Remote Data Centers Research Articles

Lyapunov-guided optimal service placement in vehicular edge computing

Vehicular Edge Computing (VEC) brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles. However, the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests, thus restricting the performance of VEC system. Besides, a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server. To strengthen the performance of VEC, in this paper we propose to place services beforehand at the edge server, e.g., by deploying the services/tasks-oriented data (e.g., related libraries and databases) in advance at the network edge, instead of downloading them from the remote data center or offloading them from vehicles during the runtime. In this paper, we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline. Specifically, the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization. Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution. The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.

DU-Net-Cloud: a smart cloud-edge application with an attention mechanism and U-Net for remote sensing images and processing

In recent ages, the use of deep learning approaches to extract ground object information from remote sensing high-resolution images has attracted extensive attention in many fields. Nevertheless, due to the high similarity of features between roads, prevailing deep learning semantic segmentation networks commonly demonstrate reduced continuity in road segmentation. Besides this, the role of advanced computing technologies including cloud and edge infrastructures has also become very important due to large number of images and their storage requirements. In order to better study the road details in images related to remote sensing, this paper suggests a road extraction technique which is basically founded on Dimensional U-Net (DU-Net) network. At the deepening level of the U-Net network, a parallel attention mechanism, known as ProCBAM, is added and implemented to the feature transmission step of the classical U-Net network. Moreover, we use and implement the edge-cloud architecture to develop and construct a unique remote sensing image service system that integrates several datacenters and their related edge infrastructure. In the proposed system, the edge network is primarily used for caching and distributing the processed remote sensing images, while the remote datacenter serves as the cloud platform and is responsible for the storage and processing of original remote sensing images. The results show that the proposed cloud enabled DU-Net model has achieved good performance in road segmentation. We observed that it can achieve improved road segmentation and resolve the issue of reduced continuity of road segmentation when compared with other state-of-the-art learning networks. Moreover, our empirical evaluations suggest that the proposed system not only distributes the workload of processing tasks across the edges but also achieves data efficiency among them, which enhances image processing efficiency and reduces data transmission costs.

Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network edge. However, current computation offloading studies in the heterogeneous edge environment face multifaceted challenges: Dependencies among computational tasks, resource competition among multiple users, and diverse long-term objectives. Mobile applications typically consist of several functionalities, and one huge category of the applications can be viewed as a series of sequential tasks. In this study, we first proposed a novel multiuser computation offloading framework for long-term sequential tasks. Then, we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading problem. Moreover, we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve them. We further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

Globus automation services: Research process automation across the space–time continuum

Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows, and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: (1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; (2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources; (3) an event-driven execution model for automating execution of flows in response to arbitrary events; and (4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources. These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.

This site is a dead end? Employment uncertainties and labor in data centers

Would technological changes increase the need for human workers or eliminate them altogether? This uncertainty has produced an unresolved tension, from the industrial revolution to the rise of the information society. The data center industry has been largely invisible in public debates about this question. Yet the same tensions exist within the industry itself: Will automation create data center jobs or kill them? In this article, we work inside the “black box” – the data center, to examine uncertainties faced by those who work there. We do so through interviews and observations, first, of data center managers and executives at international trade expos, where anxieties about the shortage of data center workers but also their irrelevance were palpable. Then, we turn to a remote data center in Finland, where security guards and technical operators negotiate employment uncertainties through the biopolitics of their labor. In both sites, the uncertainties about data center employment are manifest and embodied, even if they are expressed and experienced in different ways. On both the top and bottom levels of data center hierarchies, people are discomfited by the possibility of their own redundancy. At the same time, they present the sunnier sides of data center work when they talked about their efforts to resolve ongoing issues of worker shortage, the lack of diversity in data centers, and the routines that could easily slide into boredom or anomie. We situate our findings on the long arc of capitalist transformations and discuss the insights they might provide for today’s data-driven economy in general.

Scalable Remote Cloud Data Center for Vessel Equipment Predictive Maintenance Service-as-a-Product (SaaP)

