Many studies have proposed a peer-to-peer energy market where the prosumers’ actions, including energy consumption, charge and discharge schedule of energy storage systems, and transactions in local energy markets, are controlled by a central operator. In this paper, prosumers’ actions are not controlled by an operator, and the prosumers freely participate in the local energy market to trade energy with other prosumers. We designed and modeled a local energy market with a management algorithm that uses community energy storage for prosumers who competitively participate in trade in the real-time energy market. We propose an energy-trade management algorithm that manages the trades of prosumers in two phases based on bids and offers submitted by prosumers. The first phase is to manage the trade of prosumers who have submitted fair prices to trade with other prosumers in the real-time energy market. The second phase is managing the trade of prosumers that could not trade in the first phase. Community energy storage is employed in the second phase and controlled by a reinforcement learning-based trading algorithm to decide whether to buy, sell, or do nothing with the prosumers. The action of buying and selling means charging and discharging the community energy storage, respectively. Numerical results show that the proposed trading algorithm gains a near-maximum profit. Besides, we verified that community energy storage yields more profit than the battery wear-out cost.

As the modern computing market experiences a surge in demand for efficient data-management solutions, challenges posed by centralized storage systems become more pronounced, especially with the proliferation of Internet of Things devices. Centralized storage, although cost-effective, faces issues of scalability, performance bottlenecks, and security vulnerabilities. With decentralized storage, data are distributed across nodes, offering redundancy, data availability, and enhanced security. Unfortunately, decentralized storage introduces its own challenges, such as complex data retrieval processes, potential inconsistencies in data versions, and difficulties in ensuring data privacy and integrity in a distributed setup. Effectively managing these challenges calls for innovative techniques. In response, this paper introduces a decentralized storage system that melds cloud-native concepts with blockchain technology. The proposed design delivers enhanced scalability, data security, and privacy. When operating on a containerized edge infrastructure, this storage system provides higher data-transfer speeds than the interplanetary file system. This research thus blends the advantages of cloud-native frameworks with the security mechanisms of blockchain, crafting a storage system that addresses the present-day challenges of data management in decentralized settings.

Cloud-to-Cloud (C2C) migration enables the organizations to switch among various cloud environments without rebuilding the whole system on another cloud. Numerous virtual machines (VMs) placed in one cloud-computing environment during the C2C migration are moved to another. Service running on the multiple VMs becomes unavailable during the C2C migration because the conventional migration schemes stop the VMs for a while. The migration cost due to the increased downtime of the service deployed on the VMs may be increased by inappropriate C2C migration operation. We propose a service-aware strategy for C2C migration of services on multiple VMs, which analyzes the dependency of multiple VMs, using network traffic intensity to determine the migration sequence of dependent VMs in order to decrease the service downtime. We implement the proposed migration method in an OpenStackbased testbed environment. Our experimental results illustrate that the dependency among the VMs is successfully identified for C2C migration. The proposed method that exploits the dependency among the VMs significantly reduces the service downtime, while the service downtime increases exponentially, when the VMs are migrated randomly. In comparison with the random VM migration, the proposed scheme reduced the average service downtime by over 50% during the C2C migration.

Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention.

We propose a cloud migration strategy that migrates applications and services running on multiple virtual machines (VMs) from one cloud environment to another. Current migration techniques mostly focus on migrating VMs between physical servers that are placed inside a single cloud. In this work, we consider an automated cloud migration system that enables a cloud migration of multiple VMs between different cloud infrastructures. However, if multiple VMs are used to constitute a multi-tier web application, the cloud migration must consider any dependency among the VMs. Otherwise, it could cause a significant increase in downtime of the application during the migration. To reduce the migration downtime of applications, we propose a VM grouping scheme using principal component analysis (PCA) based on traffic dependencies. For our experiments, a cloud environment is built with OpenStack, which is an open source software for creating and managing VM-based cloud computing environments.

This paper proposes an energy storage system (ESS) management algorithm to reduce the peak electricity consumption for demand response (DR). In a building equipped with an ESS and a photovoltaic generator, the amount of energy charged into the ESS can be used to reduce the peak demand of the following day. The proposed algorithm calculates the feasible amount of peak demand reduction using the information about the amount of net energy demand and the energy charged in ESS, and provides the ESS charging and discharging schedule that can achieve the maximum reduction of the peak demand for DR requests. To examine the performance of the proposed algorithm, a case study was conducted using real-life data measured within a campus building.