Back Room Front Room 2

More documents

Recommendations

Info

ACME-DB: AN ADAPTIVE CACHING MECHANISM USING MULTIPLE EXPERTS FOR DATABASE BUFFERS Faizal Riaz-ud-Din, Markus Kirchberg Information Science Research Centre, Department of Information Systems, Massey University, Private Bag 11-222, Palmerston North, New Zealand. {F.Riaz-ud-din, M.Kirchberg}@massey.ac.nz Keywords: Database caching, adaptive caching, buffer management Abstract: An adaptive caching algorithm, known as Adaptive Caching with Multiple Experts (ACME), has recently been presented in the field of web-caching. We explore the migration of ACME to the database caching environment. By integrating recently proposed database replacement policies into ACME’s existing policy pool, an attempt is made to gauge ACME’s ability to utilise newer methods of database caching. The results suggest that ACME is indeed well-suited to the database environment and performs as well as the best currently caching policy within its policy pool at any particular moment in its request stream. Although execution time increases by integrating more policies into ACME, the overall processing time improves drastically with erratic patterns of access, when compared to static policies. 1 INTRODUCTION One of the key factors affecting a database’s performance is its ability to effectively and efficiently cache frequently requested data. Main memory access is approximately 170,000 times faster than disk accesses (Ramakrishnan and Gehrke, 2000). The challenge in main memory caching is to determine which page of data to replace (the ‘victim’) from the buffer pool once it is full, to make space for future data pages to be read in from secondary storage. The selection of victims is performed by a cache replacement algorithm, which chooses victims according to certain criteria based on past access patterns. This paper attempts to trial and evaluate a recently presented adaptive cache replacement algorithm, known as ACME, or Adaptive Caching with Multiple Experts (Ari et al., 2002). ACME uses machine learning to dynamically select the cache victim from a number of policies, or experts, during the cache replacement process. Whilst ACME was originally presented for the web-caching environment, this paper describes the evaluation of the adaptation of ACME to the database I. Seruca et al. (eds.), Enterprise Information Systems VI, © 2006 S pringer. Printed in the Netherlands. 72 72–81. environment. The rigidity and robustness of ACME is also tested by its ability to integrate more complex policies in its policy pool, including optimizations achieved in terms of overall processing time. The remainder of this paper is presented as follows: Section 2 presents related work, including adaptive caching algorithms, with a focus on ACME. Sections 3 and 4 respectively, detail the way in which ACME has been adapted to the database environment and the issues concerned with implementation. Section 5 analyses the results obtained from driving the database-adapted ACME (referred to as ACME-DB in this paper) with live and synthetic traces. Finally, section 6 presents conclusions. 2 RELATED WORK The main task of a buffer manager is to retrieve the data from secondary storage into main memory, thus allowing the data in the main memory to be accessed by the transactions that request information from the database. There are two purposes for accessing data in this manner: firstly it ensures that subsequent
ACME-DB: AN ADAPTIVE CACHING MECHANISM USING MULTIPLE EXPERTS 73 accesses to the same data are much faster in future and do not need to be accessed from secondary storage again), and secondly, it ensures that the data is presented to the database in a synchronous manner, resulting in data consistency. Any data in the main memory that has been changed by a transaction is written back to where it was retrieved from on the secondary storage device. This process of using the main memory area as an efficient data delivery mechanism is known as caching, whilst the main memory cache is also known as the buffer pool. A cache replacement algorithm is often referred to as a buffer replacement policy, and its function is to select a victim from the buffer pool. Ideally, secondary storage devices would have I/O speeds at least as fast as main memory. However, because there is a latency issue