Group Of Servers Research Articles

The equitable non-split domination number of graphs

Finding a group of dominant servers is a necessary step towards optimising service provisioning to all clients in the complex world of server-client networks. This crucial decision rests on the non-split dominance number in the graph architecture. We present the idea of an equitable non-split domination number in graphs to address the possible problem of unequal task distribution among servers. When the vertices of a dominating set, Dens, have the same colour in an equitable colouring scheme, the set is said to be equitable. The induced subgraph ⟨V −D⟩ also maintains its connectivity. In particular, the application of equitable non-split domination numbers to graphs like the Triangular Grid, Antiprism, Wheel, and Complete Graphs is explored in this study.

Verifiable Additive Homomorphic Secret Sharing with Dynamic Aggregation Support

(n,m,t)-Homomorphic Secret Sharing (HSS) allows n clients to share data secretly to m servers, which compute a function f homomorphically on the received secretly shared data while restricting the input data acquired by a collection of t servers to private ones. In Verifiable Homomorphic Secret Sharing (VHSS), if there are partially colluding malicious servers submitting erroneous computation results to the client, such erroneous computation results will be rejected by the client. In traditional static homomorphic secret sharing schemes, once a secret share of raw data is assigned to a group of servers, then all servers in the group must participate in the computation, which means that the computation has to be restarted once some servers fail to perform the task. In order to solve the above problem, we propose the first dynamic homomorphic secret sharing scheme for additive computation in this paper. In our scheme, once some servers fail, there is no need to recalculate the secret sharing but only the need to reissue the index set of servers that perform the computation, Our structure assigns more computation to the servers, which is very useful in real scenarios. In addition, we propose dynamic verifiable homomorphic secret sharing schemes based on the above schemes, which have less computational overhead compared to the existing schemes, although we sacrifice the public verifiability property. Finally, we give a detailed correctness, security, and verifiability analysis of the two proposed schemes and provide the theoretical and experimental evaluation results of the computational overhead.

Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.

RNDLP: A Distributed Framework for Supporting Continuous k-Similarity Trajectories Search over Road Network

Continuous k-similarity trajectories search over a data stream is an important problem in the domain of spatio-temporal databases. Given a set of trajectories T and a query trajectory Tq over road network G, the system monitors trajectories within T, reporting k trajectories that are the most similar to Tq whenever one time unit is passed. Some existing works study k-similarity trajectories search over trajectory data, but they cannot work in a road network environment, especially when the trajectory set scale is large. In this paper, we propose a novel framework named RNDLP (Road Network-based Distance Lower-bound-based Prediction) to support CKTRN over trajectory data. It is a distributed framework based on the following observation. That is, given a trajectory Ti and the query trajectory Tq, when we have knowledge of D(Ti), we can compute the lower-bound and upper-bound distances between Tq and Ti, which enables us to predict the scores of trajectories in T and employ these predictions to assess the significance of trajectories within T. Accordingly, we can form a mathematical model to evaluate the excepted running cost of each trajectory we should spend. Based on the model, we propose a partition algorithm to partition trajectories into a group of servers so as to guarantee that the workload of each server is as the same as possible. In each server, we propose a pair-based algorithm to predict the earliest time Ti could become a query result, and use the predicted result to organize these trajectories. Our proposed algorithm helps us support query processing via accessing a few points of a small number of trajectories whenever trajectories are updated. Finally, we conduct extensive performance studies on large, real, and synthetic datasets, which demonstrate that our new framework could efficiently support CKST over a data stream.

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC).

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.

AttFL

This work presents AttFL, a federated learning framework designed to continuously improve a personalized deep neural network for efficiently analyzing time-series data generated from mobile and embedded sensing applications. To better characterize time-series data features and efficiently abstract model parameters, AttFL appends a set of attention modules to the baseline deep learning model and exchanges their feature map information to gather collective knowledge across distributed local devices at the server. The server groups devices with similar contextual goals using cosine similarity, and redistributes updated model parameters for improved inference performance at each local device. Specifically, unlike previously proposed federated learning frameworks, AttFL is designed specifically to perform well for various recurrent neural network (RNN) baseline models, making it suitable for many mobile and embedded sensing applications producing time-series sensing data. We evaluate the performance of AttFL and compare with five state-of-the-art federated learning frameworks using three popular mobile/embedded sensing applications (e.g., physiological signal analysis, human activity recognition, and audio processing). Our results obtained from CPU core-based emulations and a 12-node embedded platform testbed shows that AttFL outperforms all alternative approaches in terms of model accuracy and communication/computational overhead, and is flexible enough to be applied in various application scenarios exploiting different baseline deep learning model architectures.

Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

Incentive-Based Distributed Resource Allocation for Task Offloading and Collaborative Computing in MEC-Enabled Networks

Computing tasks offloaded from user devices (UDs) can be carried out by one or more mobile edge computing (MEC) servers to alleviate the computing burden of UDs. The incentive is needed to encourage MEC servers to provide their computing services to other network nodes. In this paper, inspired by the fact that bargaining games have the available features of incentive, self-enforcement, and satisfaction for all participants, we propose a two-level bargaining-based incentive mechanism for task offloading and collaborative computing in MEC-enabled networks. In the first-level bargaining between UDs and local MEC server (LMECS), both UDs and LMECS try to maximize their respective offloading utilities, which are all defined as a saved-cost function considering the time and energy consumption of task execution, and computing service fees. The task offloading decision, uplink transmitting power of UDs, computing resource allocation of LMECS, and the fees paid by UDs to LMECS are jointly optimized. When large computing tasks are offloaded to LMECS, which results in LMECS overload, the second-level bargaining is proposed to achieve a computing load balance of LMECS and maximize the respective collaboration utilities of LMECS and collaborative MEC server group (CMECG), in which the optimized normalized fees paid by LMECS to CMECG for additional computing resources are obtained. The first-level and the second-level bargainings are proved to be quasi-concave and concave respectively and each has a unique Nash bargaining solution (NBS). The simulation results show that the proposed method gets better performance than benchmark methods.

Hierarchical federated learning with local model embedding

Federated learning can synergize the local model training with private data samples from geo-distributed users. Nevertheless, the unification process of a comprehensive global model through periodical parameter sharing can be time-consuming at a high cost. On the one hand, the data samples are collected from users with diverse preferences, and the data distribution can be non-independent and identically distributed (non-IID). On the other hand, the consequent model trained locally needs to communicate with a remote parameter server periodically for parameter synchronization, which leads to overwhelming communication and synchronization overhead given heterogeneous device capacities and network conditions of end-users. Generally, a hierarchical system design with a clustered group is ideal for accommodating diversity. Actually, it is still challenging to maintain the relationship of the local model training without knowing the data samples in advance for privacy concerns. Therefore, we present hierarchical federated model embedding to formulate the relationship between local data distributions. Initially, the local models are embedded through the global shared dataset to obtain feature latent representation vectors. The cloud server groups the clients according to the vectors, making clients with similar data distribution train collaboratively in a same group. Then, these vectors are used to train the predictor on the cloud server, which is utilized for efficient group assignment when new clients join the system. Compared with the baseline, the accuracy of the group model can be improved by 1.22%∼5.63% and that of the global model can be improved by 3.97%∼14.25% on different datasets.

Analysis of a Queuing System with Possibility of Waiting Customers Jockeying between Two Groups of Servers

In this paper, we consider a queueing system consisting of two multi-server subsystems that is designed for the service of clients arriving at a system according to a Markovian arrival process (MAP). Arriving clients receive information about the number of clients present in both subsystems and use this information to make a randomized decision to balk (depart without receiving service) or join the system. In the latter case, they also decide which subsystem they would like to join. One subsystem has an infinite buffer, while the buffer of the second subsystem is finite. The service time distribution is exponential in the first subsystem and phase-type in the second subsystem. During the waiting in the chosen buffers, after the random time intervals, each waiting client checks the status of the alternative subsystem. If some server in that subsystem is idle during this epoch, the client immediately leaves the buffer where it has been staying and starts a service in the alternative subsystem. The problem of computing the steady-state distribution of this system is solved. The feasibility of the proposed solution and certain features of the system’s behavior are numerically illustrated.

Deployment of a fault-tolerant videoconferencing server for a virtualization-based judicial system

Objective. The article discusses the issue of creating an infrastructure solution that allows, on the basis of modern information technologies, to organize a fault-tolerant videoconferencing server in the judicial system. The choice of technologies was determined by the need to use a video conferencing server in multi-user mode, as well as the convenience and ease of administration. The use of virtualization for the videoconferencing server will allow flexible functioning of both the system as a whole and all its components, isolate the use of resources, and configure monitoring to monitor the behavior of the system. The advantage of the solution presented in the article is its cost, which will be expressed only in the presence of a server grouping or its lease (instead of a monthly or annual fee to commercial organizations). Also, the advantages include the presence of an engineer of an average level of preparedness in the staff structure of the organization.

Efficient Caching in Vehicular Edge Computing Based on Edge-Cloud Collaboration

In vehicular edge computing (VEC), the execution of offloading task needs not only the task data uploaded by the requesting vehicle, but also the additional data to support the task to be executed successfully, and how to efficiently cache and access these supporting data becomes the key issue for task offloading in VEC. In this paper, we study the efficient caching mechanism to minimize the acquisition delay of the supporting data. Firstly, with the software defined network (SDN) based VEC framework, we analyze the acquisition ways of the supporting data and the caching collaboration between VEC servers. Then, according to the density of the requesting vehicles, we divide the VEC coverage into dense and ordinary areas. With the consideration of the similarity of the requested data and the distance between edge servers, the edge servers are clustered into multiple groups based on K-mean++ algorithm. Finally, each server's storage space is divided into three partitions, and the most beneficial data for itself, its group and the whole system are respectively stored in these partitions. Based on service area dividing, server grouping and storage space partitioning, we propose an efficient edge-cloud collaborative caching strategy, which can reduce the delay of data migration while task execution. Simulation results show that, compared with other schemes, the proposed caching strategy has better performance in terms of average data migration delay and application QoS.

Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing

Recently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for data filtering and aggregation. Each user may collect multiple data types in mobile collective sensing. For facilitating data aggregation, the same data type carried by various users is assumed to be uploaded to the same mobile edge server. The main problem is determining the server which should be activated to process each data type for reducing the overall cost. In this paper, the problem is formulated as one form of the unqualified multicommodity facility location problem. To solve this problem, two edge-server location strategies are proposed, which use a clustering method for dividing the set of mobile users with data items into clusters and use the ant colony approach to select a mobile edge server for each data type in each cluster. Extensive simulations are conducted based on widely used real data sets. The simulation results show that the proposed strategy achieves better performance than the existing methods in terms of service and facility costs.

Erlang loss systems with shortest idle server first service discipline: Maintenance considerations

We consider a variation of an Erlang loss system in which jobs are routed to servers according to the Shortest Idle Server First service discipline. Specifically, we consider a system in which idle servers are arranged in a stack; servers are returned to the top of the stack upon service completion; and arriving jobs are assigned to the server currently at the top of the stack. When busy, servers accumulate age and incur an age-dependent operating cost. For such systems, we (i) formulate a continuous-time Markov chain model to characterize the system’s transient behavior, and (ii) develop maintenance policies consisting of two possible actions: server group replacement and stack inversion. The stack inversion may be performed at any time prior to group replacement to achieve a more evenly distributed utilization among servers. We develop an optimization model to determine the optimal inversion and replacement times so as to minimize the long-run expected cost rate. Because the model is nonlinear and non-convex, we develop a set of algorithms to solve for the optimal replacement and inversion time. Lastly, we establish a lower bound for the inversion cost threshold below which it is optimal to invert the stack of servers before their replacement.

A security scheme for distributing analysis codes supporting CDM-based research in a multi-center environment

Background: Although the common data model (CDM) has achieved a standardization of medical data and a de-identification of personal patient information, hospitals still store CDM data in an on-premises environment, making it difficult for researchers to access medical data.Objective: In this study, for easy access to CDM data in a multi-institutional participatory CDM research environment and to encourage data-driven research, researchers outside hospital networks securely access and analyze CDM data in the target medical center, analyze it, and respond to the results through a public network. We propose an automated security framework that operates on a public network, such as the Internet.Method: The proposed scheme allows authenticated researchers to securely deliver CDM data analysis codes to a medical institution distributed on the network. The institutional servers automatically execute authenticated codes and return the results to the researcher safely. For this purpose, we designed a scheme based on cryptography. The scheme operates on a group of servers consisting of an authentication process, a signing process, a ticket-granting process, a relaying process, and a data analysis process located at the hospital providing medical CDM data. The scheme consists of four phases for a secure medical data analysis in a distributed environment: authentication, code signing, ticket issuing, and distribution and return.Results: Although the CDM has de-identified patient privacy, the issue still needs to be carefully addressed. Therefore, we established four security objectives to verify that the proposed scheme can be operated safely and formally proved them using BAN logic.Conclusion: As a result of the proof using BAN logic, the proposed scheme was verified to achieve the proposed security goal. Although this scheme was designed solely for CDM, it can be applied to systems with similar environments and functional goals.

Experimental study on combustion characteristics of fire in data room

Through the combustion experiments on the cables and servers in a data room, the combustion characteristic parameters of the materials were obtained, and the materials were analyzed and evaluated from the aspects of heat release rate, gas concentration, flue gas temperature, and so on. The results show that network cable and electric cable can be ignited during the cable combustion test. In the overall fire combustion experiment of single server and the server group with three cabinets, the fire process is divided into three stages: initial growth stage, stable combustion stage, and combustion attenuation stage. The heat release rate, flue gas temperature, and CO2 concentration of the overall experiment have three peaks, respectively, 58, 98, and 134 kW; 100°C, 152°C, and 232°C; 1314, 2514, and 2988 ppm. The research results can provide some reference for design of fire protection system in data rooms.

Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing

Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products, processes, and applications with limited training data while preserving data privacy. In this article, a new federated transfer learning framework, federated transfer learning for cross-domain prediction, is proposed to address the challenges of data scarcity and data privacy faced by most machine learning approaches in modern smart manufacturing with cross-domain applications. The framework architecture consists of a central server and several groups of smart devices, where each group handles a different application. The existing applications can share their knowledge through the central server as base models, while new applications can convert a base model to their target-domain models with limited application-specific data using a transfer learning technique. Meanwhile, the federated learning scheme is deployed within a group to further enhance the accuracy of the application-specific model. The integrated framework allows model sharing across the central server and different smart devices without exposing any raw data and, hence, protects the data privacy. Two public datasets, COCO and PETS2009, which represent the source and target applications, are employed for evaluations. The simulation results show that the proposed method outperforms two state-of-the-art machine learning approaches by achieving better learning efficiency and accuracy.

Distributed Business Process Discovery in Cloud Clusters

The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.

Business Process Discovery Using Process Mining Techniques and Distributed Framework

The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.