The cloud-edge-end collaboration framework is emerging as a promising means to handle diverse tasks and improve Quality of Service. Existing research rarely considers the affinity between diverse tasks and heterogeneous resources, preventing the further improvement of system performance. This paper proposes a dynamic task scheduling approach based on the intelligent affinity strategy for cloud-edge-end collaboration. The affinity strategy depicts the preference of tasks for computing nodes with different labels, including resource types and node zones, and each task can set multiple affinity rules to match target nodes. Additionally, the proposed approach adopts deep reinforcement learning theory to generate the affinity rules, which means utilizing an intelligent algorithm to guide the rule generation. Extensive experiments show that the proposed approach can reduce the average cost by at least 20% compared with the baseline.

Edge computing has become a promising technology to mitigate the latency of various cloud services. Task scheduling in edge computing is challenging due to the heterogeneous devices and multiple tasks. This paper proposes an affinity-based scheduling algorithm to solve the multi-task scheduling problem with heterogeneous computing resources under edge computing. The algorithm uses a centralized scheduling strategy that takes into account the overall matching between intelligent computing tasks and heterogeneous resources from two aspects: resource affinity and system load balancing. It can adjust the weights among these aspects to meet different application requirements. The results show that the proposed algorithm can reduce the average response time by at least 10% and has a significant advantage regarding the system running time compared to the other comparative methods.

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope.

Nowadays Telecom service providers are seeking new paradigms of service creation and execution platform to reduce new services' time to market and increase profitability. However, the existing static services orchestration approaches cannot meet the dynamic complicated business demands. This paper proposes a hybrid service orchestration platform for Internet-Telephony networks. Firstly, designs a hybrid service orchestration language for developers to achieve dynamic and rapid orchestration of new hybrid services over the Internet-Telephony networks. Secondly, proposes an event-driven and component-based hybrid service orchestration container to meet the asynchronous dynamic interactions between hybrid services. Thirdly, proposes a cost-aware auto-scaling approach, including the pre-scaling and real-time scaling stages, to dynamically scale the required resources at different levels. Finally, illustrates the hybrid voice chatting services orchestration scenario, and also the effectiveness and practicability of the proposed platform are validated through extensive experiments.

The rapid development of Internet of Things (IoT) attracts growing attention from both industry and academia. IoT seamlessly connects the physical world and cyberspace via various sensors. It is more worth for us to pay attention to the mechanism of the events to work collaboratively rather than those standalone sensors. In this paper, we present a Distributed Event-centric Collaborative Workflows development system for IoT application, called DECW. It supports loosely coupled event-based interaction between processes, which enables real-time response to events from the physical world. Unlike traditional centralized control flow mode, the interaction between processes in DECW is constrained by the event interface. Users could dynamically adjust the interface between processes without modifying the internal logic of the process. In addition, DECW system provides a full lifecycle for the development and operation of the IoT application, including graphical creation of processes, dynamic definition of the process interaction interfaces, logical validation, distributed packaging and deployment, parallel execution, and real-time monitoring and managing the running status of the IoT application.

Modern Internet of Things (IoT)-aware business processes include various geographically dispersed sensor devices. Large amounts of raw data acquired from sensors need to be regularly transmitted to the targeted processes in enterprise data centers, resulting in a significant increase in network load and latency. It is necessary to execute such processes in a distributed way. The existing work has proposed different algorithms to partition a given process for distributed execution; however, they cannot satisfy the decentralized nature of IoT-aware business processes. Moreover, up to now, there is few work that studies uncertain optimal deployment problems in which traffic data for guiding subsequent deployment derives from experts' empirical knowledge. This paper proposes a novel location-based fragmentation algorithm and α-optimal deployment solution to deal with the mentioned problems, where α is the given confidence level. A hardware-in-the-loop simulation platform based on NS-3 was built. Based on this platform, an integrated monitoring process was deployed that ran on different virtual computers, and process fragments communicated with each other via a simulated network. The experimental results show that the proposed approach can reduce network traffic and round-trip time.