Research Archive

Journal Article Open Access Edge Computing

Edge-Cloud Continuum Computing: Task Offloading Optimization, Latency-Aware Scheduling, and Mobility-Driven Workload Migration in 5G-Enabled Mobile Edge Environments

Mobile Edge Computing (MEC) in 5G-enabled environments introduces a computational continuum spanning ultra-low-latency edge nodes, regional fog nodes, and centralized cloud data centers, enabling latency-sensitive applications -- including augmented reality, industrial automation, and autonomous vehicle coordination -- that cannot tolerate cloud-only round-trip latencies. Optimal task placement across this continuum requires dynamic offloading decisions that account for task computational requirements, data transfer costs, edge node capacity, user mobility patterns, and SLA constraints simultaneously. This paper presents EdgeOpt, a multi-objective task offloading optimization framework for 5G MEC environments that employs a Deep Q-Network (DQN) agent trained to balance execution latency, energy consumption, and edge resource utilization in real-time. EdgeOpt is evaluated in a 5G MEC testbed comprising three edge nodes, one fog aggregation layer, and simulated cloud infrastructure, processing workloads representative of AR rendering, industrial sensor fusion, and V2X communication scenarios. EdgeOpt achieves 38% lower mean task execution latency and 27% lower edge energy consumption compared to greedy offloading baselines, while maintaining edge utilization above 78% under high mobility scenarios. We characterize the mobility-induced workload migration problem and introduce the Mobility-Aware Migration Cost Model (MAMCM) to quantify handover-induced service disruption risk. This work provides architectural and algorithmic foundations for latency-optimized edge-cloud continuum orchestration.

Adekunle Fashola, Emma Svensson, Yusuke Watanabe, Heba Mansour· Oct 2020· 318 citations

Journal Article Subscription Software Engineering

Value Stream Mapping for DevOps: Identifying and Eliminating Waste in Software Delivery Pipelines Using Lean Principles

Value Stream Mapping (VSM), a lean manufacturing technique, has been increasingly advocated as a tool for visualizing and optimizing software delivery pipelines in DevOps contexts. However, empirical evidence on its effectiveness and practical application nuances in software organizations remains sparse. This paper reports an action research study conducted across two organizations — one in telecommunications and one in retail banking — in which cross-functional teams applied VSM to their end-to-end software delivery processes over a 12-month period. We adapt traditional VSM notation to account for software-specific waste categories: unplanned work, context switching, approval bottlenecks, environment contention, and test instability. Our findings reveal that approval bottlenecks and environment contention account for 58% of total lead time waste across both organizations. Following VSM-guided interventions, the telecommunications organization reduced pipeline lead time from 34 days to 9 days, while the banking organization reduced its from 48 days to 14 days. We derive eight VSM adaptation principles for software delivery contexts and propose a Digital VSM notation standard compatible with DevOps toolchain data extraction. This work demonstrates that lean thinking remains powerfully applicable in digital delivery contexts when appropriately adapted.

Chioma Ezenwachi, Henrik Lindqvist, Rahul Bose, Theresa MacGregor· Oct 2020· 303 citations

Journal Article Subscription Cloud Computing

Multi-Cloud DevOps: Portability Architectures, Vendor Lock-in Mitigation Strategies, and Operational Complexity in Heterogeneous Cloud Environments

As organizations distribute workloads across multiple cloud providers to optimize cost, latency, regulatory compliance, and vendor risk, their DevOps pipelines must increasingly operate across heterogeneous cloud environments. Multi-cloud DevOps introduces significant engineering challenges: toolchain fragmentation, authentication model divergence, network topology complexity, and observability aggregation across provider-siloed data streams. This paper presents the first systematic empirical study of multi-cloud DevOps, examining 11 organizations operating production workloads across two or more major cloud providers. Through interviews (n=67), pipeline architecture analysis, and an industry survey (n=412), we identify and characterize three multi-cloud DevOps architectural patterns — Cloud-Agnostic Abstraction, Provider-Native Federation, and Workload Partitioning — and evaluate their trade-offs across portability, operational complexity, and performance dimensions. We find that the Cloud-Agnostic Abstraction pattern, typically implemented through Terraform with provider-agnostic modules and cloud-neutral container orchestration, achieves the highest portability score but incurs a 34% higher operational complexity rating than single-cloud environments. We introduce the Multi-Cloud Operational Overhead Index (MOOI) and provide a decision framework for selecting multi-cloud architecture patterns based on organizational maturity, compliance requirements, and engineering capacity.

