Serverless Computing and Scheduling Tasks on Cloud: A Review

Recently, the emergence of Function-as-a-Service (FaaS) has gained increasing attention by researchers. FaaS, also known as serverless computing, is a new concept in cloud computing that allows the services computation that triggers the code execution as a response for certain events. In this paper, we discuss various proposals related to scheduling tasks in clouds. These proposals are categorized according to their objective functions, namely minimizing execution time, minimizing execution cost, or multi objectives (time and cost). The dependency relationships between the tasks plays a vital role in determining the efficiency of the scheduling approach. This dependency may result in resources underutilization. FaaS is expected to have a significant impact on the process of scheduling tasks. This problem can be reduced by adopting a hybrid approach that combines both the benefit of FaaS and Infrastructure-as-a-Service (IaaS). Using FaaS, we can run the small tasks remotely and focus only on scheduling the large tasks. This helps in increasing the utilization of the resources because the small tasks will not be considered during the process of scheduling. An extension of the restricted time limit by cloud vendors will allow running the complete workflow using the serverless architecture, avoiding the scheduling problem

Effective resource multiplexing for scientific workflows

Scientific workflows feature complex precedence constraints that are mostly dictated by data dependencies between tasks. The inter-task communication (data staging) in these complex workflow applications incurs significant overheads resulting in a major hindering factor of high performance and effective resource utilization. As the scale of these applications becomes increasingly large due primarily to the recent explosive growth of data, addressing this hindrance is of great practical importance. In this paper, we present a resource multiplexing (RM) technique, which leverages data staging aiming to minimize idle times between execution of tasks due to inter-task communication overheads. In particular, we incorporate RM into our DEWE framework 1 with making a set of extensions to the framework. The rationale behind RM is each slot or core pairs up the actual workflow task and the RM-enabled file loading DEWE extension (File Client) in the way that their resource usage is complementary. We demonstrate the efficacy of our multiplexing technique in a data-intensive computing environment using an astronomy application. Our results from experiments conducted in Amazon EC2 demonstrate that our multiplexing technique is effective with the reduction in resource idle time between jobs by 57% on average and up to 91%.6 page(s