Group Of Servers Research Articles

Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarms

A-Prot: protein structure modeling using MSA transformer

BackgroundThe accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary.ResultsIn this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14.ConclusionThese results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.

Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers

In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast setup process occurs when the server’s states are changed from sleep to work. The servers in Group 1 are faster and cheaper than those of Group 2 so that Group 1 has a higher service priority. Putting each server in Group 2 to sleep can reduce system costs and energy consumption, but it must bear setup costs and transfer costs. For such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, both of which determine the switch rule between the work and sleep states for each server in Group 2. To find the optimal asynchronous dynamic policy, we apply the sensitivity-based optimization to establish a block-structured policy-based Markov process and use a block-structured policy-based Poisson equation to compute the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that a bang–bang control is always optimal for this optimization problem. We hope that the methodology and results developed in this paper can shed light on the study of more general energy-efficient data centers.

A c/μ‐Rule for Job Assignment in Heterogeneous Group‐Server Queues

We study a dynamic job assignment problem in queueing systems with one class of Poisson arrivals and K groups of heterogeneous servers. A scheduling policy prescribes the job assignment among servers in each group at every state n (number of jobs in the system). Our goal is to obtain the optimal policy to minimize the long‐run average cost, which involves the increasingly convex holding cost for jobs and the operating cost for working servers. This problem has wide application scenarios in operations management, such as job scheduling in manufacturing systems, packet routing in communication systems, and staffing in service systems. We prove that the optimal policy has monotone structures and quasi bang–bang control forms. Specifically, we discover that the optimal policy is governed by the marginal cost rate c − μG( n), where c is the operating cost rate, μ is the service rate, and G( n) is called the perturbation realization factor at state n. Under the condition of scale economies which can be guaranteed by any increasingly concave operating cost in μ, we prove that the optimal policy obeys a so‐called c/ μ‐ rule: Servers with a smaller c/ μ should be occupied by jobs with higher priority. Optimality of multi‐threshold type policies is further proved when the c/ μ‐rule is applied. Our c/ μ‐rule in group‐server queues can be viewed as a counterpart of the famous cμ‐rule in polling queues, which both significantly reduce the complexity of optimization problems. By utilizing these optimality structures, we also develop computational‐efficient algorithms to determine the optimal policy numerically. Simulation experiments demonstrate the good scalability and robustness of the c/ μ‐rule, which are important for managerial practice.

LB Scalability: Achieving the Right Balance Between Being Stateful and Stateless

A high performance Layer-4 load balancer (LB) is one of the most important components of a cloud service infrastructure. Such an LB uses network and transport layer information for deciding how to distribute client requests across a group of servers. A crucial requirement for a stateful LB is per connection consistency (PCC); namely, that all the packets of the same connection will be forwarded to the same server, as long as the server is alive, even if the pool of servers or the assignment function changes. The challenge is in designing a high throughput, low latency solution that is also scalable. This paper proposes a highly scalable LB, called Prism, implemented using a programmable switch ASIC. As far as we know, Prism is the first reported stateful LB that can process millions of connections per second and hundreds of millions connections in total, while ensuring PCC. This is due to the fact that Prism forwards all the packets in hardware, even during server pool changes, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">while avoiding the need to maintain a hardware state per every active connection</b> . We implemented a prototype of the proposed architecture and showed that Prism can scale to 100 million simultaneous connections, and can accommodate more than one pool update per second.

Estimation of the Performance Measures of a Group of Servers Taking into Account Blocking and Call Repetition before and after Server Occupation

The model of a fully available group of servers with a Poisson flow of primary calls and the possibility of losses before and after occupying a free server is considered. Additionally, a call can leave the system because of the aging of transmitted information. After each loss, there is some probability that a customer repeats the call. Such models are seen in the modeling of various telecommunication systems such as emergency information services, call and contact centers, access nodes, etc., functioning in overloading situations. The stationary behavior of the system is described by the infinite-state Markov process. It is shown that stationary characteristics of the model can be calculated with the help of an auxiliary model of the same class but without call repetitions due to losses occurring before and after the occupation of a free server and the aging of transmitted information. The performance measurements of the auxiliary model are calculated by solving a system of state equations using a recursive algorithm based on the concept of the truncation of the used state space. This approach allows significant savings of computer resources to be made by ignoring highly unlikely states in the process of calculation. The error caused by truncation is estimated. The presented numerical examples illustrate the use of the model for the elimination of the negative effects of emergency information service overload based on the filtering of the input flow of calls.

Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.

The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi‐scale network, i.e., Res2Net, for improved prediction of inter‐residue geometries, including distance and orientations. The second is an attention‐based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM‐score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi‐scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.

Anaysis of Different Energy Efficiency Algorithms in Cloud Computing

Cloud computing is internet computing that offers metering-based services to customers. It implies accessing data from a consolidated pool of computer resources that may be requested and consumed on-demand. It also offers computer resources via virtualized over the internet. The data center is the most essential in cloud computing which includes a group of servers on which Business records are kept and applications operate. The data center which contains servers, cables, air conditioning units, networks, etc. uses more electricity and emits a large quantity of Carbon-di-oxide (CO2) to the atmosphere. One of the most significant problems encountered in cloud technology is the optimizing of Energy Usage. Hence the idea of cloud computing originated. It is a concept for allowing all over, on-demand access to a shared pool of customizable computer resources[ Wanneng Shu et. A.,2014].

Rerouting Strategies for Highly Available Virtual Network Functions

The development of Virtual Network Functions (VNFs) migrates network functions from dedicated hardware to groups of commodity servers called network points of presence (N-PoPs). In this way, network services are redefined as interconnected VNFs called Service Function Chains (SFCs). The emerging of SFCs significantly reduces the cost of network services and improves scalability. However, the availability of SFCs brings new challenges since a failure of any N-PoP along an SFC affects its availability. In this paper, we propose two rerouting strategies to improve the availability of SFCs. First, we propose a local rerouting strategy to bypass the failed N-PoPs on SFCs using locally rerouted paths (LRPs). In the strategy, we formulate an optimization model to minimize the maximum load on links while deploying SFCs and LRPs to reduce the risk of congested links caused by local rerouting. We then propose an approximation algorithm to solve the optimization problem, preserving an approximation ratio of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log (|V|))$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> is the number of N-PoPs in the network. We also propose an alternative heuristic algorithm to improve efficiency. Second, we propose a supplementary rerouting strategy with an online algorithm to provide supplementary rerouted paths when original SFCs and corresponding LRPs fail at the same time in the local rerouting strategy. The online algorithm is proved to have an <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log (|V|))$</tex-math></inline-formula> competitive ratio to the offline optimum. Finally, our extensive simulation results show that the proposed algorithms can provide highly available SFCs with less congested links.

A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting

Hospitals have their own database structures and maintain their data in a closed manner. For this reason, it is difficult for researchers outside of institutions to access multi-center data. Therefore, if the data maintained by all hospitals follow a commonly shared format, researchers can analyze multi-center data using the same method. To safely analyze data using a common data model (CDM) in a distributed multi-center network environment, the objective of this study is to propose and implement the processes for distribution, executing the analysis codes, and returning the results. A secure CDM-based data analysis platform (SCAP) consists of a certificate authority (CA), authentication server (AS), code signer (CS), ticket-granting server (TGS), relaying server (RS), and service server (SS). The AS, CS, TGS, and RS form the central server group of the platform. An SS is stored on a hospital server as an agent for communication with the server group. We designed the functionalities and communication protocols among servers. To safely conduct the intended functions, the proposed protocol was implemented based on a cryptographic algorithm. An SCAP was developed as a web application running on this protocol. Users accessed the platform through a web-based interface.

Target classification in the 14th round of the critical assessment of protein structure prediction (CASP14).

