YACS

• Please view the document README.pdf for execution procedure and more details

Detailed Working of the code:-

I. master.py

Args - path of config, algo_type
Worker - nested dictionary
	key:value - worker_id : dictionary
		keys - max_slots,port_no,free_slots

workerIndex - sorted list of workers

Threads Used
	job_request_handler - listens for incoming job requests from request.py
												on port no 5000
		- separate thread, so runs parallelly
		- run method overrides default run()
		- run() calls job_object_maker()
			- newJob - nested dictionary
				key:value - remaining_map_tasks : len of map_tasks
							 remaining_reduce_tasks : len of reduce_task
							 map_tasks : nested dictionary
							 	key:value - task_id : dictionary
									keys - worker assigned, status {-1(not yet proc),0(processing),1(done)}
													type of task, job_id of the task
							reduce_tasks - similar to map_tasks
			- Log starting of Job
			- Jobs - dictionary
				key:value - job_id : newJob dict --------- locked
			- TaskPool - list
				append all map tasks to TaskPool --------- locked
		- close connection, listen for new one

	task_handler - maps worker to task based on algo
		- separate thread, so runs parallelly
		- run() method:-
			- pops a task from TaskPool, stores in new_task -------- locked
			- Based on type of algo, allocates worker if it has slots available
			- free_worker - assigned worker by algo
			- Decrement no of free slots on chosen worker -------- locked
			- Change status of task and assigned worker in Jobs dict
			- Connect to selected worker, send new_task
			- Log starting of Task
			- Close Connection

	worker_response_handler - receives task completion status from worker
		- separate thread, so runs parallelly
		- run() method:-
			- receives response from worker on socket 5001
			- response contains info about all completed tasks at that instant
			- It logs the completion of tasks
			- Increment no of free slots on worker -------- locked
			- Change status of the executed task to finished --- 1
			- Decrement count of remaining tasks
			- If all tasks in a Job are complete,
			 	- log completion of Job ----------- locked
			- If all map tasks of a Job are complete,
				- append all reduce tasks to TaskPool ---------- locked
			- Close connection, listen for new responses

start - implicitly calls the run method
join - main thread which spawns threads waits for threads to terminate

II. worker.py

Args - req_port,slots
execution_slots - list of dict - new_task, initially None x len(total_slots)

Threads Used:-
slot_handler() - receives scheduled task from worker
	- new_task - dict containing task info, rec from worker
	- find a free slot and allocate to task ---------- locked
	- close connection with master, listen for more incoming requests

task_executer() - sends task completion status to master
	- message_list - list of completed tasks(dicts)
	- If a given slot is assigned to a task,
		- Decrement the duration, if task completed, add to message_list ---- locked
	- Sleep for 1 sec, since duration is in secs
	- if there are completed tasks, send their info to master on port 5001
	- close connection

III. analyzer.py

reads from logs

generate_jobs_tasks()
	task_dict - stores info about each task
	job_dict - stores info about each job
	worker_dict - stores info about each worker

	- Reads timestamps(Start/End) of job/task, job_id, task_id
	- Stores info about each task, job, worker in dict

calc_mean_median() - calculates mean, median after calculating time required
											for completion (End - Start)

task_2() - Plots graphs