Loosely coupled and highly cohesived microservices running in containers are becoming the new paradigm for application development. Compared with monolithic applications, applications built on microservices architecture can be deployed and scaled independently, which promises to simplify software development and operation. However, the dramatic increase in the scale of microservices and east-west network traffic in the data center have made the cluster management more complex. Not only does the scale of microservices cause a great deal of pressure on cluster management, but also cascading QoS violations present a substantial risk for SLOs (Service Level Objectives). In this paper, we propose a Microservice-Oriented Topology-Aware Scheduling Framework (MOTAS), which effectively utilizes the topologies of microservices and clusters to optimize the network overhead of microservice applications through a heuristic graph mapping algorithm. The proposed framework can also guarantee the cluster resource utilization. To deal with the dynamic environment of microservice, we propose a mechanism based on distributed trace analysis to detect and handle QoS violations in microservice applications. Through real-world experiments, the framework has been proved to be effective in ensuring cluster resource utilization, reducing application end-to-end latency, improving throughput, and handling QoS violations.

With the development of network technology, Network Function Virtualization (NFV) provides a good paradigm of sharing network resources, aiming to transfer network functions from hardware-based devices to software-defined Virtual Network Function (VNF) instances. Each type of VNF is mostly multi-instance, and different VNF instances require to be chained in a predefined sequence to form Service Function Chain (SFC) to provide network services. However, due to the resource capacity constraints of edge nodes and the high latency of cloud edge links in cloud-edge system, it is crucial and challengeable to deploy SFC in NFV-enabled networks. In our paper, we study the SFC deployment (SFC-D) problem in cloud-edge environment. We formulate the SFC-D as an Integer Linear Programming (ILP) model aiming to minimize the deployment cost, and propose a resource-aware deployment algorithm (RADA). According to our extensive evaluations, compared with the state-of-the-art counterpart algorithm, RADA performs better in the remaining available resources of nodes, and the average acceptance rate increases by 15%.

Due to the characteristics of resource-constrained and battery-powered sensors in wireless sensor networks (WSNs), energy consumption is always a major concern. Data aggregation is an essential technique to reduce the communication overhead and energy consumption. Since many applications require data privacy, we need to take security into consideration. In this paper, we propose an energy-efficient, secure, highly accurate, and scalable scheme for data aggregation (EESSDA). The main idea of EESSDA is that secure data aggregation is achieved by establishing secure channel and slicing technology. The EESSDA scheme does not need encryption and decryption operations during the data aggregation, which saves energy and obtain high accuracy of aggregation results. Meanwhile, in EESSDA scheme, the advanced deployment of shared information between nodes is not required, making the networks with good scalability. Our analysis and simulations show that EESSDA is of lower communication overhead, more efficiency and accuracy, and better privacy preservation and scalability than existing schemes.