent with the bin-packing objective. To adapt FFD to our problem, the main issue is to nd a measure for the size of the objects. The size of an object o could be proportional to its bandwidth requirement (c(o)), its display time (l(o)), or its real size (say in Megabytes) as c(o) l(o). From our previous experiences [6], we choose the size to be c(o) l(o). This is intuitive as we are considering the area of the rectangular representation of the object as opposed to its height or length. This heuristic is termed Largest Object First (LOF). We study an entirely opposite heuristic as well: Smallest Object First (SOF). SOF was found to outperform LOF in some cases. The complexity of each of the heuristics is O(n 2 log n). When the number of all permutations of the objects being referenced n! is smaller than or equal to some threshold N, then the system may choose the exhaustive search. The exhaustive search examines all possible retrieval plan permutations n! to nd the best correct retrieval plan. Therefore, the complexity of the general exhaustive search in worst case is O(n! T Schedule ). On the other hand, if the number of objects n! is greater than the threshold N, then all permutations cannot be exhaustively searched in a reasonable time. Therefore, the heuristic described above may be used to nd a partial search space. If the cardinality of the resulting partial search space js partial j is smaller than or equal to N then the system may exhaustively search the partial search space in a reasonable time. The complexity of exhaustively searching the partial search space is O(n 2 log n + js partial j T Schedule ). Otherwise, a heuristic or a randomized search strategy may be employed to nd a sub-optimal retrieval plan. CONCLUSIONS AND FUTURE RESEARCH In this paper, we formally dened a set of exibilities inherent to FPA applications. We showed that these exibilities can be used to improve the system performance. The exibilities are formally captured by the query script parameters. Due to small number of continuous objects requested by the user, Prime exhaustively searches the

8 8 entire search space to nd the best plan. The denition of the best plan depends on the metrics with their priorities determined by the application. We focused on response time as the primary metric and some other metrics such as latency time as secondary metrics. When the search space is large, we investigated two heuristic approaches (LOF and SOF). To verify our optimizer, we conducted a simulation experiment. The main observations are as follows. First, invoking the optimizer only makes a dierence when the system load is moderate (i.e., system load is 60%? 80% of capacity). Second, response time and latency time are consistent metrics and both have been improved signicantly by our optimizer. Third, the dierence between the best case and the worst case, and the best case and the average case is substantial. For example, if latency time (i.e., time elapsed from when the retrieval plan is submitted until the onset of the display of its rst object) is considered as a metric, the best plan found by Prime observes 41% to 92% improvement as compared with the worst plan, and 26% to 89% improvement as compared with the average plans. Finally, the heuristics perform substantially better than the worst case, however, no substantial improvement was observed when compared to the average case. As part of our future research, we will consider the following extensions. First, in this paper we focused only on intra-presentation optimization, where Prime strives to optimize for a single query script. We intend to study both inter-presentation, and global-presentation optimizations. Second, as part of our future work, we will be considering memory buering techniques for Prime. Moreover, as part of our future research, we will consider the design of Prime for a hierarchical storage system that utilizes the exibilities over a tertiary storage device. Finally, as part of our future research, we will investigate the design of Parrot. For a more elaborate discussion please see [7]. References [1] James F. Allen. Maintaining Knowledge about Temporal Intervals. Communications of the ACM, 26(11):832{843, November [2] S. Berson, S. Ghandeharizadeh, R. Muntz, and X. Ju. Staggered Striping in Multimedia Information Systems. In Proceedings of the ACM SIGMOD International Conference on Management of Data, [3] M. Garey and D. Johnson. Computers and Intractability: A Guide to the Theory of NP- Completeness, pages 236{242. W.H. Freeman and Company, New York, [4] Shahram Ghandeharizadeh, Roger Zimmermann, Weifeng Shi, Reza Rejaie, Doug Ierardi, and Ta-Wei Li. Mitra: A scalable continuous media server. In Multimedia Tools and Applications Journal, January [5] K. Keeton and R. H. Katz. Evaluating Video Layout Strategies for a High-performance Storage Server. ACM Multimedia Systems, 3(2), May [6] Cyrus Shahabi. Scheduling the Retrieval of Continuous Media Objects. PhD thesis, University of Southern California, [7] Cyrus Shahabi, Ali Esmail Dashti, and Shahram Ghandeharizadeh. Retrieval optimization in multimedia database systems. Technical report, University of Southern California, 1997.

9 [8] F.A. Tobagi, J. Pang, R. Baird, and M. Gang. Streaming RAID-A Disk Array Management System for Video Files. In First ACM Conference on Multimedia, August