QoS-aware simulation job scheduling algorithm in virtualized cloud environment

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

Selling FLOPs

To meet the user demand for an ever-increasing mobile-cloud computing performance for resource-intensive mobile applications, we propose a new service architecture called Acceleration as a Service (AXaaS). We formulate AXaaS based on the observation that most resource-intensive applications, such as real-time face-recognition and augmented reality, have similar resource-demand characteristics: a vast majority of the program execution time is spent on a limited set of library calls, such as Generalized Matrix-Multiply operations (GEMM), or FFT. Our AXaaS model suggests accelerating only these operations by the Telecom Service Providers (TSP). We envision the TSP offering this service through a monthly computational service charge, much like their existing monthly bandwidth charge. We demonstrate the technological and business feasibility of AXaaS on a proof-of-concept real-time face recognition application. We elaborate on the consumer, developer, and the TSP view of this model. Our results confirm AXaaS as a novel and viable business model.