The deployment of the microservice architecture in edge networks presents new opportunities for supporting latency-sensitive network services. However, most such services are both computation-intensive and energy-consuming, posing significant challenges for edge nodes with constrained computing resources and energy supply. Therefore, designing efficient microservice orchestration strategies to reduce service latency and network energy consumption is essential but highly challenging. Due to frequent communication among microservices, service deployment and request routing are tightly coupled, which lead to a complex joint optimization problem. This complexity further increases when considering large-scale microservices under multi-instance modeling and fine-grained analysis. Nevertheless, previous work has failed to address these challenges and largely overlooked the balance between latency and energy consumption. To overcome these issues, this paper proposes an energy-latency balanced microservice orchestration method to jointly minimize service latency and energy usage. First, we adopt multi-instance modeling to enable precise end-to-end latency analysis, and integrate an energy model to quantify overall network consumption. Then, we design the Node Ranking Matrix-based Microservice Orchestration Algorithm (NRMA), which dynamically selects high-ranking nodes based on centrality and energy metrics, thereby balancing latency and energy in the deployment stage. Moreover, we use the proportional routing strategy that distributes user request traffic according to the number of deployed instances, preventing node overload and reducing cross-node communication. Experimental results show that the proposed method is significantly better than the baseline algorithms in terms of latency and energy consumption, and achieves significant results.

The emerging microservice architecture offers opportunities for accommodating delay-sensitive applications in edge. However, such applications are computation-intensive and energy-consuming, imposing great difficulties to edge servers with limited computing resources, energy supply, and cooling capabilities. To reduce delay and energy consumption in edge, efficient microservice orchestration is necessary, but significantly challenging. Due to frequent communications among multiple microservices, service deployment and request routing are tightly-coupled, which motivates a complex joint optimization problem. When considering multi-instance modeling and fine-grained orchestration for massive microservices, the difficulty is extremely enlarged. Nevertheless, previous work failed to address the above difficulties. Also, they neglected to balance delay and energy, especially lacking dynamic energy-saving abilities. Therefore, this paper minimizes energy and delay by jointly optimizing microservice deployment and request routing via multi-instance modeling, fine-grained orchestration, and dynamic adaptation. Our queuing network model enables accurate end-to-end time analysis covering queuing, computing, and communicating delays. We then propose a delay-aware reinforcement learning algorithm, which derives the static service deployment and routing decisions. Moreover, we design an energy-aware dynamic frequency scaling algorithm, which saves energy with fluctuating request patterns. Experiment results demonstrate that our approaches significantly outperform baseline algorithms in both delay and energy consumption.

Service mesh architectures are emerging as a promising microservice paradigm for developing online cloud applications. However, in large-scale microservice scenarios, frequent service communications, intricate call dependencies, and stringent latency requirements bring great pressure to efficient service mesh orchestration. In this case, the problems of service deployment and request routing based on service mesh architectures are tightly-coupled and interdependent, and cannot be effectively optimized individually, enlarging the difficulty for collaborative orchestration. When microservice multiplexing, parallel dependencies, and multi-instance modeling are considered, the difficulty is further aggravated. Nonetheless, most existing work failed to propose appropriate models and methods for the above challenges. Therefore, this article studies the large-scale service mesh orchestration with probabilistic routing and constrained bandwidths for parallel call graphs. We leverage the open Jackson queuing network theory to capture crucial microservices and analyze request processing, queuing, and communication latency for massive user requests in a fine-grained way. Then, this article proposes an efficient three-stage heuristic, which achieves elegant multi-instance consolidation and probabilistic multi-queue routing to reduce response latency and cost. We also provide the algorithm complexity and mathematical analysis of the performance. Finally, extensive trace-driven experiments are performed to validate the superiority of our proposed algorithm over other baselines.

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy.

Microservices as an emerging architecture are creating new opportunities to enable superior network services in Mobile Edge Computing (MEC). In the presence of huge amounts of user requests, the massive communications among microservices have become notoriously complicated. Due to the intricate data dependencies of the microservices, the overall performance of large-scale MEC applications simultaneously depends on both service deployment and request routing. However, most existing work ignores the interdependencies of microservices and studies the deployment and routing as two isolated problems. In this case, this paper investigates the joint optimization of service deployment and request routing in edge computing. We first formulate a delay minimization problem via mixed integer linear programming and queuing analysis, and then provide a hardness proof on the problem. In addition, this paper presents a 2-approximation algorithm, followed with rigorous mathematical proofs to demonstrate the approximation ratio. The proposed two-phase algorithm consists of rounding based service deployment and adaptive-scaling-based request routing policies, which employ fine grained joint optimization to minimize service response delay. Finally, we illustrate the near-optimal performance of the proposed algorithm via comprehensive experiments.

Microservices are an architectural and organizational paradigm for Internet application development. In cloud data centers, delay-sensitive applications receive massive user requests, which are fed into multiple queues and subsequently served by multiple microservice instances. Accordingly, effective deployment of multiple queues and containers can significantly reduce queuing delay, processing delay, and communication delay. Due to the increased complexity of call dependencies and probabilistic routing paths, the deployment of service instances fully interacts with request routing, bringing great difficulties to service orchestration. In this case, it is valuable to simultaneously consider service deployment and request routing in a fine-grained manner. However, most existing studies considered them as two independent components with local optimization, while data dependencies and the instance-level deployment are ignored. Therefore, this paper proposes to jointly optimize the deployment and request routing of microservice call graphs based on fine-grained queuing network analysis and container orchestration. We first formulate the problem as a mixed-integer nonlinear program and exploit open Jackson queuing networks to model intrinsic data dependencies and analyze response latency. To optimize the overall cost and latency, this paper presents an efficient two-stage heuristic algorithm, which consists of a resource-splitting-based deployment approach and a partition-mapping-based routing method. Further, this paper also provides mathematical analysis on the performance and complexity of the proposed algorithm. Finally, comprehensive trace-driven experiments demonstrate that the overall performance of our approach is better than existing microservice benchmarks. The average deployment cost is reduced by 27.4% and end-to-end response latency is reduced by 15.1% on average.

Aiming at measuring tree barriers in transmission line corridors, a binocular vision ranging method is proposed to measure the distance between the transmission lines and trees. Based on the principle of binocular vision ranging, the binocular camera is calibrated using a marked checkerboard as a calibration board. Then, the SAD region matching algorithm is applied to the preprocessed images, and the disparity map is acquired. Finally, the distance between the tree and the transmission line can obtain according to the three-dimensional coordinate information of two target points. The results of the experiments show that the proposed binocular vision-based method can achieve the measurement error within ±30cm. The precision can meet the need of measuring the distance between trees and the transmission lines, which is an effective way for warning tree barriers in transmission line corridors.

Carrying out high efficient and rapid analysis of big data is essential to big data application. Due to the poor scalability of DRAM, the performance of big data analysis and related applications is difficult to improve. DRAM/NVM hybrid storage architecture has the advantages of non-volatile and high storage density, which brings an opportunity to optimize big data analysis. Because the task itself depends on the data and does not modify the data, it is possible to solve the problem of operation delay if the data is deployed well on the storage system under the background of hybrid storage architecture. In order to optimize the problem of high latency, this paper discusses the data migration between disk and NVM and proposes a data deployment algorithm based on data label. The validity of labeling is verified by calculating the total time of reading data by tasks in the experiment and the efficiency of task execution is improved.