Optimization | Junfei Zhan's Website

Minimizing Maximum Age of Service in Virtualized Green IoT Networks

Sat, 07 Dec 2024 00:00:00 +0000

This project addresses the challenge of embedding and scheduling applications in solar-powered green IoT networks, with the goal of minimizing the maximum Age of Service (AoS) — a freshness metric indicating the delay between data generation and service completion.

Objectives

The research focuses on virtualized, computation-enabled IoT infrastructures powered by renewable energy (solar). The applications are modeled as Directed Acyclic Graphs (DAGs) with Virtual Network Functions (VNFs) that must be executed under fluctuating energy and computational constraints.

Key Contributions

Mixed Integer Linear Programming (MILP) Formulation

Proposed the first MILP model to jointly optimize:
- Device selection and sampling time
- DAG request embedding decision
- Energy consumption at devices, gateways, and servers
Objective: minimize the maximum AoS across all DAG requests.

Heuristic and Predictive Control Solutions

Developed GreedyOL, a fast heuristic that embeds DAGs based on current AoS.
Proposed RHCOP, a Receding Horizon Control Optimization framework:
- Utilizes Gaussian Mixture Models (GMMs) to predict solar energy arrivals and wireless channel gains.
- Enables real-time scheduling using only causal (non-future) information.

Results & Insights

RHCOP achieves a 1.07× and GreedyOL a 1.13× min-max AoS compared to optimal MILP.
More gateways and servers reduce AoS due to enhanced redundancy and flexibility.
Equal numbers of VNF-Cs (collection) and VNF-Ps (processing) yield optimal freshness.

Broader Impact

The proposed system lays groundwork for energy-aware, delay-sensitive IoT applications, especially in remote or energy-constrained environments. The results provide insights into the tradeoffs between computation freshness, resource allocation, and green network deployment strategies.

📄 [IEEE Transactions on Services Computing Submission] — Coming Soon

Minimizing Maximum Age of Service in Virtualized Green IoT Networks

Sat, 07 Dec 2024 00:00:00 +0000

本项目解决了在太阳能驱动的绿色 IoT 网络中嵌入和调度应用的挑战，目标是最小化最大 Age of Service (AoS) — 一个表示数据生成到服务完成之间延迟的新鲜度指标。

目标

本研究聚焦于由可再生能源（太阳能）驱动的虚拟化、具备计算能力的 IoT 基础设施。应用被建模为包含 Virtual Network Functions (VNFs) 的 Directed Acyclic Graphs (DAGs)，需要在波动的能量和计算约束下执行。

主要贡献

Mixed Integer Linear Programming (MILP) 建模

提出了首个 MILP 模型，联合优化：
- 设备选择与采样时间
- DAG 请求嵌入决策
- 设备、网关和服务器的能耗
目标：最小化所有 DAG 请求的最大 AoS。

启发式与预测控制方案

开发了 GreedyOL，一种基于当前 AoS 嵌入 DAG 的快速启发式算法。
提出了 RHCOP，一种 Receding Horizon Control Optimization 框架：
- 利用 Gaussian Mixture Models (GMMs) 预测太阳能到达量和无线信道增益。
- 仅使用因果（非未来）信息实现实时调度。

结果与洞察

RHCOP 实现了最优 MILP 的 1.07 倍 min-max AoS，GreedyOL 为 1.13 倍。
更多的网关和服务器由于增强的冗余性和灵活性而降低了 AoS。
VNF-C（采集）和 VNF-P（处理）数量相等时可获得最优新鲜度。

更广泛的影响

所提出的系统为能耗感知、延迟敏感的 IoT 应用奠定了基础，尤其适用于偏远或能源受限的环境。研究结果揭示了计算新鲜度、资源分配与绿色网络部署策略之间的权衡关系。

[IEEE Transactions on Services Computing 投稿] — 即将发表

Undergraduate Thesis - Scheduling in Serverless Computing for Solar-Powered IoT Networks

Tue, 30 Apr 2024 00:00:00 +0000

This undergraduate thesis proposes a two-phase optimization framework for solar-powered IoT networks, focusing on dynamic task allocation and energy-aware function configuration. A MILP benchmark and a GMM-enhanced Receding Horizon Control algorithm were developed to improve efficiency and adapt to fluctuating energy and computation conditions.

