Allocation of processors to processes in Distributed Systems. Strategies or algorithms for processor allocation. Design and Implementation Issues of Strategies.

1. PROCESSOR ALLOCATION
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Distributed
Systems

2. WHAT YOU WILL LEARN?
Why Distributed Systems need processor allocation
How performance of Distributed Systems can be
enhanced by using different Processor allocation
strategies
What are the issues that we face while designing a
processor allocation strategy
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3. MOTIVATION
• We are talking about distributed systems, hence multiple
connected machines
• A good algorithm is always appreciated
• Speeds up Computation
• Proper use of resources
• Minimizing CPU Idle time
• Concept of using idle workstations is a weak attempt at
recapturing the wasted cycles
• Using a single 1000-MIPS CPU may be much more expensive than
100 10-MIPS CPU, then the Price/Performance ratio of the latter is
much better. (It may also not be possible to build a much higher
performance CPU)
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Highest Performance
system has:
3,120,000 cores at
2.2 GHz
54,902.4 TFLOPS/s

4. ALLOCATION MODELS
• Before talking about allocating processor, we make
assumptions about the allocation models:
• All machines are identical or at least code compatible
• They differ at most by speed (MIPS or FLOPS)
• Homogeneity (architecture)
• The system is fully connected (doesn’t always mean a wire
to each system; just that transport connections can be
established)
• New work is generated when a process decides to fork or
otherwise create a sub-process
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5. PROCESSOR ALLOCATION STRATEGIES
• NONMIGRATORY
• A process when created is assigned a machine where it
stays until it terminates. It doesn’t matter how overloaded
the machine becomes or how many other machines are
idle.
• MIGRATORY
• In contrast, a process can be moved even after execution
hence allowing better load balancing.
• Although these provide better load balancing, they have a
major impact on system design
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6. AN EXAMPLE OF PROCESSOR ALLOCATION
TO GIVE AN IDEA OF THE NEED
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Mean
Response Time
Processor1 <- A
Processor2 <- B
=(10+8)/2 = 9 sec
Processor1 <- B
Processor2 <- A
=(30+6)/2 = 18 sec
Q. Which allocation is better?

7. AN EXAMPLE OF PROCESSOR ALLOCATION
TO GIVE AN IDEA OF THE NEED
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Mean
Response Time
Processor1 <- A
Processor2 <- B
=(10+8)/2 = 9 sec
Processor1 <- B
Processor2 <- A
=(30+6)/2 = 18 sec
Q. Which allocation is better?

