Massive Data Centers Research Articles

A Secure Key Exchange and Authentication Scheme for Securing Communications in the Internet of Things Environment

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection.

The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks. In particular, a new graph network reconstruction strategy, which treats data communications as nodes in a directed graph while edges are then connected according to two specifically defined rules, is devised and applied to facilitate the graph representation learning in secure and efficient IoT communications. Both the structural and traffic features are then extracted from the graph data and flow data respectively, based on the graph attention network (GAT) and multilayer perceptron (MLP) techniques. These can benefit the GNN-based KD process in accordance with the more effective feature fusion and representation, considering both structural and data levels across the dynamic IoT networks. Furthermore, a lightweight local subgraph preservation mechanism improved by the graph attention mechanism and downsampling scheme to better utilize the topological information, and a so-called global information alignment defined based on the self-attention mechanism to effectively preserve the global information, are developed and incorporated in a refined graph attention based KD scheme. Compared with four different baseline methods, experiments and evaluations conducted based on two public datasets demonstrate the usefulness and effectiveness of our proposed model in improving the efficiency of knowledge transfer with higher classification accuracy but lower computational load, which can be deployed for lightweight anomaly detection in sustainable IoT computing environments.

UAV-Assisted RSUs for V2X Connectivity Using Voronoi Diagrams in 6G+ Infrastructures

Sixth-generation networks and vehicular Ad hoc networks advancement brought us to the cusp of a new era. Autonomous Vehicles with a plethora of advanced applications require a substantially enhanced vehicle-to-everything communication network. The infrastructure should efficiently support hyper-fast, ultra-reliable, and low-latency massive data exchange. Roadside units were initially exploited as a promising communication solution to overcome this challenge. However, the challenging integration with the infrastructure led to the investigation of additional solutions. Unmanned aerial vehicles are one of the most dominant assistive solutions due to their inherent advantage of mobility. Moreover, air-to-air and ground-to-air networks are more efficient than ground-to-ground. Nevertheless, it is a prerequisite to leverage the potential of unmanned aerial vehicles to attain nationwide Vehicle-to-Everything connectivity. Therefore, we focused our research orientation on developing a strategy to optimize the network’s coverage within the intelligent transportation systems framework. In particular, we have deployed an innovative algorithmic technique that constructs Voronoi diagrams using circles. Besides, we applied the poison point process to determine the optimum locations of the transceivers’ establishment. Simulation results illustrate full network coverage for the tested area after the required iterations. Also, time complexity evaluation proved the simplicity of the proposed algorithm.

Where is the Traffic Going? A Comparative Study of Clouds Following Different Designs

Cloud computing is critical for today's information society. In this paper, we shed light on two radically different cloud design philosophies: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DC-cloud</i> built around massive data centers, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISP-cloud</i> built upon a large ISP. With extensive measurements on Alibaba, Tencent, and CTYun, we find that both designs have strengths and weaknesses: the ISP-cloud of CTYun has less inflated paths to users within the same ISP, but its paths to external users are more inflated comparing with the DC-clouds of Alibaba and Tencent. By analyzing the clouds’ routing policies, we reveal the reasons behind the path inflations: Alibaba and Tencent adopt an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">early-exit</i> policy to use more inflated public Internet paths as early as possible; while CTYun follows a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global and location-agnostic</i> policy to detour traffic to remote PoPs, leading to highly inflated paths. Based on the insights, we suggest alternative policies and averagely reduce 11.0% latency to 30.5% destinations for Alibaba, 9.8% latency to 18.6% destinations for Tencent, and 54.1% latency to external destinations for CTYun. The results suggest that both cloud designs have rooms for improvement, and an ISP-cloud has the potential to achieve a superior performance, thanks to its inherited advantages from the ISP infrastructure.

GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard Mechanism

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine environment of blockchain, which eliminates sharding benefits. To tackle the challenge, this paper presents GriDB, the first scalable blockchain database, by designing a novel off-chain cross-shard mechanism for efficient cross-shard database services. Borrowing the idea of off-chain payments, GriDB delegates massive cross-shard data exchange to a few nodes, each of which is randomly picked from a different shard. Considering the Byzantine environment, the untrusted delegates cooperate to generate succinct proof for cross-shard data exchanges, while the consensus is only responsible for the low-cost proof verification. However, different from payments, the database services' verification has more requirements (e.g., completeness, correctness, freshness, and availability); thus, we introduce several new authenticated data structures (ADS). Particularly, we utilize consensus to extend the threat model and reduce the complexity of traditional accumulator-based ADS for verifiable cross-shard queries with a rich set of relational operators. Moreover, we study the necessity of inter-shard load balancing for a scalable blockchain database and design an off-chain and live approach for both efficiency and availability during balancing. An evaluation of our prototype shows the performance of GriDB in terms of scalability in workloads with queries and updates.

Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach

Massive scale data centers (MSDC) have become a key component of current content-centric Internet architecture. With scales of up to hundreds of thousands servers, conveying traffic inside these infrastructures requires much greater connectivity resources than traditional broadband Internet transit networks. MSDCs use Fat-Tree type topologies, which ensure multipath connectivity and constant bisection bandwidth between servers. To properly use the potential advantages of these topologies, specific routing protocols are needed, with multipath support and low control messaging load. These infrastructures are enormously expensive, and therefore it is not possible to use them to experiment with new protocols; that is why scalable and realistic emulation/simulation environments are needed. Based on previous experiences, in this paper we present extensions to the ns-3 network simulator that allow executing the Free Range Routing (FRR) protocol suite, which support some of the specific MSDC routing protocols. Focused on the Border Gateway Protocol (BGP), we run a comprehensive set of control plane experiments over Fat-Tree topologies, achieving competitive scalability running on a single-host environment, which demonstrates that the modified ns-3 simulator can be effectively used for experimenting in the MSDC. Moreover, the validation was complemented with a theoretical analysis of BGP behavior over selected scenarios. The whole project is available to the community and fully reproducible.

Tuning of Extended-Resonance-Based Beamforming System for Visible Light Communication

Visible Light Communication (VLC) uses the visible light emitted from Light Emitting Diode (LED) to transmit/receive data. Since the data is transmitted through the light, the connection speed is as fast as the speed of light, making it potential for very fast and massive data exchange. One thing that needs to be considered in VLC is that the power of the signal received by the sensor relies on the angle between the LED and the light sensor used as an antenna. The bigger the angle between LED and light sensor, the less optimal the signal power will be, and surely will affect the speed and reliability of the data transmission. To optimize the signal power, multiple photonic sensors will be used as an antenna to receive the light signal. The signal received from each photonic sensor will be combined to get higher signal power. However, to ensure that all signals from all photonic sensors are constructive to each other, all phase differences must be minimized. This paper proposes the extended-resonance-based beamforming system to be used to minimize the phase difference of the light signals in VLC application. A non-linear optimization method is used to tune the extended-resonance-based beamforming system. Given that the varactor is chosen carefully and sufficient enough, the non-linear optimization method such as active set, interior point, or sequential quadratic programming is able to tune the varactor, so that the beamformer will compensate the phase difference from the incoming signal.

EDCSuS: Sustainable Edge Data Centers as a Service in SDN-Enabled Vehicular Environment

Cloud computing has emerged as one of the popular technologies which provide on-demand services to the end users. Such services are hosted by massive geo-distributed data centers (DCs). Nowadays, connected vehicles in a smart city can also avail cloud services through Internet using cellular technologies. But, the advent of 5G technology has posed challenges for DCs such as-low latency and higher data rate requirements. To handle these challenges, edge-DCs (EDCs) can be deployed across a smart city to provide low latency services to the connected vehicles. In lieu of this, in this paper, EDCSuS: Sustainable EDC as a service framework in software defined vehicular environment is proposed. In EDCSuS, first, a software defined controller handles the incoming requests and suggest an optimal flow path. Second, a multi-leader multi-follower Stackelberg game is presented for resource allocation. Third, to improve the resource utilization, a cooperative resource sharing scheme is designed, thereby minimizing the energy consumption of servers in the EDCs. Lastly, a caching scheme is presented to avert excessive energy consumption for retracing the lost link due to vehicular mobility. The efficacy of the proposed scheme has been evaluated using extensive simulations with respect to various parameters. The results obtained prove the effectiveness of EDCSuS.

A Low-Cost High-Resolution Solid-State Lidar With Wavelength Division Multiplexed Components and Interleaved Orthogonal Polarization Grating Couplers

Laser diodearray and photo diode array combined through coarse wavelength division multiplexed (CWDM) are widely used in short-reach optical communication within massive web-scale data center. Those components can be used in solid-state photonics-based Lidar built upon silicon-on-insulator (SOI) waveguide as well, achieving significant cost saving. Beam steering can be naturally achieved through diffraction in output grating couplers. However, current design is hampered by low angular resolution. We demonstrate a novel design by combining outputs of two grating couplers with orthogonal polarizations in an interleaving manner. This scheme effectively doubles the angular resolution of on-chip Lidar.

Enhanced Data Security in Cloud Computing: Survey

-The next era in the advent of the internet is internet based computing also known as cloud computing. It has been the emphasis in recent years, but security concerns are one among the most important impediments to the rise of cloud computing.. It essentially sends user data and application programs to massive data centers, i.e. a distant cloud, where consumers have little control and data management may be insecure. However, numerous security issues raised by the cloud's unique nature must be addressed and fully acknowledged.. Amongst the most critical concerns which must be tackled is the security issue. Issues with data security created by user data and software in the provider's jurisdiction. Cloud users search for cloud resources with secure data management. When sharing their personal data over public clouds, some cloud users may prefer to manage their data with more privacy. This document focuses on improving privacy-related data security in the cloud and related works that have been done to enhance data security in cloud computing. This research review work analyzes and discusses several of the security and privacy improvements evaluated in related existing systems.

Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review

The Internet architecture has been undergoing a significant refactoring, where the past preeminence of transit providers has been replaced by content providers, which have a ubiquitous presence throughout the world, seeking to improve the user experience, bringing content closer to its final recipients. This restructuring is materialized in the emergence of Massive Scale Data Centers (MSDC) worldwide, which allows the implementation of the Cloud Computing concept. MSDC usually deploy Fat-Tree topologies, with constant bisection bandwidth among servers and multi-path routing. To take full advantage of such characteristics, specific routing protocols are needed. Multi-path routing also calls for revision of transport protocols and forwarding policies, also affected by specific MSDC applications’ traffic characteristics. Experimenting over these infrastructures is prohibitively expensive, and therefore, scalable and realistic experimentation environments are needed to research and test solutions for MSDC. In this paper, we review several environments, both single-host and distributed, which permit analyzing the pros and cons of different solutions.

Cone Model in Resource Provisioning for Service-Oriented Architecture System: An Effective Network Management to the Internet of Things

The rapid growth of technologies like the Internet of Things (IoT) has thrown newer challenges to resource management, making it one of the crucial tasks in dynamic application scenarios of IoT. Service-Oriented Architecture (SOA) plays an important role in this respect by proffering reference architecture towards better positioning of infrastructural software and hardware components, mostly comprehending from a service-oriented perspective. In this direction, we propose Cone Model that deals with the performance and handling issues with SOA system. Service integration is all important feature of SOA that supports communication and interaction between multiple service components (Service Containers). In addition to discussing the related work for critical resource allocation algorithms, this paper proposes Cone Model for assigning an available set of resources to requested services, explained through mathematical expressions and algorithms of the model. These algorithms justify that how different services from various devices in IoT can be provisioned on the adoption of this proposed cone model. It is illustrated through the estimated utilization of resources by the services. The proposed model aligns with the applications in multifarious domains, primarily in the IoT and in areas of resource sharing for cloud computing and massive data centers.

An Efficient Group-Based Replica Placement Policy for Large-Scale Geospatial 3D Raster Data on Hadoop.

Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements.

APPLICATION OF MACHINE LEARNING TECHNIQUES IN DATA CENTRE ENERGY MANAGEMENT

Cloud computing is one of the leading computing paradigms that offers services like Infrastructure as a Service called IaaS, Platform as a Service called PaaS, Software as a Service called SaaS to users on a pay per use model. The massive data centers that help cloud offer all the above stated services are virtualized. Virtualization enables easy management of resources. However, the massive physical servers in the data centers tend to consume enormous energy, leading to high environmental impact. So, energy conservation with optimum usage and management is one of the prominent areas of research in cloud. The major techniques to manage energy is to identify unused physical resources and put them to low power state or sleep state. But, the usage of resources depends heavily on the user requirements in an elastic environment like cloud. Hence machine learning techniques can be used to predict the usage patterns thereby identifying the physical resources required to fulfill the user demand. This paper aims to survey the avenues wherein machine learning can be applied to help energy management in a cloud data center.

Distributed Energy Management for Multiple Data Centers With Renewable Resources and Energy Storages

For Internet and cloud computing service providers, running massive geo-distributed data centers incurs prodigious electricity cost and water consumption as well as carbon emission rooted in electricity generation. Thus, it is critical significant for providers to lower down the operation cost of data centers. In this article, we investigate the problem of energy management for geo-distributed data centers with renewable resources and energy storages. We aim to minimize the long-term operation cost including electricity cost, water consumption, and carbon emission by leveraging the spatiotemporal diversity of these system states. To this end, we first formulate the cost minimization problem as a stochastic optimization problem, then we adopt the Lyapunov optimization technique to design a close-to-optimal online algorithm which only needs the current system information and achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. To reduce the computational complexity and unnecessary communication, we further propose a distributed algorithm based on the distributed computing framework alternating direction method of multipliers (ADMM), which enables each data center to make their own control decisions. Based on the real-world traces and extensive simulations, we demonstrate the effectiveness of our proposed algorithms.

Are Federated Cloud Sharing Systems Sustainable?: On Dynamic Sharing Markets and Their Stability

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature?. In this article, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.

