Efficient multi-attribute precedence-based task scheduling for edge computing in geo-distributed cloud environment

In order to realize globalization of cloud computing, joint use of different services of different cloud providers has become an inevitable trend. The geo-distributed cloud consists of several different clouds, providing a general environment for cloud computing. In data placement, many recently proposed data placement algorithms unilaterally use a single performance index to evaluate the performance of the algorithm. In task scheduling, when tasks are allocated with excess cloud resources, resources are wasted. When little cloud resources are allocated to the complex task, cause the overall progress of the system to stagnate, the overall progress of the system is stalled. For solving the above problems, the data placement method and the task scheduling method are proposed. In the proposed data placement scheme, multiple performance indicators are considered. The detection of the straggling nodes and the reasonable allocation of cloud resources are taken into account when the task is scheduled. For proving the superiority of the proposed methods, extensive experiments are conducted. In terms of the data placement, when the number of files is set as 800, the safety level of the proposed data placement algorithm is 7.0, which is 27.3% higher than that of the IDP algorithm, 45.8% higher than that of the GA-DPSO algorithm and 16.7% higher than that of the H2DP algorithm. As for the task scheduling, the percentage improvement in the time overhead of the proposed task scheduling method is the lowest, which implies that the time overhead of the proposed task scheduling algorithm is closest to the optimal time and is the shortest.