An evolutionary-based definition and classification of target evaluation units (EUs) is presented for the 14th round of the critical assessment of structure prediction (CASP14). CASP14 targets included 84 experimental models submitted by various structural groups (designated T1024-T1101). Targets were split into EUs based on the domain organization of available templates and performance of server groups. Several targets required splitting (19 out of 25 multidomain targets) due in part to observed conformation changes. All in all, 96 CASP14 EUs were defined and assigned to tertiary structure assessment categories (Topology-based FM or High Accuracy-based TBM-easy and TBM-hard) considering their evolutionary relationship to existing ECOD fold space: 24 family level, 50 distant homologs (H-group), 12 analogs (X-group), and 10 new folds. Principal component analysis and heatmap visualization of sequence and structure similarity to known templates as well as performance of servers highlighted trends in CASP14 target difficulty. The assigned evolutionary levels (i.e., H-groups) and assessment classes (i.e., FM) displayed overlapping clusters of EUs. Many viral targets diverged considerably from their template homologs and thus were more difficult for prediction than other homology-related targets. On the other hand, some targets did not have sequence-identifiable templates, but were predicted better than expected due to relatively simple arrangements of secondary structural elements. An apparent improvement in overall server performance in CASP14 further complicated traditional classification, which ultimately assigned EUs into high-accuracy modeling (27 TBM-easy and 31 TBM-hard), topology (23 FM), or both (15 FM/TBM).

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Distance-guided protein folding based on generalized descent direction.

Advances in the prediction of the inter-residue distance for a protein sequence have increased the accuracy to predict the correct folds of proteins with distance information. Here, we propose a distance-guided protein folding algorithm based on generalized descent direction, named GDDfold, which achieves effective structural perturbation and potential minimization in two stages. In the global stage, random-based direction is designed using evolutionary knowledge, which guides conformation population to cross potential barriers and explore conformational space rapidly in a large range. In the local stage, locally rugged potential landscape can be explored with the aid of conjugate-based direction integrated into a specific search strategy, which can improve the exploitation ability. GDDfold is tested on 347 proteins of a benchmark set, 24 template-free modeling (FM) approaches targets of CASP13 and 20 FM targets of CASP14. Results show that GDDfold correctly folds [template modeling (TM) score≥=0.5] 316 out of 347 proteins, where 65 proteins have TM scores that are greater than 0.8, and significantly outperforms Rosetta-dist (distance-assisted fragment assembly method) and L-BFGSfold (distance geometry optimization method). On CASP FM targets, GDDfold is comparable with five state-of-the-art full-version methods, namely, Quark, RaptorX, Rosetta, MULTICOM and trRosetta in the CASP 13 and 14 server groups.

Privacy-Preserving Efficient Data Retrieval in IoMT Based on Low-Cost Fog Computing

The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

Dynamic Deployment and Scheduling Strategy for Dual-Service Pooling-Based Hierarchical Cloud Service System in Intelligent Buildings

Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hierarchical cloud platform with dual-service pooling structure and a dynamic strategy for the proposed model. The analytic hierarchy process (AHP) based quality of service (QoS) evaluation mechanism and the dynamic normal distribution selection method are adopted for service deployment. And the dynamic inertia particle swarm optimization (DI-PSO) algorithm is employed to realize task scheduling. Furthermore, the cloud platform and existing terminal server group are used to conduct platform structure comparison experiments, and the popular task scheduling algorithms are selected for simulation experiments. Experimental results of platform measurement show that the average service response time of different services can be improved by about 17.3% to 37.4%. The average occupancy ratio of computing resources can be reduced by about 5%. The simulation results show that the earliest completion time of single task list can be decreased by 11.3% to 20.9%, and the makespan of 100 task lists can be improved by 0.3 times.

Difference Equations Approach for Multi-Server Queueing Models with Removable Servers

We consider an extended form of the MX/M/c queue with two types of server groups: Static as well as dynamic (which turn on/off in a state-dependent manner) servers. The two server groups may have homogenous or non-homogenous service rates. The model is further extended to feature setup and delayed-off times, finite capacity, and k staffing levels. This class of queues is solved via the difference equations approach, which addresses narratives in the literature and achieves higher numerical efficiency than the direct method. While the model of this queueing system is not new, the methodology for solving it is. Comparisons between our model and classic queues are provided followed by concluding remarks, including a summary of key observations.

