Overview of Mobile Database Caching

Presented By:Rooma Rathore

Rohini Prinja

Overview

Motivation Problem definition Caching Overview Caching Granularity Cache Coherence Cache Replacement Summary Future Work

Motivation

Popularity of mobile database applications is increasing

Caching can improve performance Caching in mobile DB is different Many papers have addressed

different issues in mobile caching There are not many survey papers

Problem Definition

Given Applications running on mobile databases

Find Efficient caching mechanisms to improve the

response time Objectives

Cache is consistent at all the times Cache-hit ratio is high

Constraints Low bandwidth Frequent Disconnections Limited battery power

Basic Setup of Mobile Environment

MSS MSS MSS MSS

LAN, WAN etc

Database Servers

Cells

MH1 MH2 MH3 MH4 MH5 MH6

How mobile caching is different?

Wireless transmission is much slower Very important to minimize transmission

Frequent disconnections by client Server might not be able to reach all clients for

updates Clients have limited battery power

Energy efficiency is important Uplink speed is much slower

Important to reduce uplink queries from clients

Caching Overview

Caching mechanism characterized by Caching Granularity

What to cache? Cache Coherence

How to maintain consistency? Cache Replacement

Which items to discard when new ones need to be inserted ?

Caching Granularity

Page based caching mechanisms not suitable in mobile environment

Object Caching Server sends all attributes of object, which are

cache by client Attribute Caching

Only attributes requested in query are sent and cached

Hybrid Caching Cache the attributes likely to be used in future

Semantic Caching

Caching Granularity - Comparison

Object Attribute Hybrid Semantic Requires more cache space But good if all attributes are used

Saves cache space Not very good if some attributes used in one query and other in second.

Best of object and attributeHard to identify, which attributes will be used

Semantic information is to be saved New queries with partial cached data can be answered while disconnectedVery good in continuous queries, where just the remaining part can be sent to server.

Cache Coherence 1/3

Broadly classified into 3 categories Stateful Server

Server knows status and contents of all clients

Client Verification Client verifies before use of cache

Stateless Server Server doesn’t know about client’s

status or its cache contents Mostly used in mobile environment

Cache Coherence 2/3

Different kinds of schemes Timestamp based

Broadcasting Timestamps Amnesic Terminals Signature

Adaptive Vary transmission speed Adaptive Invalidation with Fixed Window Adaptive Invalidation with Adjusting Window

Cache Coherence 3/3

Asynchronous Stateful A Stateful scheme Based on HLC at MSS

Validity Scoped Suitable for Location dependent data Bit Vector with Compression (BVC) Grouped Bit Vector (GBVC) Implicit Scope Information (ISI)

Cache Coherence- Comparison

Timestamp Adaptive Asynchronous

Validity - based

o Periodic broadcasto IR can become too longo Long query delayo Simple

o Adapt to load and other parameterso Complexo Difficult to identify threshold

o A stateful schemeo Additional overhead of HLC on MSSo Invalidation messages sent to individual clients so bandwidth utilization is low.

o Only suitable in location dependent data environmento Doesn’t deal with data updates on servero No periodic broadcast needed

Cache Replacement

Tradition Schemes like LRU, LRU-K, MRU are not very suitable

Access Frequency based Mean – mean inter operation duration Window – highest mean in window EWMA – recent use has higher weight

Gain based SAIU – Stretch Access rate Inverse Update

Frequency MinSAUD – Minimum Stretch integrated with

Access rates Update frequency and cache validation Delay

Target Driven

Cache Replacement - Comparison

Access Freq. based

Gain Based Target driven

Uses past access frequency to predict futureoNot very good if access behavior suddenly changes

Tries to minimize stretch. Computation is complex and consumes battery power Assumes , access rate and update frequency of item is known or can be predicted correctly

Provides generalized cost function Optimal results Function can be modified to optimize different parameters Doesn’t address what to do when more than one parameter needs to be optimized. Computation is complex

Summary

Why mobile caching is important? How mobile caching is different? Discussed papers in these areas

Caching granularity Cache coherence Cache replacement

Future Work

More research is needed in area of cache replacement

Study can be done to find which combination of techniques is suitable for what applications

Study to determine which algorithms are currently being used in commercial applications.

