Distributed Cloud Network Research Articles

Color image encryption scheme for distributed architecture with SCFP chaotic map

Image protection mechanism in distributed cloud network is an essential component of information security field. In this paper, a novel one-dimensional sine-cosine fractional power chaotic map (SCFP) is proposed. Results of various dynamical system tests illustrate that SCFP exhibits superior chaotic behavior over its infinite positive real parameter range, whose complexity and unpredictability can guarantee the strength of image cryptosystem. Furthermore, a color image encryption scheme tailored for distributed architecture is devised. Firstly, a hybrid cryptographic mechanism is designed to perform diffusion and confusion encryption for image data and ECC public key encryption for intermediate keys. Secondly, the diffusion structure elevates processing units to row-column level, and the diffusion order is dictated by a pseudo-random sequence generated by SCFP. Thirdly, the confusion structure extends the unbiased and efficient Fisher-Yates algorithm into a 2D space, and adopts a design of dual plaintext-related key. Lastly, three techniques namely QOI lossless compression, DE information embedding and threshold secret sharing are integrated to resolve issues of data volume inflation, key synchronization difficulty and poor fault tolerance. Simulation experiments conducted on multiple color images demonstrate that the proposed scheme offers significant ciphertext randomness, sufficiently large key space and strong key sensitivity, which can ensure the integrity of image data and resist various typical cryptographic attacks, and outperforms existing schemes oriented to centralized architecture in terms of security and efficiency.

Computer Network Data Management Model Based on Edge Computing

Data reliability and confidence in the data are very important issues, especially when the system integrates fraud or false information. The misusing of data collected may create serious problems. With the fast development of computing techniques, much data are collected from various terminals and industrial devices. Edge computing operates by driving data, software and computer resources from the centralized network to its extremes, allowing pieces of knowledge to lie on distributed cloud networks. Its target customers continue to use commercial Internet application software for every internet customer. Edge computing is used to provide delay-free customer experience assistance for features of the Internet of Things (IoT) services on the edge of the user network. The document identifies an IoT computing platform collaborating with the edge competitive data management latency (CDML) tool. This approach separately categorizes edge layer requests and response data over time using demand-density driven optimization. A difference-based optimization optimizes the frame limits for simultaneous request processing and exact allocation of data. The architectural efficiency of edge computing can be assessed by comparing latency, bandwidth usage, and overhead. Furthermore, estimating the availability, credibility and confidentiality of security solutions within each party would take into consideration security concerns in edge computing and propose a safety assessment process for IoT networks with edge computing. This procedure is finally validated using appropriate tests, and the resulting findings are examined to demonstrate the method’s accuracy. Experimental data are used to validate methods to request maintenance and processing, response time, resource utilization and contract period. In comparison to current approaches, the results of the proposed CDML are measured with a percentage of 97.90%. The proposed system enhances the request and response comparison ratio 97.5%, analyzing request performance ratio 98.1%, response with time analysis ratio of 98.3%, data allocation approach analysis ratio 97.7%.

Blockchain-Secure Mobility Data in Smart Campus

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution’s work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel’s location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data.

Online and reliable SFC protection scheme of distributed cloud network for future IoT application

The implementation of intelligent device-free sensing (IDFS) necessitates a substantial number of edge IoT devices to extend the service coverage. Prior to deploying these IoT applications on a large scale, a reliable service provision scheme is imperative. In recent years, network function virtualization (NFV) technologies enable fast and convenient service provision by instantiating service function chain (SFC) in the distributed cloud network. However, ensuring resilient service provision is a challenge in NFV environment due to the vulnerability of software-defined network functions. Furthermore, the sequential arrangement of virtual network functions (VNFs) in SFC exacerbates the situation, as the malfunction of any VNFs within the SFC could result in the collapse of the associated service. In this paper, we firstly built a mathematical model for SFC online reliability protection and resource consumption optimization. We then propose a multi-mode VNF backup scheme to guarantee the different reliability requirements for various services. Furthermore, a novel SFC Online Reliability Protection (SORP) scheme is introduced to handle the dynamic SFC requests in distributed cloud network. The simulation results evince the efficacy of the SORP scheme in mitigating service failures and achieving a superior request acceptance ratio, while concurrently reducing average bandwidth consumption, which is particularly noteworthy in high latency scenarios.