involved with reading from secondary storage (Sacco and Schkolnick, 1986), and main memory is far more costly than secondary storage, the need to find an optimal practical buffer replacement policy still exists. A number of buffer replacement policies have been presented in the literature in the quest for that optimal replacement policy. The more well-known amongst them have been FIFO (First-In-First-Out), LIFO (Last-In-First-Out), MRU (Most Recently Used), LRU (Least Recently Used), and LFU (Least Frequently Used), as well as others (Effelsberg and Haerder, 1984). More recently, LRFU (Least Recently / Frequently Used), has been presented, which is a policy that attempts to combine the benefits of LRU and LFU (Lee et al., 1999). Whilst LRU-K (O’Neil et al., 1993) has succeeded in achieving a higher hitrate than LRU, implementation is more difficult and execution time is higher. Even more recently, policies such as LIRS (Low Inter-reference Recency Set) (Jiang and Zhang, 2002), 2Q (Johnson and Shasha, 1994), and W 2 references (since they are now in the main memory R (Weighing <strong>Room</strong> / Waiting <strong>Room</strong>) (Jeon and Noh, 1998) have been presented. Whilst these recent policies suggest a marked improvement over their predecessors, implementation is not as simple as for LRU. Adaptive caching algorithms go one step further by using two or more policies, in the attempt to reap their benefits, whilst avoiding their pitfalls (Castro et al., 1997). Hybrid Adaptive Caching (HAC) is an adaptive caching algorithm presented by Castro et al. (Castro et al., 1997) within the environment of distributed objects. It combines the benefits of both page and object caching, whilst at the same avoiding their respective disadvantages. It behaves like a page caching system when spatial locality is good, and when spatial locality degrades, it behaves like an object caching system, thereby adapting to the different access patterns in the request stream. Adaptive Caching with Multiple Experts (ACME) is another adaptive caching algorithm presented recently by Ari et al. (Ari et al., 2002) within the context of the web-caching environment. ACME makes use of a policy pool that consists of a number of different static replacement policies (‘experts’). Each of these experts has its own cache (known as a Virtual Cache). The request stream is processed by each of the experts and each performs its own caching independent of the others. When the actual cache that holds the data (the Real or Physical Cache) is full, and a page needs to be replaced, the experts are queried to see if they would have held the requested page in their virtual caches. If so, they are awarded a vote, otherwise they are penalised. The votes that each expert gains over time are used to determine its probability in choosing the next replacement victim. ACME also possesses a machine-learning algorithm and it ‘learns’ to use the current best policy over time, based on the past successes and failures of the individual experts in the policy pool (Ari, 2002). The architecture of ACME is illustrated in Figure 1 below for clarity. Figure 1: ACME architecture (Ari, 2002) ACME is used as the basis of this research because it readily allows the incorporation of other policies within its architecture. Not only does this mean that ACME has the ability to use many different criteria when selecting a victim, but importantly, it provides the flexibility required to adapt it to the database environment (Ari et al., 2002). However, it should be noted that adding more policies increases the memory usage, and increases processing time. For detailed information on ACME, please refer to (Ari et al., 2002).
Page 1 and 2:
www.dbeBooks.com - An Ebook Library
Page 3 and 4:
A C.I.P. Catalogue record for this
Page 5 and 6:
vi TABLE OF CONTENTS PART 1 - DATAB
Page 7 and 8:
viii TABLE OF CONTENTS A WIRELESS A
Page 9 and 10:
CONFERENCE COMMITTEE Honorary Presi
Page 11 and 12:
Hung, P. (AUSTRALIA) Jahankhani, H.
Page 13 and 14:
Invited Papers
Page 15 and 16:
2 ENTERPRISE INFORMATION SYSTEMS VI
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
10 ENTERPRISE INFORMATION SYSTEMS V
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34: 20 ENTERPRISE INFORMATION SYSTEMS V
Page 37 and 38: EVOLUTIONARY PROJECT MANAGEMENT: MU
Page 43 and 44: MANAGING COMPLEXITY OF ENTERPRISE I
