Dynamic Resource Discovery and Management for Edge Computing Based on SPF for HADR Operations

The Smart City concept tries to inherit the advantages of Internet-of-Things (IoT) into its realm to function alongside the existing legacy systems. One of the most promising aspects of IoT is Edge Computing, which tries to move the computing, traditionally done via a centralized infrastructure like the cloud to the edge of the network. This allows remote deployment of IoT assets closer to the source and application area of information enabling faster response times of action. Smart Cities of future envision using Edge Computing to their advantage for remote and distributed computing. Sieve, Process and Forward (SPF) is an Edge Computing solution for dynamic IoT applications for Smart City scenarios. The military is looking forward to use, as well as develop the SPF platform for its Edge Computing requirements. But currently, the SPF platform does not have the mechanism for remote discovery of edge resources and their management to leverage its potential completely. This paper tries to propose a resource discovery and management architecture and methodology for SPF to support future Human Assistance and Disaster Recovery (HADR) operations in Smart City environments with the vision of enabling interoperability between civilian and military platforms

An Information-Centric Platform for Social- and Location-Aware IoT Applications in Smart Cities

Recent advances in Smart City infrastructures and the Internet of Things represent a significant opportunity to improve people's quality of life. Corresponding research often focuses on Cloud-centric network architectures where sensor devices transfer collected data to the Cloud for processing. However, the formidable traffic generated by countless IoT devices and the need for low-latency services raise the need to move away from centralized architectures and bring the computation closer to the data sources. To this end, this paper discusses SPF, a middleware solution that supports IoT application development, deployment, and management. SPF runs IoT services on capable devices located at the network edge and proposes an information-centric programming model that takes advantage of decentralized computation resources located in the proximity of application users and data sources. SPF also adopts Value-of-Information based methods to prioritize the transmission of essential information

Considerations on the Adoption of Named Data Networking (NDN) in Tactical Environments

Mobile military networks are uniquely challenging to build and maintain, because of their wireless nature and the unfriendliness of the environment, resulting in unreliable and capacity limited performance. Currently, most tactical networks implement TCP/IP, which was designed for fairly stable, infrastructure-based environments, and requires sophisticated and often application-specific extensions to address the challenges of the communication scenario. Information Centric Networking (ICN) is a clean slate networking approach that does not depend on stable connections to retrieve information and naturally provides support for node mobility and delay/disruption tolerant communications - as a result it is particularly interesting for tactical applications. However, despite ICN seems to offer some structural benefits for tactical environments over TCP/IP, a number of challenges including naming, security, performance tuning, etc., still need to be addressed for practical adoption. This document, prepared within NATO IST-161 RTG, evaluates the effectiveness of Named Data Networking (NDN), the de facto standard implementation of ICN, in the context of tactical edge networks and its potential for adoption

Efficient microservice deployment in Kubernetes multi-clusters through reinforcement learning

Microservices have revolutionized application deployment in popular cloud platforms, offering flexible scheduling of loosely-coupled containers and improving operational efficiency. However, this transition made applications more complex, consisting of tens to hundreds of microservices. Efficient orchestration remains an enormous challenge, especially with emerging paradigms such as Fog Computing and novel use cases as autonomous vehicles. Also, multi-cluster scenarios are still not vastly explored today since most literature focuses mainly on a single-cluster setup. The scheduling problem becomes significantly more challenging since the orchestrator needs to find optimal locations for each microservice while deciding whether instances are deployed altogether or placed into different clusters. This paper studies the multi-cluster orchestration challenge by proposing a Reinforcement Learning (RL)-based approach for efficient microservice deployment in Kubernetes (K8s), a widely adopted container orchestration platform. The study demonstrates the effectiveness of RL agents in achieving near-optimal allocation schemes, emphasizing latency reduction and deployment cost minimization. Additionally, the work highlights the versatility of the DeepSets neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. Results show that DeepSets algorithms optimize the placement of microservices in a multi-cluster setup 32 times higher than its trained scenario

Adaptive and business-driven service placement in federated Cloud computing environments

The emergence of large-scale federated Cloud computing environments and of dynamic resource pricing schemes presents interesting saving opportunities for service providers, that could dynamically change the placement of IT service components in order to reduce their bills. However, that calls for smart management solutions able to respond to pricing changes by dynamically reconfiguring IT service component placement in federated Cloud environments so to enforce highlevel business objectives defined by the service providers. This paper proposes a novel adaptive and business-driven IT service component reconfiguration solution based on what-if scenario analysis and on genetic-algorithm optimization. Our solution is able to model complex Cloud computing IT services and to evaluate their performance in a wide range of alternative configurations, by also detecting the optimal placement for their components. The paper presents the experimental evaluation of our framework in a realistic scenario that consists of a 2-tier service architecture with real-world pricing schemes. The results demonstrate the effectiveness of our solution and the suitability of business-driven IT management techniques for the optimal placement of service components in federated Clouds