A Survey of ICN Content Naming and In-Network Caching in 5G and Beyond Networks

Internet usability is expanded form just human-to-human interactions toward different communication types, while the communication itself is shifting from the host-centric model to the content-centric paradigm. The 5G and beyond networks promise not only to support such changes but also to provide massive data exchange and connectivity with high reliability. The next-generation networking technologies are the key enabled for 5G that aim at building a new ecosystem. One promising piece of this ecosystem is the information-centric network (ICN), which is a future network architecture that tends to tackle the current host-centric model issues. It natively supports several features, including abstraction content naming and transparent in-network content caching that contribute to improve network performance, reduce traffic, and improve the latency. In this article, we first provide a potential road map by introducing different next-generation active technologies to enable the big picture of 5G, including mobile-edge computing (MEC), software-defined networking (SDN), and network function virtualization (NFV). Then, we discuss the need for ICN and its coexistence within this ecosystem. Later, we present an in-depth review of the recent content naming schemes and a comprehensive review of in-network content caching solutions. We classify these solutions into different classes based on the used technologies and their working principle. Finally, we highlight some research challenges and propose promising directions for the research community.

EBI-PAI: Toward an Efficient Edge-Based IoT Platform for Artificial Intelligence

Edge computing, especially multiaccess edge computing, is seen as a promising technology to improve the Quality of user Experience (QoE) of many artificial intelligence (AI) applications in the evolution toward Internet-of-Things (IoT) infrastructure. However, the management and deployment of massive edge data centers bring new challenges for the current network. In this article, we propose a new edge-based IoT platform for AI (EBI-PAI), based on software-defined network (SDN) and serverless technology. EBI-PAI provides a unified service calling interface and schedules the resources automatically to satisfy the QoE requirements of users. To optimize performances during incremental deployment, we formulate the deployment problem, prove its complexity, and design heuristic algorithms to solve it. We implement EBI-PAI based on an opensource serverless project and deploy it in real networks. To evaluate EBI-PAI, we conduct comprehensive simulations based on the generated and real-world network topology, and real-world base station data set. The simulation results show that EBI-PAI can greatly improve QoE with the same budget and save the budget to achieve similar QoE. We finally carry out a case study with real user demands, and it further validates the simulation results.

Clonal selection algorithm for energy minimization in software defined networks

With the advancements of Information and Communication Technologies (ICT), large scale distributed computing and massive data center infrastructures are becoming more common these days. Such trends have drastically put a lot of load on the volumes of data transferred over networks, thus necessitating close to capacity link utilizations flexible forwarding decision-making. Software-defined networking (SDN), with its inherent segregation of control and data planes, provides flexible decision making that can leverage the global network information available to the SDN controller for dynamic and accurate solutions. However, contemporary researchers have focused on the flexibility and security aspects of SDN, widely ignoring energy consumption strategies in next-generation IP networks, which otherwise is a crucial driver in any research field. The scanty existing energy minimization strategies are mostly based on aggregate traffic, which leads to imbalanced utilization of links and affects the quality of service (QoS) adversely. In this paper, we leverage SDN’s key benefits for reducing energy network consumption while realizing dynamic load balance with a few QoS constraints. To this end, a multiobjective optimization problem (MOOP) is formulated that attempts to minimize power consumption and link utilization. With different capacities of switches and links, finding optimal configurations and deciding best paths, even for relatively small networks, become computationally challenging and is, in fact, an NP-hard problem. In this paper, we propose to employ the Clonal Selection Algorithm (CSA), a discrete, metaheuristic solution to find out optimal solutions for this MOOP, namely a Clonal Selection based Energy Minimization (CSEM). Simulations have been carried out for testing the efficacy of the proposed CSEM using real-life network topologies and link-traffic data. The results obtained by the proposed CSEM prove to be efficacious, and the same have also been validated with three different benchmark functions to test the suitability of SDN for CSA.

On Half-Built Assemblages: Waiting for a Data Center in Prineville, Oregon

In 2010 the mega-corporation Facebook finalized an agreement to build a massive data center in Prineville, a small town in central Oregon previously known for logging, cattle ranching, and as the headquarters of the Les Schwab tire company. This was a largely unanticipated event that local leaders nonetheless prepared for several decades before when they designated a rural economic zone on the outskirts of town. However, the enterprise zone sat mostly unused, an empty and dusty piece of high desert land dotted with sagebrush and juniper trees. I describe the preparatory efforts that laid the groundwork for the data center as effecting a “half-built assemblage.” Through such anticipatory reconfigurations, local leaders recognized the limits of regional government to overcome the challenges of their peripherality. In the controversy surrounding such data center deals, critics have often cast rural leaders as naive or as pandering to voters. However, I argue that the alliance with Facebook was one of the few courses of action available to local leaders that had any chance of realizing regional economic development goals. In seeking to understand the data center deal from a local perspective, I contribute an alternative notion of temporality to materialist theorizing by looking across much longer durations of time in relation to the political economy, the natural world, and other elements as a way to temper exaggerations of anthropocentric agency and the narrow attribution of blame.