Page 67 and 68: ASSESSING EFFORT PREDICTION MODELS
Page 75 and 76: ORGANIZATIONAL AND TECHNOLOGICAL CR
Page 79 and 80: ASAP Processes W Project Kickoff A
Page 83: since it means changing the relevan
Page 87 and 88: ACME-DB: AN ADAPTIVE CACHING MECHAN
Page 89 and 90: Hit Rate (%) ACME-DB: AN ADAPTIVE C
Page 91 and 92: 5.3.6 Effect on all Policies by Int
Page 93 and 94: ACKNOWLEDGEMENTS We would like to t
Page 95 and 96: This paper describes the data sampl
Page 97 and 98: The major limit A of the operation
Page 99 and 100: RELATIONAL SAMPLING FOR DATA QUALIT
Page 101 and 102: TOWARDS DESIGN RATIONALES OF SOFTWA
Page 103 and 104: ((Brown et al., 1998), pp. 159-190,
Page 105 and 106: sive data filtering, presentation (
Page 107 and 108: • implementation changes in servi
Page 109 and 110: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 111 and 112: Data Rate [bytes/sec] 1600000 14000
Page 113 and 114: E.g., DV Camcorder Camera Packets (
Page 115 and 116: Procedure CheckOptimal (n rs 1 ...n
Page 117 and 118: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 119 and 120: PART 2 Artificial Intelligence and
Page 121 and 122: 110 ENTERPRISE INFORMATION SYSTEMS
Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
Page 135 and 136:
124 ENTERPRISE INFORMATION SYSTEMS
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
AN EXPERIENCE IN MANAGEMENT OF IMPR
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
ANALYSIS AND RE-ENGINEERING OF WEB
Page 181 and 182:
Module p0 Customer Itinerary Send I
Page 183 and 184:
departments: the marketing dept. se
Page 185 and 186:
• An acyclic module M is usable,
Page 187 and 188:
BALANCING STAKEHOLDER’S PREFERENC
Page 189 and 190:
The scenarios will provide an abstr
Page 191 and 192:
BALANCING STAKEHOLDER'S PREFERENCES
Page 193 and 194:
BALANCING STAKEHOLDER'S PREFERENCES
Page 195 and 196:
A POLYMORPHIC CONTEXT FRAME TO SUPP
Page 197 and 198:
A POLYMORPHIC CONTE XT FRAME TO SUP
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
FEATURE MATCHING IN MODEL-BASED SOF
Page 205 and 206:
combination of solution domains - a
Page 207 and 208:
• features for all the concepts:
Page 209 and 210:
Existence In both steps it is possi
Page 211 and 212:
matching strategies are maximal, mi
Page 213 and 214:
TOWARDS A META MODEL FOR DESCRIBING
Page 215 and 216:
elementary message (ae-message) can
Page 217 and 218:
problems on a pragmatic level, whic
Page 219 and 220:
TOWARDS A META MODEL FOR DESCRIBING
Page 221 and 222:
INTRUSION DETECTION SYSTEMS USING A
Page 223 and 224:
INTRUSION DETECTION SYSTEMS USING A
Page 225 and 226:
(k� 1) (k) (k�1) (k) p � w
Page 227 and 228:
Table 5: Performance Comparison of
Page 229 and 230:
A USER-CENTERED METHODOLOGY TO GENE
Page 231 and 232:
carries out the second phase of the
Page 233 and 234:
1 1 1 1 2 PROCESS STORE ENTITY EDGE
Page 235 and 236:
constraints rules. KOGGE (Ebert et
Page 237 and 238:
PART 4 Software Agents and Internet
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
Page 285 and 286:
CABA 2 L A BLISS PREDICTIVE COMPOSI
Page 287 and 288:
y disables evidencing severe mental
Page 289 and 290:
Figure 4: Symbols emission in DAR-H
Page 291 and 292:
they evidence a generalization erro
Page 293 and 294:
A METHODOLOGY FOR INTERFACE DESIGN
Page 295 and 296:
and mobility loss coupled with redu
Page 297 and 298:
Tradeoff: The default input is poss
Page 299 and 300:
Sessions on the VABS which are held
Page 301 and 302:
A CONTACT RECOMMENDER SYSTEM FOR A
Page 303 and 304:
A CONTACT RECOMMENDER SYSTEM FOR A
Page 305 and 306:
- "Going to the sources". The user
Page 307 and 308:
Here are the matrix W and P for our
Page 309 and 310:
EMOTION SYNTHESIS IN VIRTUAL ENVIRO
Page 311 and 312:
theme turns out to be surprisingly
Page 313 and 314:
6 FROM FEATURES TO SYMBOLS 6.1 Face
Page 315 and 316:
sions are described by different em
Page 317 and 318:
Electronic Imaging 2000 Conference
Page 319 and 320:
to the accessibility problem for th
Page 321 and 322:
Figure 3: Hierarchical View of the
Page 323 and 324:
4 CONCLUSIONS AND FUTURE WORK On th
Page 325 and 326:
PERSONALISED RESOURCE DISCOVERY SEA
Page 327 and 328:
Page 329 and 330:
profile, the user defines his curre
Page 331 and 332:
Page 333 and 334:
Abdelkafi, N. .....................
show all

Back Room Front Room 2

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?