Task scheduling for large scale computing systems is a challenging problem. From the users' perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing cost while providing the required service is critical. Therefore, there is a need for task scheduling mechanisms that balance users' performance requirements while being energy efficient. We present a time dependent Value of Service (VoS) metric that takes into consideration the arrival time of a task while evaluating the value of completing a task within its deadline and its energy consumption within a constraint. We consider the variation in value for completing a task at different times such that the value of energy reduction can change significantly between peak and non-peak periods. We use completion time and energy based value functions with soft and hard thresholds. Thus, we define the VoS for a given workload to be the sum of the values for all tasks that are executed during a given period of time. Our system model is based on virtual machines (VMs), where each task will be assigned a resource configuration characterized by the number of the homogeneous cores and amount of memory. Using VoS, we design, evaluate, and compare our task scheduling methods to show a significant improvement in energy consumption when considering time-of-use dependent scheduling algorithms. The experimental results are run on an IBM blade server using KVM. The experimental results show using time dependent VoS, 50% improvement in performance value, 40% improvement in energy value, and up to 91% improvement in VoS over a heuristic that only considers only the hard threshold of the value functions.