Designing a job scheduler for a distributed system requires careful consideration of various factors such as job priority, resource availability, and system load. Here is an approach to design a job scheduler for distributed systems:
1. **Job Classification:** Understanding the nature of the job is the first step in designing job scheduler. Jobs could be CPU-bound, memory-bound, I/O-bound etc. Consider the priority of the jobs, whether they can be executed in parallel, or they have dependencies.
2. **Resource Awareness:** The scheduler should be aware of the resources of the system - how many nodes are there, what is the capacity of each node in terms of CPU, memory, storage, network bandwidth.
3. **Load Balancing:** The scheduler needs to distribute jobs to ensure that all worker nodes have approximately equal workload. It requires keeping track of the number of executing jobs on each node, their resource consumption, and the node-capacity.
4. **Fault Tolerance:** The scheduler should be able to handle failures. If a node fails, the jobs running on that node should be rescheduled to run on some other node.
5. **Data Locality:** In data-intensive jobs, data locality plays a significant role in improving performance. Scheduler should schedule jobs where data is located to reduce data movement over the network.
6. **Scheduling Algorithms:** Various job scheduling algorithms can be used based on the requirements. For instance, First Come First Serve (FCFS), Shortest Job Next (SJN), Round Robin (RR), Priority Scheduling, Fair Sharing etc.
A simple design of job scheduler would have the following components in its architecture:
1. **Job Queue:** It is used to store the incoming jobs to be scheduled for execution. This can be a priority queue for supporting priority-based job scheduling.
2. **Scheduler:** It takes into account the priorities, dependencies, and resources required for the job. Based on these factors and the scheduling algorithm, it decides which job to assign to which worker node.
3. **Worker Nodes:** They execute the jobs received from the scheduler. They also update the scheduler about their available resources and current load.
Example: In hadoop’s MapReduce, YARN (Yet Another Resource Negotiator) acts as a scheduler. The Resource Manager is the main authority that arbitrates resources among all the applications in the system. Node Manager is the per-machine framework agent who is responsible for containers, monitoring their resource usage and reporting the same to the Resource Manager/Scheduler.
Here’s a visual representation:
Node manager reports to Resource Manager about resource availability. Resource Manager’s responsibility is to allocate resources to different applications based on requirement and priority. Application Master is responsible for accepting job-submissions, negotiating the first container for executing the application specific ApplicationMaster and provides the service for restarting the ApplicationMaster container on failure.
The challenges in such a design include dealing with "starvation" where a low-priority job may never get scheduled if high-priority jobs keep coming. These can be mitigated using techniques like aging, where a job increases its priority if it waits too long in the queue.