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![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
RITU RANJAN SHRIVASTWA](https://image.slidesharecdn.com/processorallocation-slideshare-141013061436-conversion-gate02/75/Processor-allocation-in-Distributed-Systems-19-2048.jpg)
![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
RITU RANJAN SHRIVASTWA](https://image.slidesharecdn.com/processorallocation-slideshare-141013061436-conversion-gate02/75/Processor-allocation-in-Distributed-Systems-20-2048.jpg)
Uploaded byRitu Ranjan Shrivastwa
Processor allocation in Distributed Systems
The document discusses processor allocation strategies in distributed systems, highlighting the importance of effective allocation for enhancing performance and minimizing CPU idle time. It categorizes processor allocation into nonmigratory and migratory strategies, detailing their benefits and challenges, and reviews various algorithm types such as deterministic, heuristic, centralized, and distributed. Additionally, it outlines practical implementation issues and introduces a graph-theoretic deterministic algorithm for optimizing resource allocation through traffic minimization.
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Processor allocation in Distributed Systems
- 1. PROCESSOR ALLOCATION BY R I TU RANJAN SHR I VAS TWA Distributed Systems
- 2. WHAT YOU WILLLEARN? 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 RITU RANJAN SHRIVASTWA
- 3. MOTIVATION • Weare 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) RITU RANJAN SHRIVASTWA 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 RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 6. AN EXAMPLE OFPROCESSOR ALLOCATION TO GIVE AN IDEA OF THE NEED RITU RANJAN SHRIVASTWA 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 OFPROCESSOR ALLOCATION TO GIVE AN IDEA OF THE NEED RITU RANJAN SHRIVASTWA 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 PROCESSORALLOCATION • 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 RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 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. RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 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. RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 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. RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 17. A GRAPH-THEORETIC DETERMINISTIC ALGORITHM (CONTD.) • The weighted graph would look like RITU RANJAN SHRIVASTWA
- 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. RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
- 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 RITU RANJAN SHRIVASTWA
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