Predictive maintenance is gradually replacing conventional preventive maintenance, through an informed decision-making process for a fleet operator to proactively monitor the health status of the sea-based equipment and machinery. As a result, the fleet operator will only need to ask for on-time repair and maintenance services instead of periodic maintenance, which saves both time and cost significantly. While moving into this new maritime business model, which is also known as Service-as-a-Product (SaaP), both fleet operators and maintenance, repair, and overhaul (MRO) companies could also build a stronger collaboration in the maritime industries. With the advent of digital transformation towards Industry 4.0 (I4.0), the Industrial Internet-of-Things (IIoT) renders the massive collection of operational and machinery process data from the vessel equipment through sensorization, where these collected real-time information are useful for advanced analytics to predict equipment failure and to avoid unplanned downtime. Unlike other industries, there are a few challenges in the maritime industries when developing a centralized smart vessel equipment monitoring platform. One of the key challenges, is the lack of a feasible data management system, requires the centralized host must be able to handle and manage the telemetry IoT data that is transmitted over the satellite communication, by complying with all cybersecurity considerations and regulations in maritime sector. In this paper, we present a scalable, hybrid cloud-based data management framework that can connect to multiple edge systems where each system is deployed on a physical vessel, to establish a scalable SaaP business model. Hence, the ship-to-shore sensorized data can be processed and monitored via a real-time visualization dashboard through the centralized cloud-based platform. We argue that our approach, would be a turn-key solution that can be implemented for mostly all types of marine equipment and machinery, and thus to further improve the prediction tools to support advanced decision-making techniques, such as optimal time repair of vessel equipment.

Sustainable AI Processing at the Edge

Edge computing is a popular paradigm for accelerating light- to medium-weight machine learning algorithms initiated from mobile devices without requiring the long communication latencies to send them to remote datacenters in the cloud. Edge servers primarily consider traditional concerns, such as size, weight, and power constraints for their installations. However, such metrics are not entirely sufficient to consider environmental impacts from computing given the significant contributions from embodied energy and carbon. In this article we explore the tradeoffs of hardware strategies for convolutional neural network acceleration engines considering inference and online training. In particular, we explore the use of mobile graphics processing unit (GPU) accelerators, recently released edge-class field-programmable gate arrays, and novel processing in memory (PIM) using dynamic random-access memory (DRAM) and emerging Racetrack memory. Given edge servers already employ DRAM and sometimes GPU accelerators, we consider the sustainability implications using breakeven analysis of replacing or augmenting DDR3 with Racetrack memory. We also consider the implications for provisioning edge servers with different accelerators using indifference analysis. While mobile GPUs are typically much more energy efficient, their significant embodied energy can make them less sustainable than PIM solutions in certain scenarios that consider activity time and compute effort.

DQN-enabled content caching and quantum ant colony-based computation offloading in MEC

Computation-intensive and time-delay-sensitive tasks show explosive growth, which makes mobile user devices put forward higher requirements for such tasks. Mobile user devices are difficult to meet the task requirements due to limited computing capacity. To handle this situation, a mobile edge computing system requires to be constructed. Nevertheless, in the mobile edge computing system, if no popular content is cached in the edge server, users will repeatedly send requests to a remote cloud data center when requesting content, which will cause delay and backhaul link load. To handle this situation, this article plans to put forward a content caching policy. In this policy, the caching benefit in cache replacement, delay in transmission and backhaul link load is taken into account in the content cache. Then DQN is used to handle the problem to obtain the optimal content caching scheme. In addition, in the mobile edge computing environment, the computing capacity of mobile user devices is limited, so task data needs to be unloaded to the edge server for local or remote execution to improve the computing capacity. To handle the above problems, this article plans to put forward a quantum ant colony based computing offloading strategy. This strategy takes into account three aspects: delay, energy consumption and server cost. Quantum ant colony algorithm is used to handle the situation. Experimental results indicate that the content caching strategy based on DQN can effectively improve cache hit percentage, and cut down transmission delay and return link load. Quantum ant colony based computational offloading strategies can increase task completion rates, and decrease task completion delays, equipment energy consumption and total system cost.

Near Optimal Learning-Driven Mechanisms for Stable NFV Markets in Multitier Cloud Networks

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

A virtual gateway media access placement approach for large-scale Internet of Things

The virtualisation of the Internet of Things (IoT) Gateway (GW) Media Access Control (MAC) component separates the GW MAC functions from the GW device by deploying the GW MAC functions in a selected remote edge Data Centre (eDC). This concept can help support media access for a large number of devices based on concurrent transmissions over Radio Frequency (RF) virtualisation. However, the requests for virtual GW MAC instances are dynamic and the virtual network is characterised by varying resource levels and response times. This can affect the throughput performance of the MAC protocol since the timing deadlines of the MAC functions or frame transmissions may not be met. In this work, the GW MAC functions of the IEEE 802.11ah standard for Large Scale IoT (LS-IoT) networks are virtualised as concurrent virtual Access Groups (vAGs) over a proposed Software Defined Network (SDN) virtual network. A virtual GW MAC placement strategy is proposed to optimally select the host for running the virtual GW MAC instances to minimise the virtualisation delay, improve the robustness of the deployment process and maintain a good MAC throughput performance. The results obtained in this work show that the virtualisation delay is minimised and the MAC throughput versus the number of devices is improved especially for high IoT traffic rates when compared to suboptimal approaches and the dedicated localised GW MAC approach.