Adaeze Okonkwu, Marcus Lindblom, Akihiro Watanabe, Clara Rodrigues· Jul 2020· 298 citations

Journal Article Open Access Bioinformatics

Single-Cell RNA Sequencing Data Analysis Pipelines: Scalable Dimensionality Reduction, Cell Type Clustering, and Trajectory Inference for Million-Cell Atlas Construction

Single-cell RNA sequencing (scRNA-seq) has transformed cell biology by enabling genome-wide transcriptomic profiling at single-cell resolution, but the computational pipelines required to process, normalize, cluster, and interpret datasets at the scale of million-cell atlases demand engineering solutions that go substantially beyond the academic prototype tools in common use. This paper presents ScaleSC, a horizontally scalable scRNA-seq analysis pipeline designed for cloud cluster deployment, and evaluates its performance on datasets ranging from 10,000 to 4.2 million cells against the Seurat, Scanpy, and RAPIDS cuML pipelines. ScaleSC implements distributed PCA using a randomized SVD algorithm that scales linearly with cell count on Apache Spark clusters, a GPU-accelerated UMAP implementation achieving 18x speedup over CPU UMAP at one million cells, and a graph-based clustering module supporting both Leiden and Louvain algorithms with adaptive resolution selection. On the 4.2-million-cell Human Cell Atlas bone marrow dataset, ScaleSC completes the full analysis pipeline in 47 minutes on a 32-node cluster, compared to 14.3 hours for Scanpy on the same hardware. Cell type assignment accuracy (benchmarked against expert-annotated ground truth labels) is 94.2 percent using ScaleSC marker-gene transfer, versus 91.8 percent for Seurat v4 label transfer. We release ScaleSC as open-source software with Docker-based deployment pipelines and cloud infrastructure templates for AWS, GCP, and Azure.

Chukwuebuka Aneke, Astrid Karlsson, Masashi Yamada, Amira El-Sayed· Jul 2020· 389 citations

Journal Article Open Access Artificial Intelligence

Machine Learning-Augmented DevOps: Automated Anomaly Detection and Predictive Incident Management in High-Velocity Deployment Environments

The increasing velocity of software deployments enabled by mature CI/CD practices has outpaced the capacity of human operators to detect and respond to production incidents through manual monitoring. This paper explores the integration of machine learning techniques into DevOps operational pipelines — an emerging discipline termed AIOps — with particular focus on anomaly detection and predictive incident management. We present ML-DevOps, a reference architecture that integrates unsupervised anomaly detection models (Isolation Forest, LSTM Autoencoders) with supervised incident classifiers into a continuous delivery pipeline. The architecture is evaluated using a real-world dataset comprising 14 months of telemetry from a large e-commerce platform processing over 2 million daily transactions. ML-DevOps achieves a 91.3% anomaly detection precision and a 5.2-minute mean advance warning time before customer-impacting incidents, representing an 82% improvement over threshold-based alerting baselines. We further analyze model drift in the context of continuous deployment, demonstrating that retraining frequency must scale with deployment frequency to maintain detection accuracy. This work bridges the gap between machine learning research and DevOps practice, providing both an architectural blueprint and empirical evidence for AIOps integration.

Victoria Osei, Daniel Reinhardt, Yuki Tanaka, Fatima Al-Rashidi· Apr 2020· 589 citations

Journal Article Subscription Cybersecurity

Zero Trust Architecture Implementation in Enterprise Networks: A Comparative Study of Identity-Centric, Microsegmentation, and Software-Defined Perimeter Approaches

