Orchestration Policies Research Articles

LESS-ON: Load-aware edge server shutdown for energy saving in cellular networks

While advances in wireless networks enable novel services with previously unreachable latency guarantees, edge computing becomes essential for delivering computing resources close to the users and meeting the strict latency requirements. However, addressing the energy footprint of computing resources is crucial amid the pressing sustainability concerns. The energy consumption of idle resources accounts for a significant part of the total energy footprint. While server shutdown during low-demand periods is common in cloud computing, it is challenging to determine which edge servers to shut down and how to route requests due to the stringent latency requirements of the applications. Thus, this work formulates an optimal orchestration policy to minimize the energy consumption of the edge computing infrastructure and presents LESS-ON, a strategy with a polynomial time complexity that reduces the operational energy footprint of edge computing by shutting down edge servers during low-demand periods. In contrast to previous studies, LESS-ON considers the energy requirements associated with routing requests to the designated edge servers. Our numerical evaluation shows that LESS-ON reduces the total consumption by 42% with respect to the common always-on approach during low-demand periods and by 35% over 24 h, all while meeting latency requirements.

HeROsim: An Allocation and Scheduling Simulator for Evaluating Serverless Orchestration Policies

HeROsim: An Allocation and Scheduling Simulator for Evaluating Serverless Orchestration Policies

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

DRL-Based Joint Resource Allocation and Device Orchestration for Hierarchical Federated Learning in NOMA-Enabled Industrial IoT

Federated learning (FL) provides a new paradigm for protecting data privacy in Industrial Internet of Things (IIoT). To reduce network burden and latency brought by FL with a parameter server at the cloud, hierarchical federated learning (HFL) with mobile edge computing (MEC) servers is proposed. However, HFL suffers from a bottleneck of communication and energy overhead before reaching satisfying model accuracy as IIoT devices dramatically increase. In this paper, a deep reinforcement learning (DRL)-based joint resource allocation and IIoT device orchestration policy using non-orthogonal multiple access is proposed to achieve a more accurate model and reduce overhead for MEC-assisted HFL in IIoT. We formulate a multi-objective optimization problem to simultaneously minimize latency, energy consumption and model accuracy under the constraints of computing capacity and transmission power of IIoT devices. To solve it, we propose a DRL algorithm based on deep deterministic policy gradient. Simulation results show proposed algorithm outperforms others.

A Machine Learning Approach for an HPC Use Case: the Jobs Queuing Time Prediction

High-Performance Computing (HPC) domain provided the necessary tools to support the scientific and industrial advancements we all have seen during the last decades. HPC is a broad domain targeting to provide both software and hardware solutions as well as envisioning methodologies that allow achieving goals of interest, such as system performance and energy efficiency. In this context, supercomputers have been the vehicle for developing and testing the most advanced technologies since their first appearance. Unlike cloud computing resources that are provided to the end-users in an on-demand fashion in the form of virtualized resources (i.e., virtual machines and containers), supercomputers’ resources are generally served through State-of-the-Art batch schedulers (e.g., SLURM, PBS, LSF, HTCondor). As such, the users submit their computational jobs to the system, which manages their execution with the support of queues. In this regard, predicting the behaviour of the jobs in the batch scheduler queues becomes worth it. Indeed, there are many cases where a deeper knowledge of the time experienced by a job in a queue (e.g., the submission of check-pointed jobs or the submission of jobs with execution dependencies) allows exploring more effective workflow orchestration policies. In this work, we focused on applying machine learning (ML) techniques to learn from the historical data collected from the queuing system of real supercomputers, aiming at predicting the time spent on a queue by a given job. Specifically, we applied both unsupervised learning (UL) and supervised learning (SL) techniques to define the most effective features for the prediction task and the actual prediction of the queue waiting time. For this purpose, two approaches have been explored: on one side, the prediction of ranges on jobs’ queuing times (classification approach) and, on the other side, the prediction of the waiting time at the minutes level (regression approach). Experimental results highlight the strong relationship between the SL models’ performances and the way the dataset is split. At the end of the prediction step, we present the uncertainty quantification approach, i.e., a tool to associate the predictions with reliability metrics, based on variance estimation.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks

As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.

5G‐Hander: A network service for handover detection in 5G networks

In the next Fifth Generation (5G) networks, Telco Operators will assign network slices to their users and will guarantee very stringent requirements even in mobility. In this context, detecting handover events will be challenging in order to design management and orchestration policies allowing resource allocation and network slice assignment dynamically. To this purpose, this letter proposes a network service called 5G‐Hander that enables the Network Orchestrator to be alerted of an incoming handover before it is completed, in order to decide resource reallocation or even to inhibit its occurrence. Numerical results show how the proposed network service works in a little, but realistic, network.