When Edge Caching Meets a Budget: Near Optimal Service Delivery in Multi-Tiered Edge Clouds

More and more artificial intelligence (AI) applications, such as virtual reality (VR) and video analytics, is rapidly progressing towards enterprise and end users with the promise of bringing immersive experience. Such AI applications need unprecedented requirements for ultra-low latency. Edge computing is emerged to guarantee the timeliness for the delay-sensitive applications. The delay experienced by AI users can be significantly reduced, by caching various services that are initially deployed at data centers to cloudlets in edge networks. However, it is impractical to cache all services to cloudlets, due to often limited operational budget of service providers and resource capacity constraints of edge cloudlets. In this paper, we investigate a fundamental problem of service caching from remote data centers to cloudlets in a multi-tiered edge cloud network. We first develop two approximation algorithms with approximation ratios to solve the problem for a single type of service demanded by users. We then devise an efficient heuristic to solve the problem that users request different types of services. We finally conduct extensive experiments on a real testbed to evaluate the performance of the proposed algorithms, and experimental results demonstrate that our algorithms can outperform some existing algorithms significantly with 25% lower average delay.

Intelligent Content Precaching Scheme for Platoon-Based Edge Vehicular Networks

To provide various onboard entertainment services, the ever-increased Internet contents to be exchanged among remote data centers, roadside units (RSUs), and vehicles demand reliable and fast content dissemination in the vehicular networks. Edge precaching technology is expected to provide flexible and low-latency content dissemination by allowing edge nodes (i.e., RSUs and vehicles) to precache contents. However, the content dissemination process of edge precaching still suffers from high mobility and highly dynamic topology of vehicular networks. The recently proposed platoon-based vehicular network has potentials to mitigate the mobility challenges, but need to deal with multihop wireless content dissemination’s latency and reliability issues. Additionally, the network resources are limited in edge nodes, whereas various onboard Internet services with different Quality-of-Service (QoS) requirements share the same resource pool by the same network resource scheduling policy, thereby decaying the network performance. Based on the above observations, to cope with the challenging content precaching problem under diverse QoS requirements in a platoon-based edge vehicular network, we first abstract two isolated virtual content service slices with different QoS requirements based on network slicing technology to provide on-demand customized services. Then, we propose an intelligent deep reinforcement learning (DRL)-based content precaching scheme, which optimally matches the available communication resources and limited caching capacities in the edge vehicular network. The scheme jointly considers the impacts of content precaching policy and multihop wireless transmission on the content precaching performance. Simulation results show that our proposed DRL-based content precaching scheme achieves a competitive performance of reliability and latency comparing with other state-of-the-art algorithms.

AVTR receives substantial orders for its cloud-based condition monitoring tools

UK-based AVT Reliability Ltd (AVTR) has won orders worldwide, worth £8 million, for its Cloud/Internet of Things and Remote Data Centre (RDC) business.

Over-the-Air Computation of Large-Scale Nomographic Functions in MapReduce Over the Edge Cloud Network

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Map</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shuffle</i> ; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reduce</i> . However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.

Protecting Data on Mobile Cloud Computing

Mobile Cloud Computing is a combination of general Cloud Computing and Mobile Computing in which we have to access resources from the remote cloud data center with the help of mobile electronics and peripherals like mobile smartphones, laptops, gadgets, etc. via Cellular Technology or Wireless Communication. Mobile devices have lots of resource constraints like storage capacity, processing speed, and battery life. Hence through simple mobile computing software and programming, we cannot manipulate on mobile devices of cloud data center information. Because of such kinds of difficulty, we have to process information or data through external mobile devices. Accessing and processing of data with the help of Trusted Third Party Agency (TPA) outside the cloud data center and mobile devices have lots of security challenges. To make cloud data secure over outside resources, lots of terminologies and theory are put forward by various researchers. In this paper, we will analyze their theory and its limitations and offer our security algorithm proposal. In this thesis article, we analyze the security framework for storing data on Cloud Server by Mobile and limitation of this process. Also, we review the theory of how data can be secure our data on cloud administrators.