Zero Trust Architecture (ZTA) has emerged as the prevailing framework for enterprise network security in the aftermath of high-profile perimeter breach incidents, premised on the principle that no user, device, or network segment should be implicitly trusted regardless of its position relative to the network boundary. Despite widespread adoption of ZTA terminology, organizational implementations vary dramatically in scope, completeness, and operational effectiveness. This paper presents a comparative analysis of three primary ZTA implementation approaches -- Identity-Centric ZTA (exemplified by Google BeyondCorp), Microsegmentation-Based ZTA (exemplified by Illumio and Guardicore), and Software-Defined Perimeter ZTA (exemplified by Zscaler and Cloudflare Access) -- across eight enterprise deployments. Using a standardized ZTA Maturity Assessment Instrument (ZTMAI) comprising 47 control dimensions mapped to NIST SP 800-207, we evaluate each deployment against access policy granularity, continuous authentication coverage, lateral movement containment, data exfiltration prevention effectiveness, and operational complexity. Identity-Centric implementations achieve the highest ZTMAI scores in continuous authentication and data access governance dimensions but require substantial identity infrastructure investment. Microsegmentation delivers the strongest lateral movement containment (mean blast radius reduction of 91%) but incurs the highest policy management overhead. We provide a ZTA implementation selection framework based on organizational threat model, existing infrastructure, and engineering capacity.

Obiageli Chukwu, Malin Eriksson, Takuya Yamada, Amira El-Sayed· Apr 2020· 452 citations

Journal Article Open Access Quantum Computing

Quantum Error Correction Codes for Noisy Intermediate-Scale Quantum Devices: Implementation Trade-offs of Surface Codes, Steane Codes, and Bacon-Shor Codes on Superconducting Qubit Architectures

Quantum error correction (QEC) is widely recognized as a prerequisite for fault-tolerant quantum computation, yet the overhead requirements of leading QEC codes -- in terms of physical-to-logical qubit ratios and gate operation counts -- exceed the capabilities of current Noisy Intermediate-Scale Quantum (NISQ) devices by orders of magnitude. This paper investigates the implementation trade-offs of three QEC code families -- Surface Codes, Steane Codes, and Bacon-Shor Codes -- on superconducting transmon qubit architectures representative of current IBM Quantum and Google Sycamore hardware generations. Using a hardware-calibrated noise model derived from publicly available device characterization data, we simulate QEC circuit performance across logical qubit distances 3 through 9, measuring logical error rate suppression, syndrome extraction circuit depth, connectivity requirements, and decoding latency. Surface codes achieve the best logical error rate suppression per physical qubit overhead at distance 5 (logical error rate 2.3x10-4 at 0.1% physical gate error rate), but require all-nearest-neighbor connectivity that strains current device topologies. Bacon-Shor codes demonstrate the lowest syndrome extraction circuit depth, making them favorable for architectures with limited two-qubit gate fidelity. We introduce a QEC Code Suitability Index (QCSI) that maps device connectivity, gate fidelity, and coherence time profiles to code family recommendations, and apply it across six current quantum hardware platforms.

Chukwuemeka Obialo, Astrid Lindqvist, Hiroshi Nishimura, Rania Ahmed· Jan 2020· 387 citations

Journal Article Open Access Software Engineering

Shift-Left Performance Engineering: Integrating Load Testing, Profiling, and Performance Budgets into DevOps Pipelines

Performance degradation in production software systems frequently originates in code changes that pass functional testing but introduce latency regressions, memory leaks, or throughput reductions that only manifest at scale. Shift-left performance engineering addresses this through early, continuous performance validation integrated into CI/CD pipelines. This paper presents a comprehensive framework for shift-left performance engineering, grounded in a practitioner survey (n=319) and a longitudinal case study of four organizations that transitioned from periodic load testing to continuous pipeline-integrated performance validation. We define the Performance Gate Model, comprising three gate types — microbenchmark gates at unit level, service-level load test gates at integration level, and synthetic traffic replay gates at staging level — and demonstrate their complementary fault detection profiles. Our longitudinal analysis shows that organizations implementing all three gate types detect 89% of performance regressions before production deployment, compared to 31% for organizations relying solely on post-deployment monitoring. We evaluate toolchain options including k6, Gatling, Apache JMeter, and Locust for pipeline integration, comparing their CI/CD ergonomics, scripting model, and result visualization capabilities. Performance budget enforcement — defining and rejecting builds that violate response time, error rate, or throughput thresholds — is identified as the highest-leverage single practice, adopted by only 24% of surveyed organizations despite its measurable impact.

Olawale Adeyemi, Kristina Magnusson, Ryuichi Yamada, Inês Carvalho· Jan 2020· 334 citations