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Adaptive approach in handling human inactivity in computer power management
Ria Candrawati1, Nor Laily Hashim2.
—Human inactivity is handled by adapting the
behavioral changes of the users. Human inactivity refers to as
unpredictable workload of a complex system that is caused by
increments of amount in power consumption and it can be
handled automatically without the need to set a fixed time for
changing the computer state. This is happens due to lack of
knowledge in a software system and the software self-adaptation
is one approach in dealing with this source of uncertainty. This
paper observes human inactivity and Power management policy
through the application of reinforcement learning approach in
the computer usage and finds that computer power usage can be
reduced if the idle period can be intelligently sensed from the
user activities. This study introduces Control, Learn and
Knowledge model that adapts the Monitor, Analyze, Planning,
Execute control loop integrates with Q Learning algorithm to
learn human inactivity period to minimize the computer power
consumption. An experiment to evaluate this model was
conducted using three case studies with same activities. The
result show that the proposed model obtained those 5 out of 12
activities shows the power decreasing compared to others.
Affiliation:
- Universiti Utara Malaysia, Malaysia
- Universiti Utara Malaysia, Malaysia
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Indexation |
Indexed by |
MyJurnal (2019) |
H-Index
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0 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus (SCImago Journal Rankings 2016) |
Impact Factor
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0 |
Rank |
Q4 (Computer Networks and Communications) Q4 (Electrical and Electronic Engineering) Q4 (Hardware and Architecture) |
Additional Information |
0.112 (SJR) |
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