8. ISSUES IN PROCESSOR ALLOCATION
• Design Issues
• Deterministic vs Heuristic Algorithms
• Centralized vs Distributed Algorithms
• Optimal vs Sub-optimal Algorithms
• Local vs Global Algorithms
• Sender-initiated vs Receiver-initiated Algorithms
• Implementation Issues
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9. DETERMINISTIC VS HEURISTIC
ALGORITHMS
• Deterministic
• All information regarding processes is known (for example:
computing requirements, file requirements, communication
requirements, etc.)
• Total information is not always available but approximations
can be done. For example: In Banking, Insurance, Airline
Reservation, today’s work is just like yesterdays so nature of
workload can at least be statistically characterized.
• Heuristic
• Workload is completely unpredictable
• Requests for work may change dramatically from hour to
hour or minute to minute
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10. CENTRALIZED VS DISTRIBUTED
• Centralized
• Collecting all the information at one place
(machine/system) allows better decision to be made but is
less robust and can put a heavy load on the central
machine.
• Distributed
• Opposite to centralized (may also be termed as
Decentralized). Here there is no central machine and
algorithm is implemented on all the machines.
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11. OPTIMAL VS SUB-OPTIMAL
• Depends upon the first two issues
• Are we trying to find best solution or simply an
acceptable one
• Optimal Solutions can be found out in both
centralize and distributed systems but finding
optimal solution may be costly as they involve
collecting more information and processing it more
thoroughly.
• In practice we use Heuristic, Distributed and Sub-optimal
solutions
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12. LOCAL VS GLOBAL
• Deciding whether to keep a new born or forked
process in the same machine or transferring to other
• Crude algorithms suggest to keep the newly born
process to the same machine if the workload on
that machine is below threshold value. But this
technique may be far from optimal.
• A better approach is to keep information of all the
systems and decide upon which system to be
allocated with the new process. This can provide a
slight better result than the local technique but at a
much higher cost.
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13. SENDER-INITIATED VS RECEIVER-INITIATED
ALGORITHMS
• This issue deals with location policy
• Once transfer policy decides whether to keep a process or
not, this comes into play
Sender Initiated Receiver Initiated
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14. IMPLEMENTATION ISSUES
• Calculating work load (not an easy task)
• A way suggests to count the total no. of processes and use the number as
the load – but on idle systems even there are various processes that keep
on running in background so process count says nothing about current
load)
• A second way is to count just the running or ready processes
• A more direct measure is to capture the busy time of the CPU that can be
achieved by setting a timer to generate periodic interrupts that records the
current CPU status. Con: Interrupts are switched off when kernel executes
critical code. This may lead to faulty readings and will tend to
underestimate the true CPU usage
• Another implementation takes into consideration the Overhead of the
algorithms (during transferring processes) but is not easy so most algorithms
ignore it
• Next we consider complexity of the algorithm as an issue. (The algorithm
may produce better results but its running time degrades the outcome and
which may not be better than existing algorithms). An example.
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15. PROCESSOR ALLOCATION
ALGORITHMS
• There are many algorithms like
• A GRAPH-THEORETIC DETERMINISTIC ALGORITHM
• A CENTRALIZED ALGORITHM
• A HIERARCHICAL ALGORITHM
• A SENDER-INITIATED DISTRIBUTED HEURISTIC ALGORITHM
• A RECEIVER INITITATED DISTRIBUTED HEURISTIC ALGORITHM
• A BIDDING ALGORITHM
• In this part we will study only about
• A GRAPH-THEORETIC DETERMINISTIC ALGORITHM
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16. A GRAPH-THEORETIC DETERMINISTIC
ALGORITHM
• Recall assumptions of Deterministic Algorithms
• Here the communication requirements are known
• There can be more processes than processors
• In which case multiple processes are allocated to one
processor
• The system can be represented as a weighted graph
• Each node is a process
• Each arc (edge) represents the flow of messages between two
processes
• Lets take a scenario where there are 3 processors and 9
processes
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17. A GRAPH-THEORETIC DETERMINISTIC
ALGORITHM (CONTD.)
• The weighted graph would look like
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18. A GRAPH-THEORETIC DETERMINISTIC
ALGORITHM (CONTD.)
• The problem is reduced to finding a way to partition
(i.e., cut) the graph into k disjoint sub-graphs,
subject to certain constraints (e.g., total CPU and
memory req. below some limits for each sub-graph)
• Arcs joining two sub-graphs will represent network traffic
• Arcs joining two processes within a sub-graph can be
ignored as it is intra-machine communication.
• Goal is to find the partitioning that minimizes the
network traffic while meeting all the constraints.
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19. A GRAPH-THEORETIC DETERMINISTIC
ALGORITHM (CONTD.)
CPU1 CPU 2 CPU3
Partitioning the graph to allocate 9 processes to 3 processors
Network traffic = ΣEn [sum of all network edges]
= 30
We can also partition the graph differently, as we will see in the next slide
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20. A GRAPH-THEORETIC DETERMINISTIC
ALGORITHM (CONTD.)
CPU1 CPU 2 CPU3
Partitioning the graph to allocate 9 processes to 3 processors
Network traffic = ΣEn [sum of all network edges]
= 28
Clearly we can see that a different approach reduces network traffic
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