Micro-data Centers Research Articles

Pobreza y costo relativo de la vivienda en Santiago (Chile): una perspectiva histórica (1966-202)

This article aims to analyze the relative cost of housing in Chile between 1966 and 2022. Specialized literature in this field focuses on four main themes: the impact of the neoliberal model on housing policies, the issue of property and renting, the maintenance of shantytowns, and issues of space and architecture. While there is consensus on the positive impact of social policies on access to housing for low-income families, debates persist regarding poverty and marginality. The research employs the Occupational and Unemployment Survey of Gran Santiago conducted by the Microdata Center of the University of Chile, administered quarterly since 1957 in all districts of Gran Santiago, with an annual survived population of around 10 000 individuals and 2 000 to 3 000 family groups. The findings reveal that: 1) the reduction in the relative cost of housing is more significant in non-poor households than in poor households; 2) in comparison to the international average, there is a decrease in the relative cost from 2000 to 2010, followed by an increase between 2011 and 2017; 3) the relative cost rises in impoverished sectors, particularly in the bottom 10% of income earners; 4) the reduction in relative cost is more pronounced in rented homes than in owned ones. Keywords: Chile; housing; relative poverty; income.

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing.

Robust and Fault-Tolerant Fog Design and Dimensioning for Reliable Operation

Internet of Things (IoT) applications depend on reliable external storage and processing such as cloud data centers. In response to high latency from cloud, fog computing has been introduced as a network of microdata centers closer to IoT devices that provides a geo-distributed low-latency response. Current contributions regarding design and dimensioning of fog infrastructures are developed to service a static set of IoT traffic and a reliable fog network. However, these designs are not fault tolerant. This article explores the implementation of reliable and fault-tolerant fog infrastructures via dynamically available fog nodes—standby nodes that activate when a nearby fog node fails. We formulate the design and dimensioning of dynamically available nodes as a set partitioning problem, which is solved via a mixed-integer linear program (MILP). This MILP formulation proves to be intractable; we therefore introduce a column generation approach to increase scalability with little loss to optimal design and dimensioning cost. Compared to other benchmark heuristic methods, our column generation approach yields reduced cost, with proportional solution time.

Efficient micro data centres deployment for mobile healthcare monitoring systems in IoT urban scenarios

ABSTRACT The Internet of Things (IoT) has caused an exponential increase in the number of connected devices. This brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services. However, more powerful storage and processing capabilities are required to service all these devices. Modelling and Simulation (M&S) can help to deploy IoT infrastructure, providing flexible and powerful mechanisms to study and compare different strategies. In this scenario, Micro Data Centers (MDCs) are an effective solution to reduce overwhelmed Cloud Data Center infrastructures. This paper explores an M&S methodology to study the overall power consumption of a healthcare IoT scenario, where patients wear non-intrusive monitoring devices that periodically generate computing tasks. We extract the layout of existing urban infrastructures, simulate the monitored population’s behaviour, and compare the power consumption of several data centre configurations.

Optimizing NFV placement for distributing micro-data centers in cellular networks

With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users’ requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption.

A new cooling strategy for edge computing servers using compact looped heat pipe

The rapid development of distributed edge computing system demands urgently that the thermal management solution for server CPUs in micro-data centers have the characteristics of compact structure, high performance, maintenance-free, and energy saving. This paper focuses on a new cooling concept enabled by a compact loop heat pipe. Through removing compensation chamber, selecting a spindle-shaped evaporator structure with aid of an auxiliary gradient sintered capillary wick connecting the condenser and the evaporator, this new proposed loop heat pipe module can provide high reliability operation, meanwhile maintaining high thermal performance. Under forced air cooling, it is demonstrated that the 4 mm thick loop heat pipe module with finned tube condenser could dissipate a heat load of 150 W under a wide fan voltage range from 12 V to 24 V at tested directions when the chip junction temperature was below 85 °C, and could manage a heat load up to 275 W without dry-out. The proposed new loop heat pipe module with finned tube condenser is a potential solution to cool edge-computing server CPUs.

Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment

Fog computing is an emerging computation technology for handling and processing the data from IoT devices. The devices such as the router, smart gateways, or micro-data centers are used as the fog nodes to host and service the IoT applications. However, the primary challenge in fog computing is to find the suitable nodes to deploy and run the IoT application services as these devices are geographically distributed and have limited computational resources. In this paper, we design the two-level resource provisioning fog framework using docker and containers and formulate the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications. We solved the said multi-objective problem using the Elitism-based Genetic Algorithm (EGA). The proposed approach is evaluated on fog computing testbed developed using docker and containers on 1.4 GHz 64-bit quad-core processor devices. The experimental results demonstrate that the proposed method outperforms other state-of-the-art service placement strategies considered for performance evaluation in terms of service cost, energy consumption, and service time.

Predictive Autoscaling of Microservices Hosted in Fog Microdata Center

Fog computing provides microdata center (MDC) facilities closer to the users and applications, which help to overcome the application latency and response time concerns. However, guaranteeing specific service-level objectives (SLOs) for the applications running on the MDC requires automatic scaling of allocated resources by efficiently utilizing the available infrastructure capacity. In this article, we propose a novel predictive autoscaling method for microservices running on the fog MDC to satisfy the application response time SLO. Initially, our proposed approach uses a reactive rule-based autoscaling method to gather the training dataset for building the predictive autoscaling model. The proposed approach is efficient, as it can learn the predictive autoscaling model using an increasing synthetic workload. The learned predictive autoscaling model is used to manage the application resources serving different realistic workloads effectively. Our experimental evaluation using two synthetic and three realistic workloads for two benchmark microservice applications on a real MDC shows excellent performance compared to the existing state-of-the-art baseline rule-based autoscaling method. The proposed autoscaling method yields 75.51% reduction in the number of rejected requests and 77.53% fewer number of SLO violations compared to the baseline autoscaling methods by using only 9.20% additional data center resources at the fog layer.

Modeling temperature distribution and power consumption in IT server enclosures with row-based cooling architectures

Traditional data center cooling methods cannot yet control cooling airflows and temperatures on demand, creating an intrinsic inefficiency. A recent solution places row-based cooling unit adjacent to servers and places the entire assembly within an enclosure, which improves airflow distribution and provides rapid real-time control. This is, in particular attractive for micro-data centers where traditional room-based cooling is less energy efficient. Spatiotemporal predictions of temperatures are required to control and optimize data center performance as the system configuration, and other parameters are varied. Current methods, such as proper orthogonal decomposition,machine learning, and heuristic models are inapplicable in practice because they require a prohibitively large number of a priori simulations or experiments to generate training datasets. We provide an alternative a computationally inexpensive training-free model for enclosedmicro-data centers that are integrated with in-row cooling units that requires no a priori training. The model determines the air flowrate within each zone based on a mechanical resistance circuit analysis. These flowrates are then introduced into a zonal energy balance to predict the temperature of each zone. The methodology is validated with experimental measurements and coupled with a power consumption calculation. Its applicability is demonstrated by evaluating the influence of various system factors, such as IT server configurations, cooling unit air, and water flowrates and the numbers of cooling units, on the temperature distributions, and total cooling power consumption. The method can improve micro-data centers control and help to optimize the design of any data center with a row-based cooling system.

Flexible low-latency metro-access converged network architecture based on optical time slice switching

The “pay-as-you-grow” cloud computing model has become popular for today’s enterprises. Cloud computing not only frees end users from complex operations, but also allows higher resource utilization, lower investment, and increased energy efficiency. However, with some emerging technologies, cloud computing is unable to meet the required latency level, especially for delay-sensitive services such as 5G communications, live streaming, and online gaming. In this context, edge computing is regarded as a promising technology that can provide low-latency connections in the near future. Unlike cloud computing, which provides service in a centralized mode, edge computing deploys micro data centers (MDCs) at the edge of the network to provide rapid-response service. However, due to the limited computing and storage capacity of a single MDC, a user may not be able to access the resources from the closest MDC during peak traffic periods. Under such circumstances, the user is served through another MDC or a remote cloud data center. The data are processed in an optical line terminal and then transmitted via the metro network, which significantly increases the latency. In this study, we introduce a flexible low-latency metro-access converged network architecture based on optical time slice switching (OTSS) to address the latency problem. By leveraging the transparent connections of the OTSS in this novel architecture, data can be transmitted through the MDCs without requiring extra processing time. The simulation results demonstrate that our proposed architecture can provide lower-latency connections under a range of conditions, with a negligible decrease in network throughput, compared with an existing representative architecture. Additionally, we conducted experiments to validate the feasibility of our approach.