LEGOStore

We design and implement LEGOStore, an erasure coding (EC) based linearizable data store over geo-distributed public cloud data centers (DCs). For such a data store, the confluence of the following factors opens up opportunities for EC to be latency-competitive with replication: (a) the necessity of communicating with remote DCs to tolerate entire DC failures and implement linearizability; and (b) the emergence of DCs near most large population centers. LEGOStore employs an optimization framework that, for a given object, carefully chooses among replication and EC, as well as among various DC placements to minimize overall costs. To handle workload dynamism, LEGOStore employs a novel agile reconfiguration protocol. Our evaluation using a LEGOStore prototype spanning 9 Google Cloud Platform DCs demonstrates the efficacy of our ideas. We observe cost savings ranging from moderate (5-20%) to significant (60%) over baselines representing the state of the art while meeting tail latency SLOs. Our reconfiguration protocol is able to transition key placements in 3 to 4 inter-DC RTTs (&lt; 1s in our experiments), allowing for agile adaptation to dynamic conditions.

Satellite-Enabled Internet of Remote Things Network Transmits Field Data from the Most Remote Areas of the Tibetan Plateau.

In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

CLOUD FORENSICS FRAMEWORK FOR LAW ENFORCEMENT AGENCIES

Internet-based cloud technology is a network of remote data centers often placed beyond the country's legal frontiers worldwide. Contrary to the benefits of cloud computing, it is also a target of cybercriminals who may affect its resources on a larger scale by a single exploit. For protecting the cloud resources and increasing the confidence of cloud users, it is necessary to make one accountable for disrupting its services based on relevant evidence that proves someone's guilt in a court of law. In the literature, various frameworks have been presented for evidence collection against the attack on the cloud service for Cloud Service providers (CSP), but there is no framework for LEAs. Unfortunately, the evidence of a security breach in the cloud resides under the control of CSP, which is the sole custodian of cloud resources. However, the CSP does not fully cooperate with the investigators due to various legal, technical, and operational reasons. Hence the entire prosecution is dependent on the provision of evidence by the CSP, which is a great challenge for law enforcement around the world. The study's objective is to design a framework that mitigates the dependency of CSP by collecting the evidence of a security incident outside the cloud by colluding the Internet Service Providers (ISPs) and law Enforcement for a particular cloud service. The framework integrates the components that can detect the attack on a cloud service earlier at ISP and store the logs of the incident in a forensic server which can be used for forensics purposes as and when required.

Stochastic programming based multi-arm bandit offloading strategy for internet of things

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things (IoT) users, Multi-access Edge Computing (MEC) migrates computing and storage capabilities from the remote data center to the edge of network, providing users with computation services quickly and directly. In this paper, we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading, where the connection between the IoT user and the MEC servers is uncertain. This uncertainty would be the main obstacle to assign the task accurately. Consequently, if the assigned task cannot match well with the real connection time, a migration (connection time is not enough to process) would be caused. In order to address the impact of this uncertainty, we formulate the offloading decision as an optimization problem considering the transmission, computation and migration. With the help of Stochastic Programming(SP), we use the posteriori recourse to compensate for inaccurate predictions. Meanwhile, in heterogeneous networks, considering multiple candidate MEC servers could be selected simultaneously due to overlapping, we also introduce the Multi-Arm Bandit (MAB) theory for MEC selection. The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method (SMM) for offloading in terms of reward, cost, energy consumption and delay. The results show that SMM can achieve about 20% improvement compared with the traditional offloading method that does not consider the randomness, and it also outperforms the existing SP/MAB based method for offloading.

Monitoring of air quality with satellite-based sensor: The case of four towns in Southeast, Nigeria

Air pollution is an important issue globally because it is linked to health risks or death as the case may be. No nation has placed a carefree attitude on the menace. Constant monitoring to reduce or bring it to an end is on the increase. Internet of Things (IoT) is one of the tools used in monitoring air quality. The main functionality of IoT includes sensing out the surrounding environment collecting the data from the surrounding and transmitting those data to the remote data centers or the cloud. In this study, Satellite model information provided by Plume Labs, Paris, France was used for the determination of the air quality in four towns (Owerri, Awka, Aba, and Nsukka) in the Southeastern part of Nigeria. The data were obtained from the Plume Labs website between the hours of 6-9 am and 7-9 pm every day for a period of sixty (60 days). The air qualities of the towns were obtained by comparing the satellite results with Plume Air Quality Index Indicator (PAQI). PAQI has seven levels (Low, Moderate, High, Very High, Excessive, Extreme, and Airpocalypse) of pollution which are linked to the World Health Organization (WHO) limits. The results depicted that the air quality of the cities has reached elevated levels of pollution above the 24-hour daily limit set by the WHO, which is an indication that everyone may start to experience serious health effects. A long-term exposure constitutes a real health risk.