pervasive compu

1. 1
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (34)
Drilling Down - The Difficult Problems (11)
 4.3) Adaptation Strategy
 Adaptation is necessary when there is a significant
mismatch between the supply and demand of a resource
 Examples of resources: wireless network bandwidth, energy,
computing cycles, memory, …
 Three alternative adaptation strategies for adaptation in
PERV
 Recall: UPCS is a client of a smart space (3 interacting entities)
 UPCS = user’s personal computing space
 Strategy 1 (reservation): Client can ask the environment for
guarantee of a certain level of a resource
 Typically used by reservation-based QoS systems [22]
 QoS – quality of service
 This effectively increases the supply of a scarce resource to
meet the client’s demand
[LTL: Client able to reserve is privileged in some sense]
 Strategy 2 (reduction): Client can guide applications in changing
their behavior so that they use less of a scarce resource
 This change usually reduces the user-perceived quality, or
fidelity, of an application
 E.g., Odyssey [11, 24] uses this strategy

2. 2
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (35)
Drilling Down - The Difficult Problems (12)
 4.3. Adaptation Strategy –cont.
 Three alternative strategies for adaptation in PERV – cont.
 Strategy 3 (correction): Client can suggest a corrective action to
its user
 If the user complies with it, resource supply will likely (but
without certainty) become adequate to meet demand
 E.g.., in Scenario 1, Aura advised Jane to walk to Gate 15 in
order to obtain adequate wireless bandwidth.
 Conceptually promising, but not implemented by any real
system yet (2001)
 All three strategies are important in pervasive computing
 Some smart spaces capable of accepting resource reservations
(Strategy 1 - reservation)
 In some smart spaces [some] clients can’t reserve resources —
must ask applications to reduce their fidelities (Strategy 2 -
reduction)
 Happens when space is uncooperative or resource-impoverished
 Corrective actions may be particularly useful when reduction of
fidelity is unacceptable (Strategy 3 - correction)
 Broaden the range of possibilities for adaptation by involving the
user

3. 3
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (36)
Drilling Down - The Difficult Problems (13)
 Adaptation Strategy – some research questions
 How does a client choose the best adaptation strategy?
 What factors should a good decision procedure take into account?
How should different factors be weighted? What role, if any,
should the user play in making this decision? How can smooth and
seamless transitions between strategies be ensured as a user
moves between different spaces?
 Reservation strategy might appear superior for a user
 User neither required to accept lower fidelity nor perform a
corrective action
Is this true in all circumstances?
 What are the hidden costs and ‘‘gotchas,’’ if any, in a widely-
deployed system using Strategy 2?
 How will the implementation of a smart space honor
resource reservations?
 What are the most appropriate admission control policies when
there are competing requests from multiple clients? What
resources, beside wireless network bandwidth, is it meaningful
and useful for a smart space to reserve? What are the APIs and
protocols necessary to negotiate these reservations?

4. 4
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (37)
Drilling Down - The Difficult Problems (14)
 Adaptation Strategy – some research questions – cont.
 Issues for corrective strategy
 Is corrective adaptation practically feasible? Do users find it
intrusive or annoying? What is the best way to communicate
potential corrective actions to users? What are the programming
models and APIs necessary to support corrective actions?
 Legacy problem: Can existing applications use this approach? If
so, how substantial are the modifications to them?
 Issues for reduction strategy
 What are the different ways in which fidelity can be lowered for a
broad range of applications? Are existing APIs, such as that of
Odyssey [24], adequate?
 How should those APIs and programming models be revised based
on extensive usage experience?
 What is the negative impact of lowered fidelity on users and how
can this be minimized?

5. 5
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (38)
Drilling Down - The Difficult Problems (15)
 4.4) High-level Energy Management
 Sophisticated software capabilities increase the energy
demand of an MC (= mobile computer)
 Examples of sophisticated capabilities: as proactivity / self-tuning
 At the same time: pressure to make MCs lighter and more
compact places severe restrictions on battery capacity
 Growing consensus:
 Advances in battery technology and low-power circuit design
cannot, by themselves, reconcile these opposing constraints
 The higher levels of the system must also be involved [10, 25]
 How does one involve the higher levels of a system in
energy management?
 Example 1: Energy-aware memory management [18]
 OS dynamically controls the amount of physical memory that
has to be refreshed
 Example 2: Energy-aware adaptation [11]
 Under OS control, individual applications switch to modes of
operation with lower fidelity and energy demand

6. 6
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (39)
Drilling Down - The Difficult Problems (16)
 High-level Energy Management – some research questions
 In what ways can the higher levels of a system contribute to
managing energy?
 What are the relative strengths and weaknesses of these approaches?
When should one method be used in preference to another?
 How does high-level energy management impact the goal of
invisibility in pervasive computing?
 How intrusive or distracting to users are such techniques?
 Can knowledge of user intent be exploited in energy management?
 If so, how robust is this approach in the face of imperfection in this
knowledge?
 Can smart spaces and surrogates be used to reduce energy demand
on a mobile computer?
 What are possible approaches, and what are their relative merits?
 What is the role of remote execution in extending battery life?
 Under what circumstances does its energy savings exceed the energy
cost of wireless communication? Can a system predict these savings and
costs accurately enough in practice to make a significant difference?

7. 7
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (40)
Drilling Down - The Difficult Problems (17)
 4.5) Client Thickness
 Types of clients
 Thick client = a powerful client
 Thin client = a minimal client
 Thick clients have many negative features from the user’s viewpoint
 Larger / heavier / with bigger & heavier battery / dissipate more
heat
 Over time, physical size and weight of a thick client are reduced
 Due to improvements in VLSI and packaging technology
 The same improvements shrinking thick clients produce
even smaller and lighter thin clients
 Q: How powerful MC is needed for a pervasive computing
environment?
 How much CPU power, memory, disk capacity, … does it need?
 The answer determines many of the key constraints imposed
on the hardware design of the client
 A: For a user, a client can never be too powerful, too
small, too light or have too long battery life!

8. 8
© 2007 by Leszek T. Lilien
Based on: M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, 2001
Pervasive Computing vs. Distributed Systems & Mobile Computing (41)
Drilling Down - The Difficult Problems (18)
 4.5. Client Thickness – cont.1
 A wide range of client designs
 One extreme: Ultra-thin clients
 Bare-bones devices
 E.g., Infopad [6, 40], SLIM [33]
 Can’t operate in isolation
 Little more than high-resolution displays connected through
high-bandwidth wireless links to nearby compute servers
 In-between: Midsize clients
 Handheld computers
 E.g., PalmPilot
 Can operate in isolation
 But run a limited range of applications
 Other apps run on compute servers of smart spaces
 The other extreme: Full-function clients
 Wearable computers and laptops
 E.g., the Navigator family of wearable computers [34], laptops
running as clients of the Coda File System [17]
 Capable of standalone or disconnected operation
 Make use of wireless connectivity when available
 Not critically dependent on it