A Novel Scheduling Approach of E-learning Content on Cloud Computing Infrastructure

E-learning ecosystem based on cloud computing infrastructure constantly gains a popularity in a wide research and consumer popularity. There are multiple reasons for this, including the dynamic adaptability of clouds to the changing demands, their ability to provide resources per need basis and the support for virtualization. By additionally bringing the robust security and chargeback model in it, the cloud becomes a real next generation engine for all e-learning aspects, including database, application and web layers. However, and especially in a hybrid public cloud environments, the right allocation scheme of an e- learning content is critical if the given service level agreement has to be fulfilled. It is by far not enough to distribute content accordingly to the provided amount of processing power of computational nodes and corresponding storage systems. If the content scheduler does not take into account the throughput of the network communication links, the whole system become saturated with waiting times and the response of the cloud based e- learning system degrades. Our approach is to consider both processing power of computational nodes and communication links. The idea is to minimize SLEM, the magnitude of the second largest eigenvalue of the positive symmetric square matrix with elements representing time activities of computational nodes and communication links in parallel processing environment. It shows that this minimization problem can be recast as a semi-definite optimization problem, with ability to find an optimal load distribution of e-learning content.