This study presents a soft deadline scheduler for distributed systems that aims of exploring data locality management. In Hadoop, neither the Fair Scheduler nor the Capacity Scheduler takes care about deadlines defined by the user for a job. Our algorithm, named as Cloud Least Laxity First (CLLF), minimizes the extra-cost implied from tasks that are executed over a cloud setting by ordering each of which using its laxity and locality. By using our deadline scheduling algorithm, we demonstrate prosperous performance, as the number of available nodes needed in a cluster in order to meet all the deadlines is minimized while the total execution time of the job remains in acceptable levels. To achieve this, we compare the ability of our algorithm to meet deadlines with the Time Shared and the Space Shared scheduling algorithms. At last we implement our solution in the CloudSim simulation framework for producing the experimental analysis.