2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6550-6568· 0 citations· 42 references
Abstract
Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managing such Apps involves three key challenges: (i) optimizing the placement of MSs to reduce both response time and resource overhead, (ii) handling MS migration or relocation as users move while minimizing App service disruption (App downtime), and (iii) enabling MS sharing across Apps while ensuring performance guarantees. We formulate this as an optimization problem, named Multi-microservice Application Placement (MAP), prove its NP-hardness, and introduce STEP (State and Topology-aware Edge-MS Placement), a polynomial-time heuristic. STEP distinguishes itself from prior work by: (i) jointly considering stateful and stateless MS characteristics in deployment decisions, (ii) exploiting MS shareability to reduce resource usage, (iii) balancing response latency, App downtime, and resource utilization, and (iv) leveraging multiple versions of the same MS to adapt quality of service to available edge resources. Our results in a small-scale scenario show that STEP achieves near-optimal performance with only 7% higher CPU cost than the optimal solution. Large-scale real-time experiments on a Kubernetes cluster demonstrate that STEP consistently outperforms competing methods, achieving up to 50% lower deployment costs while delivering 50% gain in app quality and saving 15% in radio resources with over 90% request success rates.
With the advent of the 6G era and the evolution of distributed systems, edge computing has become a pivotal architecture for deploying latency-sensitive, resource-efficient applications. In particular, the microservice architecture, characterized by modular and loosely coupled components, has gained significant traction for building scalable and maintainable applications at the network edge. However, deploying microservice-based applications in heterogeneous and geographically distributed Multi-Cluster Edge Computing (MCEC) environments presents critical challenges, especially in achieving efficient and scalable resource management. Although existing research has explored resource allocation and container scaling for microservice-based systems, most prior works consider container efficiency in isolation or within single-cluster or cloud-centric environments, without jointly addressing container-level efficiency, inter-cluster task offloading, and resource allocation in MCEC scenarios. To address this gap, we propose RACCOON, a request-offloading cascaded resource allocation algorithm tailored for microservice-oriented deployments in MCEC settings. RACCOON aims to minimize user-perceived service latency while optimizing overall resource utilization. Complementing this, we introduce RASCAL, a reinforcement learning (RL)-based container scaling mechanism that dynamically adjusts resource provisioning at the container level to further enhance system performance. Experimental evaluation shows that our approach consistently outperforms methods that address only resource allocation, only task offloading, or only container scaling, by jointly optimizing these dimensions to reduce end-to-end user-perceived latency and computational overhead.
This work presents a comprehensive overview of the TSN deployment lifecycle, current challenges, limitations of existing tools, and future research directions for TSN deployment and management, and identifies key research gaps from a deployment perspective and provides guidance for the development of next-generation deployable TSN networks.
Rubi Debnath, Paul Pop, Silviu S. Craciunas et al.· 0 citations
The deployment of microservices in edge environments is increasingly critical to support latency-sensitive and data-intensive applications such as IoT analytics, real-time monitoring, and smart city services. Edge infrastructures are highly heterogeneous in terms of compute capacity, communication latency, and energy efficiency. Furthermore, the geographical distance between edge nodes may introduce non-negligible energy consumption for data transfers, which is often overlooked in traditional placement strategies. While prior works focus on minimizing response time, there is a need to adapt and extend such strategies for energy efficiency in edge computing. This paper proposes an Energy-Latency Trade-off Optimization Strategy (ELTOS) for microservice placement in edge environments. ELTOS formulates and solves a cost-based optimization for microservice placement on heterogeneous edge servers. The main goal of this optimization algorithm is to minimize the transmitted data size between microservices on different edge servers, saving energy while ensuring a suitable level of end-to-end latency for each request. Experimental evaluations using real edge computing infrastructure demonstrate the efficiency of the proposed ELTOS compared with previous work. A comparison result of energy consumption shows that ELTOS outperforms the previous method in 70% of all pairwise comparisons; therefore, ELTOS consumes less energy while maintaining QoS performance level. ELTOS enhances the median, 95th, and 99th percentile response times on average by 6.03%, 7.47%, and 3.30%, respectively.
Ida Falco, A. K. Idrees, Carmine Colarusso et al.· International Conference on...· 0 citations
As computing resources in cloud environments become increasingly abundant, executing complex scientific workflows on large-scale cloud infrastructure has become a standard practice. However, communication-intensive workflows face two fundamental bottlenecks. First, the lack of physical topology awareness often forces high-frequency interacting microservices to be placed on geographically distant nodes, which generates excessive cross-node communication overhead, leads to network load imbalance, and increases latency. Second, the prohibitive online computation time of conventional iterative scheduling algorithms further degrades response speed, making them unsuitable for real-time scenarios. To address these bottlenecks, this paper proposes AMSche, a framework for microservice deployment and task scheduling that is aware of both position and topology. The framework comprises two core mechanisms. The first mechanism, position-aware service deployment, colocates high-frequency interacting services on the same physical node based on communication affinity, thereby compressing cross-node communication overhead at the physical level. The second mechanism, topology-aware task scheduling, leverages online topology feature similarity mapping to instantly reuse historical scheduling plans, achieving scheduling decisions at the millisecond level. Extensive experiments on real-world scientific workflow datasets demonstrate that AMSche achieves an average improvement of 16.19% to 39.15% over existing baseline methods in comprehensive metrics including response time, total communication volume, and network load balance.
Hao Wei, Hailiang Chen, Jia-Nan Sun et al.· Fall Joint Computer Conferen...· 0 citations
ADASCALE is an adaptive framework that jointly scales and places microservice replicas under multi-dimensional dynamics that consistently meets SLO targets and improves both latency and throughput.
Ming Chen, Muhammed Tawfiqul Islam, M. R. Read et al.· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· Computers· 0 citations