本科毕业论文 - Scheduling in Serverless Computing for Solar-Powered IoT Networks

Tue, 30 Apr 2024 00:00:00 +0000

本科毕业论文提出了一种面向太阳能供电 IoT 网络的两阶段优化框架，重点研究动态任务分配和能量感知的函数配置。开发了 MILP 基准测试和基于 GMM 增强的 Receding Horizon Control 算法，以提升效率并适应波动的能量和计算条件。

Task Offloading and Approximate Computing in Solar Powered IoT Networks

Sun, 07 Jan 2024 00:00:00 +0000

This research proposes a novel framework for minimizing the total energy consumption of solar-powered IoT networks through task offloading and approximate computing. Devices can choose between local execution (exact or approximate) or offloading tasks to a solar-powered edge server.

Core Objectives

Reduce energy usage by allowing approximate task execution when tolerable errors are acceptable.
Leverage digital twins (DTs) to estimate future energy availability and channel conditions.
Optimize offloading decisions and resource allocation across time slots and channels.

Technical Highlights

MILP Formulation

Designed the first MILP to jointly optimize:
- Task offloading decisions
- Approximate vs. exact execution
- Channel allocation
- Virtual machine (VM) assignment
Captures constraints on energy arrivals, CPU cycles, approximation error bounds, and VM capacity.

DT-Assisted Receding Horizon Control (DT-RHC)

Introduced a DT-based control algorithm using:
- Gaussian Mixture Models (GMMs) to predict energy and channel gain
- Sliding-window MILP optimization for dynamic scheduling
Achieves energy usage within 1.62× of MILP optimal while requiring only causal (past) data

Results & Evaluation

DT-RHC significantly outperforms random strategies across metrics such as:
- Energy consumption vs. number of devices
- Impact of approximation ratios
- Task completion within extended time horizons
Simulations conducted in Python + Gurobi over 100×100 m² deployments using realistic solar input and wireless models.

Conclusion

This study demonstrates the viability of integrating approximate computing and intelligent offloading in renewable-powered IoT environments. It provides a robust foundation for future distributed optimization and adaptive energy-aware network control.

IEEE Paper DOI: 10.1109/LNET.2023.3328893

Task Offloading and Approximate Computing in Solar Powered IoT Networks

Sun, 07 Jan 2024 00:00:00 +0000

本研究提出了一种新颖的框架，通过任务卸载和近似计算来最小化太阳能驱动 IoT 网络的总能耗。设备可以选择本地执行（精确或近似）或将任务卸载到太阳能驱动的边缘服务器。

核心目标

在可容忍误差的情况下，通过允许近似任务执行来降低能耗。
利用 Digital Twin (DT) 估计未来的能量可用性和信道条件。
优化卸载决策以及跨时隙和信道的资源分配。

技术亮点

MILP 建模

设计了首个 MILP，联合优化：
- 任务卸载决策
- 近似与精确执行
- 信道分配
- 虚拟机（VM）分配
捕获了能量到达、CPU 周期、近似误差界和 VM 容量等约束条件。

DT 辅助的滑动窗口控制 (DT-RHC)

引入了基于 DT 的控制算法，使用：
- Gaussian Mixture Models (GMMs) 预测能量和信道增益
- 滑动窗口 MILP 优化实现动态调度
仅使用因果（历史）数据即可实现 MILP 最优值 1.62 倍以内的能耗

结果与评估

DT-RHC 在以下指标上显著优于随机策略：
- 能耗与设备数量的关系
- 近似比率的影响
- 扩展时间范围内的任务完成率
仿真在 100×100 m² 部署上使用 Python + Gurobi 进行，采用真实的太阳能输入和无线模型。

结论

本研究证明了在可再生能源驱动的 IoT 环境中集成近似计算和智能卸载的可行性。它为未来的分布式优化和自适应能耗感知网络控制提供了坚实基础。

IEEE Paper DOI: 10.1109/LNET.2023.3328893