3. Scheduling Approaches
Task assignment approach
Schedule the tasks in a user submitted process to
suitable nodes for improving performance
Load balancing approach
Tasks are distributed to nodes to balance workload of
the nodes
Load sharing approach
Ensures no node is idle when a process may wait for a
processor
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4. Desirable Features of Good
Scheduling Algorithm
No A priori knowledge about the processes
Dynamic in nature
Quick decision making capability
Balanced system performance and scheduling
overhead
Stability
Scalability
Fault tolerance
Fairness of service
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5. Task Assignment Approach
The Basic Idea
Assumptions (so that tasks can be assigned optimally)
The process is already split into tasks, that are broken at
natural boundaries and data transfers can be minimized
between tasks
Computation required by a task and speed of each processor
are known
Cost of execution of the task at each node is known
IPC costs between pairs of processes are known
Other constraints, such as resource requirements, resources
at each node, precedence among tasks etc are known
Reassignment of tasks is , generally, not possible
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6. Task Assignment Approach
The optimization to be done
Minimize IPC costs
Quick turn around for the process
High degree of parallelism
Efficient utilization of system resources, in general
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7. Load Balancing Approach
Static vs. dynamic
Deterministic vs. probabilistic
Centralized vs. Distributed
Cooperative vs. Non-cooperative
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9. Static vs. Dynamic
Static: Uses average behavior of system, ignores
current state
Simple, no need to process system state information
But does not adjust to current situation
Dynamic: Reacts to system changes dynamically
Responds to system state, avoids unnecessary poor
performance
Provides better performance , but complex as system
state information needs to be collected and processed
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10. Deterministic Vs. Probabilistic
Deterministic processes allocate tasks based on
known properties of nodes and the processes to be
scheduled
A probabilistic algorithm uses information regarding
static attributes of the system such as number of
nodes, processing capability at each node, network
topology and formulate simple placement rules
Deterministic algorithms are difficult to optimize and
costs more to implement
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11. Centralized vs. Distributed
A centralized server node assigns tasks based on state
information collected at that node. All other processor
nodes send information to that node and keep it
updated.
Quite efficient
But, single point of failure is the central node
Solution is to replicate k+1 servers if k faults are to be
serviced. Issue of consistency arises
If a delay of a few seconds are acceptable re-
instantiation can be used
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12. Distributed Scheme
K physically distributed entities
Each is considered a local controller, runs concurrently
and asynchronously with other entities
Entities make decisions based on system wide
objective function
In a fully distributed system all N nodes are the
scheduling entities scheduling local as well as
acceptance of remote processes
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13. Cooperative vs. Non-cooperative
In non-cooperative mode entities make decisions
independent of other nodes
In cooperative mode the entities cooperate to arrive at
a decision
Cooperative algorithms are thus complex and has
higher overheads
Cooperative algorithms have better stability though
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14. Design issues
Load estimation policy
Process transfer policy
State information exchange policy
Location policy
Priority assignment policy
Migration limiting policy
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15. Load estimation Policy
Parameters that are time dependent and node
dependent are
Number of processes on the node at the time of
estimation
Resource demands of these processes
Instruction mixes of these processes
Architecture and speed of the node’s processor
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16. Load Estimation Methods
A simple measure could be number of processes executing
at the node
That may not be accurate as the load really depends on the
remaining service time of these processes, some methods
are
Memoryless method: assumes remaining time is same for all,
irrespective of time used by these processes, load estimate is
thus just the number of processes method
Pastrepeats: remaining service time is equal to the time used
by it so far
Distribution method: if the distribution of services times is
known then the remaining service time is expected
remaining service time conditioned by time already used
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17. Process Transfer Policy
Threshold decides if a process will be transferred
Static policy: predefined threshold, no exchange of state
information is required to decide the threshold
Dynamic policy: the threshold is the product of average
workload of all nodes and a predefined constant Ci. Ci is
proportional to the processing capability of ni relative to
the processing capability of other nodes. State
information needs to be exchanged to keep determining
the load
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18. Process Transfer-Threshold
Single threshold policy: rigid single threshold can lead
to instability as if the local load is just below threshold
and a process is accepted, the accepted process
increases the load beyond the threshold
Double threshold policy: The threshold is a band
creating a high watermark and a low watermark to
decide transfers (overloaded, high mark, normal load,
low mar, under-loaded)
Necessary that the newer processes coming messages
should not affect the local load significantly
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19. Thresholding
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Overloaded
Underloaded
Overloaded
Underloaded
Normal
Threshold
High mark
Low mark
20. State Information Exchange Policy
