Abstract: Cloud-based computing systems, although claimed to have virtually unlimited resources, could get oversubscribed due to budget constraints of cloud users. The disclosed invention proposes a mechanism to identify various types of “mergeable” tasks. The system also determines when it is appropriate to aggregate tasks and how to allocate them so that the QoS of other tasks is not affected. Experimental results under real-world workload settings show that the disclosed system can improve robustness of the system in the face of oversubscription and also saves the overall time of using cloud services by more than 14%.

Abstract: Disclosed is a method for task pruning that can be utilized in existing resource allocation systems to improve the systems' robustness without requiring changing to existing mapping heuristics. The pruning mechanism leverages a probability model, which calculates the probability of a task competing before its deadline in the presence of task dropping, and only schedules tasks that are likely to succeed. Pruning tasks whose chance of success is low improves the chance of success for other tasks. Tasks that are unlikely to succeed are either deferred from current scheduling event or are preemptively dropped from the system. The pruning method can benefit service providers by allowing them to utilize their resources more efficiently and use them only for tasks that can meet their deadlines. The pruning method further helps end users by making the system more robust in allowing more tasks to complete on time.

Abstract: Disclosed is a method for task pruning that can be utilized in existing resource allocation systems to improve the systems' robustness without requiring changing to existing mapping heuristics. The pruning mechanism leverages a probability model, which calculates the probability of a task competing before its deadline in the presence of task dropping, and only schedules tasks that are likely to succeed. Pruning tasks whose chance of success is low improves the chance of success for other tasks. Tasks that are unlikely to succeed are either deferred from current scheduling event or are preemptively dropped from the system. The pruning method can benefit service providers by allowing them to utilize their resources more efficiently and use them only for tasks that can meet their deadlines. The pruning method further helps end users by making the system more robust in allowing more tasks to complete on time.

Abstract: Cloud-based computing systems, although claimed to have virtually unlimited resources, could get oversubscribed due to budget constraints of cloud users. The disclosed invention proposes a mechanism to identify various types of “mergeable” tasks. The system also determines when it is appropriate to aggregate tasks and how to allocate them so that the QoS of other tasks is not affected. Experimental results under real-world workload settings show that the disclosed system can improve robustness of the system in the face of oversubscription and also saves the overall time of using cloud services by more than 14%.