Introduction
Job scheduling is the challenge of arranging jobs with set deadlines to optimize profit. The key goal is to determine the job sequence that maximizes completion within the time limit. It's a well-known greedy algorithm-based problem with numerous real-world applications. Job scheduling is the process of executing certain jobs at a predetermined time or when the appropriate event occurs. A task scheduler is a software system that can be coupled with other systems to execute or inform other software components when a predetermined, scheduled time arrives.
The primary goals of scheduling algorithms are to reduce resource scarcity and promote fairness among those who use the resources. Scheduling is concerned with determining which of the pending requests will receive resources.
Need for Job Scheduling algorithm
Enterprise task scheduling benefits your bottom line for various reasons, including increased overall efficiency, improved customer service, and more time spent on high-level strategy. The elimination of unnecessary downtime is one area where an enterprise job scheduler is particularly useful.
Problem
You are given a series of n jobs, each with a deadline and a profit attached. Only one work can be planned at a time, and each job takes one unit of time to accomplish. If and only if the project is finished before the deadline, we gain the profit linked with it. Find the most profitable job scheduling for the provided jobs.
Solution
To understand this problem's solution, one should be aware of the greedy algorithm and Priority queue.
Greedy algorithm
Greedy is an algorithmic paradigm that takes the approach of choosing the locally best decision at each stage to the globally best solution. A greedy algorithm always selects the best immediate or local solution when achieving the final answer. To get more insights about greedy algorithms, you can refer to our greedy algorithm path from code studio.
A priority queue is a form of the queue in which each entry has a priority value assigned to it. In addition, items are served in order of importance. That is, the elements with the highest priority are served first. If elements with the same priority appear, they are served in the order they appeared in the queue. To get a better understanding in deep, you can check out our blogs on heap form code studio.
Approach
To solve the job scheduling problem, we'll employ a greedy strategy. We'll take the steps below to arrange the job the way we want it.
- To begin, arrange the jobs in decreasing order of profit.
- Then, from all the deadlines, choose the one with the highest priority.
- Individual job ids must then be assigned time slots.
- First, see if the task's maximum available time slot, i.e., its deadline, has been given to another job. Assign the slot to the current job id if it hasn't been filled yet.
- Otherwise, look for any open time window that is less than the present job's deadline. If one is found, assign the slot to the current work id and continue on to the next job id.
Complexity
The time complexity for the above job scheduling algorithm problem is O(n^2) in the worst case because we are iterating two times in a nested loop. Here space complexity is O(n) as extra space is used in the above implementation.
Optimized approach
- Sort the jobs according to their due dates.
- Iterate from the beginning, calculating the available slots between each pair of deadlines. In the max heap, including the profit, deadline, and work ID of the ith job.
- Include the job ID with the highest profit and deadline in the result while slots are available and jobs remain in the max heap.
- Sort the result array by the deadlines they have.
Complexity
The time complexity for the above job scheduling algorithm problem is O(n*log(n)), where n is the size because we are sorting the array that will cost n*log(n) time in the best case. Here space complexity is O(n) as extra space is used in the above implementation.
Application in operation system
An operating system's job scheduling is the process of allocating system resources to a variety of tasks (OS). The system should handle prioritized job queues waiting for CPU time, determining which job should be taken from which queue and how much time should be provided to each job.
Following are the popular process scheduling algorithms :
- First-Come, First-Served (FCFS) Scheduling.
- Shortest-Job-Next (SJN) Scheduling.
- Priority Scheduling.
- Shortest Remaining Time.
- Round Robin(RR) Scheduling.
- Multiple-Level Queues Scheduling.
An operating system uses process scheduling to guarantee that processes run smoothly and with minimal wait periods. Process scheduling strategies are designed to make efficient use of CPU resources, boost throughput, decrease wait times, and improve response and turnaround times. Short processes are done first, followed by lengthier processes, according to the definition. Because more processes may be completed in less time, throughput is boosted.
Best Scheduling algorithm
In brief bursts of time, the FCFS algorithm is better than the others. However, Round Robin is better for numerous processes all of the time. However, it is impossible to foretell what process will follow. The average waiting time is a standard metric for assessing the scheduling algorithm's performance.
Scheduling skills
Scheduling abilities refer to the capacity to tackle such complex and frustrating situations by preparing your actions in such a way that you can finish all of your tasks and goals according to your priorities while staying within the time constraints.
Some more applications
In networking, such as the road network, railway network, and airline network, sequencing techniques play a critical role. These are the real-world scenarios in which optimal routing is necessary.
Our job scheduling and job sequencing are the same
The order in which actions must be completed in a chain is referred to as sequencing. As a result, the following duty begins after the prior one is accomplished. Scheduling, on the other hand, is the act of assigning employees to specific times to do various duties. It enhances delivery quality while reducing manufacturing time and expense.
