Multi-edge cooperative computing that combines constrained resources of
multiple edges into a powerful resource pool has the potential to deliver great
benefits, such as a tremendous computing power, improved response time, more
diversified services. However, the mass heterogeneous resources composition and
lack of scheduling strategies make the modeling and cooperating of multi-edge
computing system particularly complicated. This paper first proposes a
system-level state evaluation model to shield the complex hardware
configurations and redefine the different service capabilities at heterogeneous
edges. Secondly, an integer linear programming model is designed to cater for
optimally dispatching the distributed arriving requests. Finally, a
learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS
embeds the real-time states of multi-edge system and requests information, and
combines the embeddings with a policy network to schedule the requests, so that
the response time of all requests can be minimized. Evaluation results verify
that CoRaiS can make a high-quality scheduling decision in real time, and can
be generalized to other multi-edge computing system, regardless of system
scales. Characteristic validation also demonstrates that CoRaiS successfully
learns to balance loads, perceive real-time state and recognize heterogeneity
while scheduling.

多边协作计算利用多个边缘的受限资源构建强大的资源池，带来了巨大的计算能力、响应时间改善和多样化的服务，但多边计算系统的资源组成和缺乏调度策略使其建模和协作变得尤为复杂。本文首先提出了一个系统级状态评估模型，用于屏蔽复杂的硬件配置并重新定义异构边缘的不同服务能力；其次，设计了一个整数线性规划模型，用于优化分配分布式到达的请求；最后，提出了一种基于学习的轻量级实时调度器 CoRaiS，该调度器将多边系统的实时状态和请求信息结合起来，通过策略网络调度请求，以最小化所有请求的响应时间。评估结果验证了 CoRaiS 能够实时做出高质量的调度决策，并可以推广到其他多边计算系统，无论系统规模如何。特性验证还证明 CoRaiS 成功学习了负载平衡、实时状态感知和异构性识别的调度能力。

CoRaiS: 轻量级实时多边缘合作计算调度器

CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative  Computing

Energy storage systems (ESS) are pivotal component in the energy market,
serving as both energy suppliers and consumers. ESS operators can reap benefits
from energy arbitrage by optimizing operations of storage equipment. To further
enhance ESS flexibility within the energy market and improve renewable energy
utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which
leverages the unique characteristics of battery energy storage (BES) and
hydrogen energy storage (HES). For scheduling tasks of the heterogeneous
PV-ESS, cost description plays a crucial role in guiding operator's strategies
to maximize benefits. We develop a comprehensive cost function that takes into
account degradation, capital, and operation/maintenance costs to reflect
real-world scenarios. Moreover, while numerous methods excel in optimizing ESS
energy arbitrage, they often rely on black-box models with opaque
decision-making processes, limiting practical applicability. To overcome this
limitation and enable transparent scheduling strategies, a prototype-based
policy network with inherent interpretability is introduced. This network
employs human-designed prototypes to guide decision-making by comparing
similarities between prototypical situations and encountered situations, which
allows for naturally explained scheduling strategies. Comparative results
across four distinct cases underscore the effectiveness and practicality of our
proposed pre-hoc interpretable optimization method when contrasted with
black-box models.

能源储存系统是能源市场中至关重要的组成部分，本研究提出了一种异质光伏 - 能源储存系统，利用了电池能量存储和氢能存储的特性，通过开发全面的成本函数和基于人为设计的原型网络，实现了透明的调度策略并取得了令人满意的效果。

优化异构能源存储系统的可解释深度强化学习

Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous  Energy Storage Systems

The demand for real-time cloud applications has seen an unprecedented growth
over the past decade. These applications require rapidly data transfer and fast
computations. This paper considers a scenario where multiple IoT devices update
information on the cloud, and request a computation from the cloud at certain
times. The time required to complete the request for computation includes the
time to wait for computation to start on busy virtual machines, performing the
computation, waiting and service in the networking stage for delivering the
output to the end user. In this context, the freshness of the information is an
important concern and is different from the completion time. This paper
proposes novel scheduling strategies for both computation and networking
stages. Based on these strategies, the age-of-information (AoI) metric and the
completion time are characterized. A convex combination of the two metrics is
optimized over the scheduling parameters. The problem is shown to be convex and
thus can be solved optimally. Moreover, based on the offline policy, an online
algorithm for job scheduling is developed. Numerical results demonstrate
significant improvement as compared to the considered baselines.

本文研究了实时云应用中基于 IoT 设备更新信息的计算任务调度问题，并提出了基于信息新鲜度和完成时间的调度策略，以及优化这两个指标的凸优化模型和在线算法。模拟实验显示了该算法相比于基线方法的显著改进。