Data availability in a mobile environment Daniel Cutting University of Sydney & Smart Internet Technology CRC

Talk outline Introduction objective, motivation, approach literature review  Distributed operating systems & file systems, distributed applications, context. initial model papers, future plans.

Introduction Earlier distributed systems used fixed machines and networks portables led to ‘offline’ operation mobile devices led to ad hoc networks and weak connectivity  Want to run applications across them  need to share data.

Objective Hypothesis: sharing of data between mobile devices can be improved by using context  Maximise availability of data to applications  minimise battery usage and network traffic  constrained by codified semantics and user policies  use relevant contextual information to aid sharing. identify context appropriate to each situation find heuristics for representing all situations.

Personal Persistent (PP)

Joint Transient (JT)

Communal Persistent (CP)

Approach Build data sharing model for experiments test various data distribution policies run simulations, but maybe also a prototype build simple applications  Test general data availability.

Distributed operating systems Abstraction: thread/storage not processor/network Amoeba: server/terminal, processor pool Sprite: distributed over terminals distributed virtual machines: cJVM, Jupiter, …  MagnetOS: distributes objects around sensor network  works for some applications in some environments. generally brittle for mobile environments.

Distributed file systems Abstraction: file/directory (open/read/write/close) Andrew: client/server, fully connected Coda: Andrew + disconnected mode Odyssey: ‘application-aware adaptation’ DFS is OK when network is stable, not so good when transient.

Distributed applications Why not distribute at application level? application components + communication  RPC/RMI, sockets, … mobile devices weakly connected, so want decoupled communication  Middleware.

Publish-subscribe systems Clients receive events matching subscriptions anonymous, decoupled cannot ‘store’ data Elvin  Federation, quenching.

Tuple spaces Linda  Tuple:, tuple space contains tuples  OUT(t), IN(t), RD(t). anonymous, decoupled, can store data  but no notifications LIME: Linda in a Mobile Environment  Merging/separation of tuple spaces.

Context Bottom-up (sensors, aggregated, inferred) top-down (user preferences, input) use of context often ad hoc, hard to reuse so, formalise  CSCP: structured, interchangeable, (de)composable, uniform, extensible, standardised  Context Toolkit: GUI-like widgets + generators, interpreters, servers.

Model Design data sharing model around middleware generalise for more types of apps: “Middies”  Members, spaces, blocks, reactors, matchers. distribute blocks according to a policy  Full, server, random, context-aware. context:  Device: battery, storage  application/user: access patterns, directives.

Papers “Middies: Passive Middleware Abstractions for Pervasive Computing Environment”. With Adam Hudson and Aaron Quigley. Submitted to ICPS “BlueStar: Beacon + MPC based location detection”. With Belinda Ward, Aaron Quigley, Chris Ottrey, Bob Kummerfeld. To appear at IEEE PLANS 2004 “AR phone: Accessible Augmented Reality in the Intelligent Environment”. With Adam Hudson, Mark Assad and David Carmichael. Presented at OZCHI 2003.

Future plans April deeper context study completed May completed model design and confirmed hypothesis October completed construction of model November journal paper February begin experiments March submit thesis.

Comments and questions Daniel Cutting University of Sydney

Mobile computing Satyanarayanan on mobile computing:  Devices are resource-constrained  device connectivity is variable and unreliable  devices need to run on batteries. service discovery for resource allocation  BASE, SLP? DMUTEX, replication  Cache consistency: primary copy, …

Toolkits Rover  Relocatable Dynamic Objects (RDO), Queued RPC (QRPC) Bayou  Epidemic replication: read-any/write-any. one.world: build entire applications  Tuple space for data: two-tier replication  components for application.