Decentralized Control of Distributed Cloud Networks With Generalized Network Flows

Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty control theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies.

Ultra-Reliable Distributed Cloud Network Control With End-to-End Latency Constraints

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay-constrained least-cost dynamic network control problem</i> . To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable cloud network control</i> (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

Abstract 6393: The Cancer Genomics Cloud enables bioinformatics training in a hybrid educational world

Abstract The Cancer Genomics Cloud (CGC), powered by Seven Bridges, is an NCI-funded platform that streamlines access to large cancer datasets, bioinformatic tools, and cloud computation for cancer researchers. The attributes of the CGC designed to democratize data analysis also make it ideal for training the next generation of data scientists. During the Covid-19 pandemic, it has become clear that remote/virtual learning is of great importance for workforce development, and that reducing barriers to high quality educational resources is critical for many populations. Here we present our best practices for education of bioinformatics using the CGC, taking advantage of both distributed cloud networks and platform features to enhance learning. Our best practices methods are focused on a systematic approach that takes the instructor and students through a typical bioinformatics workflow in their field of research. Briefly, the organizational structure of the CGC, known as “projects,'' contains all aspects of an analysis, including data files, tools, and tool settings. Projects have fine-grained permission settings, which allow the owner to securely share their project for viewing or editing. An instructor can generate an example analysis from start to finish then share an entire project with trainees. Both students and instructors have access to the same data on the cloud, so the teacher can pre-populate the projects with specific files, ensuring the same starting point. All members within the project can communicate, including adding notes directly on the results, allowing students to troubleshoot in real time with the teacher. Instructors can manage costs for the whole class through the CGC’s billing system. The CGC also provides Public Projects for self-guided training, where a researcher can learn by example using the data, tools, and completed tasks within the project, including detailed instructions embedded in markdown language. We have successfully used these methods to train masters students in bioinformatics at Georgetown University for three years, as well as high school students, college students, and current cancer researchers. This approach to virtual learning of bioinformatics can democratize training for the next generation of data scientists. The FAIR practices built into the CGC enables education across all levels of expertise, empowering users to drive cancer research from any stage of their career. Citation Format: Manisha Ray, Sai Lakshmi Subramanian, David Roberson, Zelia Worman, Dennis A. Dean, Soner Koc, Jack DiGiovanna, Brandi Davis-Dusenbery. The Cancer Genomics Cloud enables bioinformatics training in a hybrid educational world [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6393.

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

Secure image secret sharing over distributed cloud network

A (k, n)-threshold secret image sharing (SIS) scheme protects a secret image by dividing it into n shadow images. When any k or more shadow images are collected, the secret image is reconstructed. While any (k−1) or fewer shadows give no clue about the secret. However, all previous SIS schemes do not deal with secure secret image recovery over distributed cloud network (DCN). To achieve the goal that reconstructing secret over DCN, private communication channels among all involved participants holding shadow images are required. A naive solution for this problem is to use an extra key distribution protocol that delivers a common key to all participants. In this paper, two (k, n)-SIS schemes over DCN (SISDCN) are introduced, where threshold property (recover the secret image from any k shadow images) and secure reconstruction over DCN (noninteractively share a common key among any k participants) are provided. Further, theoretical analysis, experiments and comparisons are conducted to show the effectiveness and advantages of the proposed schemes.

Resource allocation in the cloud with unreliable resources

We consider a resource allocation problem in a geographically distributed cloud network, where the goal is to obtain the capacities of the servers across the network in order to minimize the overall cost. In this study, the system resources (servers) are subject to failures, due to occasional breakdowns or cyber attacks. As a result, the servers supply is of a stochastic nature.From an optimization point of view, we are facing a non-convex multi-dimensional problem. For the solution, we propose an efficient algorithm to obtain the optimal capacities of the servers in all the regions in the network, where computational experience is presented and discussed.We then numerically analyze the effect of the supply stochastic properties, namely expected volume, variability and correlation across regions, on system performance. The methodology and the results can be used to evaluate the effect of cyber attacks on resource allocation in geographically distributed systems and on the planning of these systems.

Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals

The significant objective of research in the distributed cloud networks is resource planning and allocation using voids in scheduled intervals, which grabbed several researchers’ attention in contemporary literature. Minimum failures of resource scheduling, completion of a robust task and fair usage of the resource were the important parameters of resource scheduling schemes. Therefore, this paper projected scalable-resource scheduling method for distributed CC environments, which aimed to attain the metrics of scheduling. The projected method is known as "Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals (RPA-DCN)," where resources are scheduled towards the corresponding task so that, the optimum using of idle time of resource is attained. The projected method performs scheduling in sequential order, and they were allocation of optimum resource when no single resource is identified to allocate, then optimum manifold idle resources are allocated with a considerable filling of schedule intervals. This paper discusses (a) pre-requisite of resource scheduling schemes, (b) current scheduling methods found in recent literature, (c) methods &amp; materials utilized and method of projected resource scheduling scheme (d) and its significance over other standard methods. The analysis of performance for the proposal conducted by metrics cross-validation such as load vs. loss, optimality of task completion &amp; process-overhead in the resource-scheduling. Here, the simulation outcomes exhibit that the projected method is robust and scalable under modified metrics

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works.

Optimal Dynamic Cloud Network Control

Distributed cloud networking enables the deployment of a wide range of services in the form of interconnected software functions instantiated over general purpose hardware at multiple cloud locations distributed throughout the network. We consider the problem of optimal service delivery over a distributed cloud network, in which nodes are equipped with both communication and computation resources. We address the design of distributed online solutions that drive flow processing and routing decisions, along with the associated allocation of cloud and network resources. For a given set of services, each described by a chain of service functions, we characterize the cloud network capacity region and design a family of dynamic cloud network control (DCNC) algorithms that stabilize any service input rate inside the capacity region, while achieving arbitrarily close to minimum resource cost. The proposed DCNC algorithms are derived by extending Lyapunov drift-plus-penalty control to a novel multi-commodity-chain (MCC) queuing system, resulting in the first throughput and cost optimal algorithms for a general class of MCC flow problems that generalizes traditional multi-commodity flow by including flow chaining, flow scaling, and joint communication/computation resource allocation. We provide throughput and cost optimality guarantees, convergence time analysis, and extensive simulations in representative cloud network scenarios.

A Survey on Metrics and Measurement Tools for Sustainable Distributed Cloud Networks

Energy efficiency and emissions awareness are core capabilities for sustainable and lower cost distributed cloud networks. In this context, metrics are fundamental for comparison and management purposes, along with the methods and tools which support such metrics’ capture and analysis. However, prior works on green metrics and tools have presented only a partial view, mainly as a result of the recent advances in green networking technologies. In this survey, we present an extensive study of metrics, methods, and tools to support sustainable operations in distributed cloud networks, with the aim of providing an end-to-end and up-to-date scenario to support current and coming research, as well as to analyze existing gaps.

Smoothed Online Resource Allocation in Multi-Tier Distributed Cloud Networks

The problem of dynamic resource allocation for service provisioning in multi-tier distributed clouds is particularly challenging due to the coexistence of several factors: the need for joint allocation of cloud and network resources, the need for online decision-making under time-varying service demands and resource prices, and the reconfiguration cost associated with changing resource allocation decisions. We study this problem from an online optimization perspective to address all these challenges. We design an online algorithm that decouples the original offline problem over time by constructing a series of regularized subproblems, solvable at each corresponding time slot using the output of the previous time slot. We prove that, without prediction beyond the current time slot, our algorithm achieves a parameterized competitive ratio for arbitrarily dynamic workloads and resource prices. If prediction is available, we demonstrate that existing prediction-based control algorithms lack worst case performance guarantees for our problem, and we design two novel predictive control algorithms that inherit the theoretical guarantees of our online algorithm, while exhibiting improved practical performance. We conduct evaluations in a variety of settings based on real-world dynamic inputs and show that, without prediction, our online algorithm achieves up to nine times total cost reduction compared with the sequence of greedy one-shot optimizations and at most three times the offline optimum; with moderate predictions, our control algorithms can achieve two times total cost reduction compared with existing prediction-based algorithms.