Cluster-PSO Based Resource Orchestration for Multi-task Applications in Vehicular Cloud

Vehicular cloud computing (VCC) provides a vehicular user attaching several resources with different types at the same time. Additionally, the vehicular applications especially for big data processing are always complicated and may be decomposed into several fine-grained tasks. When offloading the complicated multi-task application to the vehicular clouds, the task executes individually in terms of its own computation, storage and bandwidth requirement. Different from the task offloading in mobile cloud computing which aims to optimize the energy consumption, the important metric for vehicular users is the application delay. Moreover, the moving vehicles always have the similar resource properties and may form the solution clusters when finding the resource orchestration policy, which brings an opportunity of improving resource orchestration performance. In this paper, we formulate the VCC resource orchestration as an optimization problem, and propose a cluster-particle swarm optimization (PSO) algorithm to obtain the resource orchestration policy. A fast cluster algorithm is used to divide the solution space and generate sub-swarms for better exploring the orchestration solutions. The experiment results show that the cluster-PSO algorithm can achieve a higher resource orchestration accuracy in an acceptable time comparing to the other PSO algorithms. Especially, when there are more tasks in an application and the vehicle has more optional VCC resources, the performance of the cluster-PSO based resource orchestration is outstanding.

Orchestrating Softwarized Networks with a Marketplace Approach

In the last years, network softwarization is gaining increasing popularity since it allows to achieve dinamicity and flexibility in network management, stimulating a lot of interest by both academia and industry. Cloud computing paradigm together with the new networking paradigms of Software Defined Networking (SDN) and Network Function Virtualization (NFV) are supporting this evolution, by providing network services as single Virtual Network Functions (VNFs) or chains of them. The main problem is scalability of both infrastructure and management. In fact, in order to support the SDN/NFV paradigm, the Telco Operator should deploy huge data centers, which have to be geographically distributed to guarantee low latencies to time-constrained flows, and implement complex orchestration policies. To this purpose, this paper proposes to extend the SDN/NFV framework with a marketplace where Telco Operator customers behave as third-party sellers with their hardware and software resources providing VNF as a service (VNFaaS), so helping the Telco Operator in providing network services in an efficient and scalable way.

Transport Abstraction Models for an SDN-Controlled Centralized RAN

In a centralized radio access network (C-RAN) scenario the joint coordination of radio (e.g., remote radio units, baseband units) and transport (e.g., optical cross connects) resources can be achieved via software defined networking (SDN) control plane, where a global orchestrator harmonizes the use of resources across all network segments. The more accurate the information about each domain (i.e., the abstraction of wireless and transport resources) is, the better will be the outcome of the orchestration work. This letter presents three transport resources abstraction models along with their corresponding orchestration policies. Their performance are compared showing that there is not a single best abstraction strategy that fits all the cases. If radio resources are scarce compared to transport resources, complex transport abstraction models are not needed. Contrariwise, if enough radio resources are widely available, more detailed abstraction models are required for achieving good network performance, but at the expense of an increased implementation complexity.

SOME CURRENT CHANGES IN THE CONDITIONS OF RESEARCH

Introduction In the aftermath of the Second World War the growth of became a recognized policy objective. The Frascati Manual that was developed by OECD ministers of and higher education in order to keep tabs on and compare funding flows to in different countries recognized three categories for accounting: basic research, applied research and product development (RD the third OECD doctrine, associated with the 1980s was an orchestration policy with a partial focus on basic research to stimulate and emerging technologies; and in the 1990s the triggering phrase became, towards a for (1) The definitions were normative, and so were the statistical household procedures. The early morphology of the research landscape in many countries displayed concomitant organizational features hinging on a distinction between basic research councils on the one hand and sectoral funding agencies associated with various departments of state on the other. It was taken for granted that such clear-cut distinctions existed, and that norms and rules in the different realms recapitulated stages in a linear model of innovation. This reinforced the belief that discoveries and ideas rightfully emerged in a free space where they gradually matured, before becoming applied and eventually taken up as products and processes (i.e. innovations) in a marketplace. The very definition of innovation was thus contextually contingent. In other words it had a specific epistemology and historical background in which a particular mode of boundary maintenance between and politics was significant. By the late 1980s, and especially with the end of the Cold War and the collapse of the former Soviet Union, these boundaries and distinctions were no longer self-evident, and several attempts have been made to redefine what in retrospect has been called the social for science (Baldursson 1995, Jenkins 1997, Lubchenko 1998, Guston 1994, Gibbons 1999, Kates et al. 2001). The American adviser Vannevar Bush, author of an influential report, Science--The Endless Frontier (1945) is often credited with drafting the blueprint for the traditional for that undergirded OECD's first policy doctrine, but actually he never used the term social contract (Bragesjo 2001). It is a retrospective construction in a quest in the 1980s to shape a reconfiguration, one that in the eyes of many researchers has narrowed the confines of academic freedom and autonomy, while giving freer play to commodification of research and commercial stakeholder interests (market governance) and other players, including movements and activists or NGOs. A number of terms have been introduced to try to capture characteristic features of the new situation in order to contrast these with the old image(s) of science. The most frequently cited notions are: --mandated (Salter) --postacademic (Ziman) --Mode-2 (Gibbons et al.) --Triple Helix (Etzkowitz and Leydesdorff) --academic capitalism (Slaughter and Leslie) --post-normal (Funtowicz and Ravetz) --socially robust science, or in the agora (Nowotny et al. …