Application Layer Scheduling in Cloud: Fundamentals,Review and Research Directions

The cloud computing paradigm facilitates a finite pool of on-demand virtualized resources on a pay-per-use basis. For large-scale heterogeneous distributed systems like a cloud, scheduling is an essential component of resource management at the application layer as well as at the virtualization layer in order to deliver the optimal Quality of Services (QoS). The cloud scheduling, in general, is an NP-hard problem due to large solution space, thus, it is difficult to find an optimal solution within a reasonable time. In application layer scheduling, the tasks are mapped to logical resources (i.e., virtual machines), aiming to optimize one or more QoS parameters, and conforming to several constraints. Various algorithms have been proposed in the literature for application layer scheduling, where each of them is based on some fundamental design techniques like simple heuristics, meta-heuristics, and most recently hybrid heuristics. Although ample literature survey exists for cloud scheduling algorithms, none of them present their study exclusively for the application layer. In this survey paper, we present a study on task scheduling algorithms used only at the application layer of the cloud. We classify our study according to various fundamental techniques used in designing such scheduling algorithms. One of the main features of our study is that it covers numerous application type e.g., a set of independent tasks, simple workflow, scientific workflow, and MapReduce jobs. We also provide a comparative analysis of existing algorithms on various parameters like makespan, cost, resource utilization, etc. In the end, research directions for future work have been provided.