Periodic Broadcast: lot of communication load, does
not scale well
Broadcast when state changes: further refinement is to
transmit state information only when state changes to
under loaded or overloaded
On-demand exchange: request state when the load
changes to under loaded/ overloaded from normal (
note: only nodes that are not normal need to convey
information)
Exchange by polling: When needed a node can poll
others
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21. Location Policy
Threshold: select nodes at random, check if placing
the process will overload it
Shortest: a pre-decided number of nodes are polled,
process transferred to one with the least load
Bidding: request bids for a process to be migrated,
nodes bid for it. Best bidder, in terms of fastest time,
cheapest processing, best price/performance
Pairing: low load and high load nodes are paired, there
may be requests seeking pairing
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22. Priority Assignment Policy
Selfish: Local processes get higher priority
Altruistic: remote processes get higher priority
Intermediate: Local processes get higher priority if the
number is more than or equal to remote processes
number. Otherwise remote processes are prioritized
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23. Priority Assignment Comparisons
Selfish: worst response time for remote processes,
higher penalty I remote process arrives at highly
loaded node, lower otherwise
Altruistic: best response time, local processes may face
penalty
Intermediate: response time performance is close to
that of altruistic policy yet the local processes do not
suffer as much
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24. Migration Limiting Policy
Uncontrolled: Local and remote processes treated the
same way. migration may happen any number of
times, can cause instability
Controlled: A migration count is used. Many designers
feel migration is expensive and thus it is limited to 1.
Some prefer limiting it to a small value k, determined
statically or dynamically
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25. Load Sharing Approach
Load sharing may be better than load balancing, load
fluctuates and the overhead of exchanging state
information is quite a bit. It might be better to make sure
no nodes idle when any node has more than one process
Priority assignment and migration limiting policies are
same as with load balancing approach
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26. Load Sharing Design issues
Load estimation Policies
Process transfer policies
Location policies
Sender initiated location policies
Receiver ini8tiated location policies
State Information Exchange policies
Broadcast when state changes
Poll when state changes
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27. Load Estimation Policies
Simplest load estimation required to find only if a
node is idle or overloaded by counting number of
processes at a node
Most modern systems have some processes running all
the time at any node. A better estimator may be the
CPU utilization
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28. Process Transfer Policies
All or nothing strategy
Fix threshold at 1
A node will accept a process if it has no processes, an
will try to send away a process when count is more
than one
If CPU utilization is used, a high-low policy will need
to be used
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29. Location Policies
Location policies is one of the two following types
Sender initiated
Receiver initiated
Depending on who initiates the search for a node that
can accept the process
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30. Location-Sender Initiated
When a node exceeds the threshold, it start
broadcasting its status to find a node or starts random
probes to other nodes to find a free node
A probe limit is specified to stop the nodes from
searching a large number of nodes and wasting time
Scalability is better with limited probing
Some analyses indicate the probe limit does not have
much of an effect
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31. Location-Receiver Initiated
A node load falls below threshold, it’ll start
broadcast/probing to find a node that can provide a
process
A node can transfer a process only if doing that would
not cause the load to go below the threshold
With broadcast, the receiver can find one candidate
from among the replies
In random probing the probing continues until a
candidate is found or the probing limit is reached
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32. Observations on Location Policies
Either location policies offer advantage over no location of
processes
Sender initiated policies are preferred when the system is
lightly or moderately loaded
In a lightly loaded system, receiver generates probe
messages would be many
Receiver initiated policies are preferable on high load
systems only if process transfer costs are comparable. As
sender probes increase when the system is already highly
loaded
If transfer costs in receiver initiated policies are
significantly higher then, sender initiated policies provide
better performance
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33. State Information Exchange
Policies
No need to periodically exchange information
State information is needed only when a node is
under-loaded or overloaded
Information is exchange only when state changes
Policies used
Broadcast when state changes
Poll when state changes
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34. State Info-broadcast when State
Changes
When under-loaded or overloaded a node broadcasts a
request for status information
In sender initiated policy, the broadcast happens when
the node is underloaded
In receiver initiated policy the broadcast would be
initiated when a node is changes state to underloaded
When threshold is 1, the policy is called broadcast
when idle
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35. State Info-Poll when State
Changes
When a node falls below the threshold, it initiates a
random polling cycle
The cycle ends when a node is found that can transfer
a process to the receiver
Or when a probing limit is reached
When a fixed threshold of 1 is used, this is known as
the poll when idle policy
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