Multi-heuristic scheduling methods for workflow in credit cloud

In the credit cloud, credit services are sold to applications for credit computing, credit fusion and credit risk estimates. Plenty of services with different performance for the same task may have different execution time and charged by various ways. The users have specific requirements for the workflow completion time or cost. Hence, to meet the user’s satisfaction is an important challenge. In this paper, we propose heuristic scheduling methods for credit workflow with total cost minimization, and the deadline should be satisfied. The problem can be divided into two sub-problems, task-mode mapping and task tabling on renting service instances. For the task-mode mapping problem, a recursive heuristic method is constructed to select appropriate service for each task of the workflow. Then another heuristic algorithm based is established to get a final schema with deadline constraint. We discussed the service instance rented in shareable manner and compared with un-shareable manner. Three renting strategies are discussed in detail. Experimental results show the effectiveness and efficiency of the proposed algorithm.

An MQTT Brokers Distribution Based on Mist Computing for Real-Time IoT Communications

As we consider the number of IoT time-sensitive applications , the transfer of data to a remote data center and server such as Cloud, Fog, and Edge becomes inefficient since the deadline constraint is not satisfied. Thus, ensuring that the IoT time-sensitive applications meet their timing constraints is a challenge. Mist Computing is closer to IoT devices, presenting the lowest communication delay but less computational resource than the Cloud, Fog, and Edge. Seeing several IoT devices use MQTT protocol to access the data due to its lightness and flexibility, we propose an architecture for IoT time-sensitive applications based on MQTT protocol and integrating Mist Computing. We focus on distributing the MQTT brokers over Mist nodes to satisfy the deadline constraints with the consideration of the limited resource of Mist nodes. Hence, we propose an approach for the selection of the appropriate MQTT Mist broker. We have also proposed MQTT communication model that provides the M/M/1 based analysis for delay computing and energy conception. The experiment results show that our proposal is very effective for time-sensitive applications and also maximize the lifetime of IoT systems since it minimizes the cumulative energy of the system. Compared to MQTT Edge broker distribution, our solution provides the lesser delay of communication between IoT devices.

Energy-aware task offloading with deadline constraint in mobile edge computing

With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.