Reinventing Cloud with Blockchain

We need to shift our perspective of blockchain, from just the programmable ledger, to a networked infrastructure of computing machinery. Doing so, we could easily visualize how computer programs will operate over this new infrastructure, which is presently being used for transaction validations.We cannot take the cloud computing analogy literally because the blockchain infrastructure can’t replace cloud computing completely. Although, it unbundles and democratizes cloud computing. Thus, companies and individual consumers can soon keep their data in distributed cloud network based on highly secure blockchain technology.

IoT-Cloud Service Optimization in Next Generation Smart Environments

The impact of the Internet of Things (IoT) on the evolution toward next generation smart environments (e.g., smart homes, buildings, and cities) will largely depend on the efficient integration of IoT and cloud computing technologies. With the predicted explosion in the number of connected devices and IoT services, current centralized cloud architectures, which tend to consolidate computing and storage resources into a few large data centers, will inevitably lead to excessive network load, end-to-end service latencies, and overall power consumption. Thanks to recent advances in network virtualization and programmability, highly distributed cloud networking architectures are a promising solution to efficiently host, manage, and optimize next generation IoT services in smart environments. In this paper, we mathematically formulate the service distribution problem (SDP) in IoT-Cloud networks, referred to as the IoT-CSDP, as a minimum cost mixed-cast flow problem that can be efficiently solved via linear programming. We focus on energy consumption as the major driver of today’s network and cloud operational costs and characterize the heterogeneous set of IoT-Cloud network resources according to their associated sensing, computing, and transport capacity and energy efficiency. Our results show that, when properly optimized, the flexibility of IoT-Cloud networks can be efficiently exploited to deliver a wide range of IoT services in the context of next generation smart environments, while significantly reducing overall power consumption.

In-Network View Synthesis for Interactive Multiview Video Systems

In multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints, allowing users to freely navigate within a 3D scene. However, bandwidth constraints may restrict the number of reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from the cloudlet to the users, re-sampling at the couldlet the viewpoints of the 3D scene (i.e., synthesizing novel virtual views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere subsampling of the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints. We prove that the problem is NP-hard, and we propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem under general assumptions that cover most of the multiview scenarios in practice. Simulation results confirm the performance gain offered by virtual view synthesis in the network.

A healthcare information sharing scheme in distributed cloud networks

Current information & communication technology infrastructures are struggling for a wide variety of devices, services, and business and technology evolution. Cloud computing is becoming a promising...

A fault tolerant channel allocation scheme in distributed cloud networks

With the cloud computing, storage and computing resources are moving to remote resources such as virtual servers and storage systems in large datacenters, which raise many performance and management challenges. The distributed cloud networks must have an ability to maintain an acceptable level of service during network failures to provide high resilience against service failures. This paper presents an eccentric fault tolerant path grouping scheme that can achieve mobility independent Quality of Service guaranteed services in distributed cloud networks To make this possible, we propose a path allocation and restoration strategy called "Reverse Shared Risk Link Group" and demonstrates how this concept can be applied to minimize recovery contention in cloud networks and to assess the network convergence associated with failures. To reflect little more actual situations, we assume that the bandwidth of cloud connection requests can be some fraction of the lightpath capacity. Simulation results show that the proposed recovery scheme outperforms other schemes with fewer signaling traffics and contention probability. Very fast restorability can be accomplished for multiple failures. We also has developed an analytical model and performed analysis for the proposed scheme in terms of two performance factors: mean system time and reservation blocking probability.