Pervasive Data Science on the Edge

Proliferation of sensors into everyday environments is resulting in a connected world that generates large volumes of complex data. This data is opening new scientific and commercial investigations in fields such as pollution monitoring and patient health monitoring. Parallel to this development, deep learning has matured into a powerful analytics technique to support these investigations. However, computing and resource requirements of deep learning remain a challenge, often forcing analysis to be carried at remote third-party data centers. In this paper, we describe an alternative computing as a service model where available smart devices opportunistically form micro-data centers that can support deep learning-based investigations of data streams generated by sensors. Our model enables smart homes, smart buildings, smart offices, and other types of smart spaces to become providers of powerful computation as a service, enabling edge analytics, and other applications that require pervasive (in-space) decisioning.

THE DISTRIBUTION OF SLICE SERVICE RESOURCES IN 5G NETWORKS

The trend of mobile networks 5G is growing with the increase in the number of users and the speed of transmission. Many operators are turning to small cellular and internal coverage of telecommunication network services. Due to the emergence of software-specific network technologies and virtualization of network functions, the ISP can deploy its networks more flexibly and dynamically. In addition to changing the model for deploying a wireless mobile network, it also determines the tendency for micro-operators to develop (μO). Communication operators can provide regional network services through public buildings, shopping malls or industrial facilities. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed μO architecture meets the computing requirements for applications, data, and services from the cloud data center to the peripheral network devices or the micro-data center μO. The μO service model is able to reduce network latency in response to applications with a low latency for the future 5G boundary computing environment. This article discusses the design of the micro-operator 5G and proposes a mechanism for redirecting flows based on the decision tree (DTBFR) to redirect traffic flows to neighboring service nodes. The DTBFR mechanism allows different microcontrollers to share network resources and accelerate the development of border computing in the future.

RDNA: Residue-Defined Networking Architecture Enabling Ultra-Reliable Low-Latency Datacenters

Datacenter (DC) design has been moved toward the edge computing paradigm motivated by the need of bringing cloud resources closer to end users. However, the software defined networking (SDN) architecture offers no clue to the design of micro DCs (MDCs) for meeting complex and stringent requirements from next generation 5G networks. This is because canonical SDN lacks a clear distinction between functional network parts, such as core and edge elements. Besides, there is no decoupling between the routing and the network policy. In this paper, we introduce residue defined networking architecture (RDNA) as a new approach for enabling key features like ultra-reliable and low-latency communication in MDC networks. RDNA explores the programmability of residues number system as a fundamental concept to define a minimalist forwarding model for core nodes. Instead of forwarding packets based on classical table lookup operations, core nodes are tableless switches that forward packets using merely remainder of the division (modulo) operations. By solving a residue congruence system representing a network topology, we found out the algorithms and their mathematical properties to design RDNA’s routing system that: 1) supports unicast and multicast communication; 2) provides resilient routes with protection for the entire route; and 3) is scalable for 2-tier Clos topologies. Experimental implementations on Mininet and NetFPGA SUME show that RDNA achieves 600 ns switching latency per hop with virtually no jitter at core nodes and sub-millisecond failure recovery time.

Impact of Metro-Embedded Data Centers on Metropolitan Network Design and Traffic Profile

Traditional data centers (DCs) that are located in distant areas usually lack flexibility and also have long service latency for metro users. On the other hand, the recently introduced and implemented metro-embedded data center (ME-DC) architecture, based on micro-data centers (mDCs), delivers more flexible services with less access latency. Hierarchical software-defined network control is employed to virtualize and holistically coordinate the distributed mDCs and metro network slices into a reconfigurable virtual data center. Such an approach improves the overall resource mobility and access to the service; however, the impact on the metro network traffic profile induced by user/mDC and inter-mDC connections due to the dispersion of mDCs has not been evaluated. In this paper, we develop a mixed integer linear programming model that optimizes the total DC traffic, taking into consideration the intra-, inter-, and external DC communications. Furthermore, we numerically assess the changes in the metro network traffic induced by ME-DC architecture, considering different types of DCs and their corresponding traffic profile. Simulation shows that the distribution of the mDCs has a beneficial effect, leading to a fourfold reduction in the total traffic load.