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.
Abstract
Microservice applications are increasingly deployed across cloud--edge environments, where heterogeneous nodes and time-varying inter-node delays amplify the impact of placement decisions. At the same time, these applications face non-stationary traffic, shifts in the mix of root request operations that exercise different call graphs, and heterogeneous communication modes that determine how network latency and queuing propagate to end-to-end (E2E) performance. Existing autoscalers and network-aware schedulers typically handle only a subset of these dynamics, leading to either compute bottlenecks or inflated cross-node latency and thus SLO violations. We propose ADASCALE, an adaptive framework that jointly scales and places microservice replicas under such multi-dimensional dynamics. ADASCALE implements a Monitor--Analyzer--Planner--Executor (MAPE) loop that extracts per-edge and per-service demand from distributed traces and service-mesh metrics, identifies the most critical root operation under a mixed workload, computes SLO-aware replica targets, and then places replicas to minimize a demand-weighted latency objective given the current inter-node latency matrix. To react quickly to networking perturbations, ADASCALE triggers a reactive placement loop, while a steady-state autoscaling loop handles demand shifts. We evaluate ADASCALE on a cloud--edge Kubernetes cluster using the DeathStarBench Social Network application with three root operations under varying load and workload mixes. Across scenarios, ADASCALE consistently meets SLO targets and improves both latency and throughput: compared with NetMARKS_Scale, it achieves up to 1.56x, 1.93x, and 1.34x lower average response time (for compose-post, read-home-timeline, and read-user-timeline) and up to 2.16x, 1.32x, and 1.36x higher throughput, respectively.
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.
Madhura Adeppady, Yenchia Yu, Ali Rahmanian et al.· IEEE Transactions on Network...· 0 citations
The proliferation of cloud-native microservice architectures has fundamentally transformed enterprise software delivery, yet the accompanying inter-service communication overhead constitutes a dominant source of end-to-end latency, often jeopardising stringent service level objectives (SLOs). Existing mitigation strategies ranging from static load balancing to threshold-based horizontal pod autoscaling remain predominantly reactive and topology-agnostic, failing to exploit the rich structural and temporal signals inherent in the microservice call graph. This paper presents GAL-RL (Graph Attention Latency Reinforcement Learner), a novel framework that tightly couples multi-head graph attention networks with a continuous-action Soft Actor-Critic agent to jointly optimise peredge traffic routing and per-service replica scaling. The GAT encoder learns predictive latency embeddings by attending over spatio-temporal neighbourhoods in the service mesh, while the SAC policy translates these embeddings into fine-grained resource orchestration decisions that balance tail-latency reduction against compute expenditure. Evaluated on the DeathStarBench social-network workload and the PetShop anomaly-injection benchmark, GAL-RL reduces 95th-percentile latency by 42 % and CPU utilization by 23 % compared with Kubernetes HPA, while maintaining a 2.1 % SLO violation rate. Ablation studies confirm that both the graph attention mechanism and the joint routing-scaling formulation are important for achieving these performance gains.
Mathivanan Durai, Madabhushana Pranitha· 2026 6th International Confe...· 0 citations
iFogSim-placement, a framework designed for experimenting with self-adaptive service placement, is introduced, demonstrating its ability to capture performance metrics, including application latency, resource usage, and energy consumption.
Joseph Poon, Christian Cabrera, N. D. Lawrence· SEAMS@ICSE· 0 citations
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
O-DAG is presented, an end-to-end framework that closes the SAGA--simulation gap and evaluates five scheduling algorithms for a slice scheduling application across various configurations spanning 5K--50K UEs, 2--20 cells, and 2--10 network slices.
Y. Hwang, B. Krishnamachari· International Conference on...· 0 citations
TADOF is a dynamic optimization framework that jointly performs energy-aware elastic scaling and task migration under drift-aware periodic modeling and burst detection and observed about 18–34% lower energy and 15–28% lower migration cost than threshold and A2C/TD3 baselines while preserving QoS.
Juan Guo, Yanqun Zuo, Zhixian Chang· Cluster Computing· 0 citations