References 1/3

[1] Kahol, S. Khurana, S.K.S. Gupta and P.K. Srimani, “A strategy to managecache consistency in a distributed mobile wireless environment”, IEEE Trans.on Parallel and Distributed System, 12(7), pp 686700, 2001

[2] Hong V. Leong, Antonio Si “On Adaptive Caching in Mobile Databases” April1997, Proceedings of the 1997 ACM symposium on Applied computing

[3] Alok Madhukar, Reda Alhajj “An Adaptive Energy Efficient Cache InvalidationScheme for Mobile Databases” April 2006, Proceedings of the 2006 ACMsymposium on Applied computing SAC '06

[4] Ken. C.K. Lee, H.V. Leong, Antonio Si “Semantic Query Caching in MobileEnvironments”, June 2005, Proceedings of the 4th ACM international workshopon Data engineering for wireless and mobile access

[5] Chi-Yin Chow, Hong Va Leong, Alvin T. S. Chan “Distributed Group-basedCooperative Caching in a Mobile Broadcast Environment” May 2005,Proceedings of the 6th international conference on Mobile data managementMDM '05

[6] Boris Y. L. Chan, Antonio Si, Hong Va Leong . “Cache Management for MobileDatabases: Design and Evaluation.” 1998, Proceedings of the FourteenthInternational Conference on Data Engineering

[7] Jianliang Xu; Qinglong Hu; Wang-Chien Lee; Dik Lun Lee; “Performanceevaluation of an optimal cache replacement policy for wireless datadissemination” 2004, IEEE Transactions on Knowledge and Data Engineering

[8] Quen Ren, Margret Dunham, “Semantic Caching in Mobile computing” (2001)

References 2/3[9] Baihua Zheng, Dik Lun Lee, “Semantic Caching in Location-Dependent Query

Processing”, Lecture Notes in Computer Science (2001)[10] D. Barbara, “Mobile Computing and Databases—A Survey”, IEEE Trans.

Knowledge and Data Eng., vol. 11, no. 1, Jan./Feb. 1999.[11] Kristian Kvilekval, Ambuj Singh, “SPREE: Object Prefetching for Mobile

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mobile computing” , Proc. ACM SIGMOD Int’l Conf. Management of Data,[13] A. Kahol, S. Khurana, S. K. S. Gupta, P. K. Srimani, “An Efficient Cache

Maintenance Scheme for Mobile Environment” ,(2000) InternationalConference on Distributed Computing Systems

[14] G. Cao, “A Scalable Low-Latency Cache Invalidation Strategy for MobileEnvironments,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 5, Sep 03.

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[16] S. Acharya, M. Franklin, and S. Zdonik, “Dissemination-Based Data DeliveryUsing Broadcast Disks,” Personal Comm., vol. 2, no. 6, Dec. 1995.

References 3/3[17] J. Xu, X. Tang, D. L. Lee ,“ Performance Analysis of Location-dependent

Cache Invalidation Schemes for Mobile Environments”, 2002.[18] H. Song; G. Cao, “Cache-miss-initiated prefetch in mobile environments”, IEEE

International Conference on Mobile Data Management, 2004 Page(s):370 – 381[19] K.-L. Wu, P. S. Yu, and M.-S. Chen. “Energy-efficient caching for wireless

mobile computing”. In 20th International Conference on Data Engineering,pages 336-345, Feb. 26-March 1 1996.

[20] Xu, Q.L. Hu, and D.L. Lee, W.-C. Lee, “SAIU: An Efficient Cache ReplacementPolicy for Wireless On-Demand Broadcasts”, Proc. Ninth ACM Int'l Conf.Information and Knowledge Management, pp. 46-53, Nov. 2000

[21] Chun-Hung Yuen, J.; Chan, E.; Lam, K.-Y.; Leung, H.W. “Database andExpert Systems Applications”, 2000. Proceedings. 11th International Workshopon 4-8 Sept. 2000 Page(s):155 - 159

[22] J. Jing, A. K. Elmargarmid, S. Helal, and R. Alonso. “Bit-Sequences: AnAdaptive Cache Invalidation Method in Mobile Client/Server Environments”.ACM/Baltzer Mobile Networks and Applications, 2(2), 1997

[23] Q.L. Hu and D. L. Lee. “Adaptive cache invalidation methods in mobileenvironments”. In Proceedings of the 6th IEEE International Symposium onHigh Performance Distributed Computing, August 1997.

[24] L. Yin, G. Cao, and Y. Cai. “Target-Driven Cache Replacement for MobileEnvironments”, Journal of Parallel and Distributed Computing, Vol. 65, pp.583-594, 2005

Questions

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