Adaptive and business-driven service placement in federated Cloud computing environments

he emergence of large-scale federated Cloud computing environments and of dynamic resource pricing schemes presents interesting saving opportunities for service providers, that could dynamically change the placement of IT service components in order to reduce their bills. However, that calls for smart management solutions able to respond to pricing changes by dynamically reconfiguring IT service component placement in federated Cloud environments so to enforce highlevel business objectives defined by the service providers. This paper proposes a novel adaptive and business-driven IT service component reconfiguration solution based on what-if scenario analysis and on genetic-algorithm optimization. Our solution is able to model complex Cloud computing IT services and to evaluate their performance in a wide range of alternative configurations, by also detecting the optimal placement for their components. The paper presents the experimental evaluation of our framework in a realistic scenario that consists of a 2-tier service architecture with real-world pricing schemes. The results demonstrate the effectiveness of our solution and the suitability of business-driven IT management techniques for the optimal placement of service components in federated Clouds

The ubiQoS Middleware for Audio Streaming to Bluetooth Devices

The full and seamless integration of wireless devices with traditional fixed networks is more and more important to foster the mobile and ubiquitous access to the Internet. In particular, the heterogeneity and resource limitations of wireless devices motivate novel support infrastructures that can facilitate the wired-wireless integration and can provide service tailoring depending on client characteristics. The paper presents an application-level portable middleware, called ubiQoS, for QoS-enabled audio streaming to Bluetooth clients. ubiQoS exploits support proxies for QoS tailoring and for managing the QoS over the last segment of the audio distribution path towards the clients, by using different types of Bluetooth links. Proxies execute at the wired-wireless network edges and can even migrate to follow the device movements, where and when needed. The reported experimental results show the feasibility of the application-level approach in the challenging case of QoS-enabled audio streaming to resource-limited Bluetooth devices

Middleware-level QoS Differentiation in the Wireless Internet: the ubiQoS Solution for Audio Streaming over Bluetooth

The ultimate goal of mobile and ubiquitous Internet accessibility is not only the seamless integration of wireless devices with traditional fixed networks but also the dynamic differentiation of the provided levels of Quality of Service (QoS) depending on client characteristics. In this context, the paper presents the provisioning of audio streaming with different QoS levels in the application-level ubiQoS middleware. In particular, it focuses on how ubiQoS manages the QoS over the last segment of the audio distribution path towards Bluetooth clients by allocating different types of Bluetooth communication channels (unicast connection-oriented or broadcast connectionless) depending on the differentiated QoS re-quirements of different user classes. To this purpose, we have designed and implemented a library that extends the JSR82 standard specification with the support of Active Slave Broadcast, thus simplifying the Java-based management of Bluetooth communications. The reported experimental results show the feasibility of our application-level middleware approach in the challenging case of audio streaming with differentiated QoS to resource-limited Bluetooth devices

Business-Driven Optimization of Component Placement for Complex Services in Federated Clouds

With the advent of connected services ecosystems, new generations of services and systems are being conceived, responding to the ever growing demands of the market place. In parallel, the effective adoption of the Cloud computing paradigm is becoming an essential enabler for business enterprises. With such importance placed on the services ecosystem, the design and management of services becomes a key issue both for the providers and the users. One of the main challenges for service providers lies in the complexity of the services, comprising of a multiplicity of technologies and competing and cooperating providers, which is difficult to address through current technology-centric service design approaches, in particular for the deployment infrastructure. The work described in this paper lays a foundation for business driven service design by proposing a business goals driven model of resource allocation in the Cloud. We define a goal/loss/processing cost function for resource allocation that we optimize, while taking into account the dynamic and varying nature of requests load

Potential Benefits and Challenges of Closed-Loop Optimization Processes for IT Support Organizations

IT services are getting increasingly complicated, and require IT support organization to manage them. IT support organization are in charge of the incident management process and represent mission-critical structures whose performance needs to be frequently assessed and optimized. State-of-the-art research in the performance optimization of IT support organization proposes user-driven performance assessment and optimization processes based on what-if scenario analysis tools that implement sophisticated IT support organization models. This manuscript instead represents a preliminary study of a different kind of optimization processes, of the closed-loop type, that try to autonomously identify optimal IT support organization configurations according to inputs provided by the user. This paper discusses the development challenges in realizing decision support tools for closed-loop optimization processes and presents a prototype system. The preliminary evaluation of our tool demonstrates that closed-loop processes might be impractical as reference tools but can effectively complement and extend human-driven ones