Minimum-Cost Edge-Server Location Strategy in Mobile Crowdsensing

Mobile crowdsensing has become a significant sensing technique which takes advantage of mobile devices to collect information about the surrounding. The traditional cloud-based centralized mobile crowdsensing architecture generates significant traffic on networks and computation burden on the cloud. In this paper, we investigate the edge-based mobile crowdsensing architecture, where a group of mobile edge servers is deployed at network edge as the bridge between the central server and mobile users for data filtering and aggregation. Each user may collect multiple types of data in mobile crowdsensing. To facilitate data aggregation, the same type of data carried by different users is supposed to be uploaded to the same mobile edge server. In this scenario, a problem emerges: which server should be activated for processing each type of data in order to minimize the total cost? The cost consists of the facility cost (activating server and processing data) and the service cost (the users' movement cost for uploading data). Furthermore, the problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. In particular, two situations of the problem are studied in our work: (1) for the situation where each user carries at most two types of data, we propose a relaxation based approximation algorithm, which is proved to have a bound to the optimal solution; (2) for a more generalized situation where each user can carry multiple types of data, we propose a connected multi-agent simulated annealing algorithm. Finally, we conduct extensive simulations based on the widely-used real-world datasets: roma/taxi, epfl/mobility and geolife trajectory. The simulation results show that the proposed algorithms demonstrate their superiority over baseline methods and are consistent with the theoretical analysis.

RETRACTED: Design of Russian corpus based on embedded system and cloud computing

Russian Learner Corpus (Radio Link) first corpus and foreign language learners , and there is a noticeable difference between traditional speakers. Russian Student Texts Corpus (CoRST) Language Lab is a computational and program for holes initiated by corpus. It will be a collection of Russian texts written by students of different Russian universities. Its main goal is to study language changes and mark language deviations. CoRST is provided with Metalanguage, morphology, and error specifications. Usually, an error part is marked with incorrect labels because it can be fixed in many ways. Also, corpus offers a search by spelling and construction compounds for two possible descriptive factors. Cost growth prospects need two calculations and methods—CoRST and legal and comparative statistical analysis of NRC language events. The define predictive methods for problem formulation and adjustment of the calculation model. The purpose of this work is to explain the answer to this problem. Combine sophisticated computing and computing prototype strategies to provide a flexible execution framework for cloud embedded or mobile devices as a test; an accreditation system developed. Cloud computing allows for advantages such as smooth performance, shortened development time, simpler embedded systems, and is considered impossible. Cloud computing functionality and performance is a collection of potential hardware and software remote server groups Centralized data storage and transfer Data services Online access to prediction resources.

Evaluation of Knowledge, Effectiveness, and Challenges of Picture Archiving and Communications System in National Orthopaedic Hospital Dala-Kano, Nigeria

Background: Picture Archiving and Communications System (PACS) is a networked group of computers, servers, and archives that can be used to manage digital images, specifically designed for the radiology department. Aims: The study aims at evaluating the knowledge, effectiveness, and challenges of picture archiving and communications system (PACS) in National Orthopaedic Hospital Dala-Kano, Nigeria. Materials and Methods: The study design was cross-sectional, and it was prospectively carried out from July 2020 to December 2020. A total number of 120 semi-structured questionnaires were distributed to consented respondents for the purpose of data collection. The validity and reliability of the questionnaire were tested. The collected data were analyzed using SPSS version 21.0. Results: The results showed that 78 (85%) of the respondents had a better knowledge on how to use PACS, 80 (87%) admitted that PACS saves a lot of their time, and 91 (99%) admitted that PACS improves the quality of their work in providing better patient care. Eighty-five (92%) admitted that images on PACS have diagnostic information and 73 (79%) were satisfied with the quality of images on PACS. Challenges faced by the PACS user include epileptic power supply, unavailability of PACS computers at all service points, slow network, improper patient identification, and sometime poor image quality. Conclusion: This study shows that majority of the respondents had good knowledge of PACS and it was highly effective.

Systematic Approach for Generation of Feasible Deployment Alternatives for Microservices

Microservice architectures rely on the development of modular and independent software units, which typically address a single task and communicate with other microservices via well-defined interfaces. This has several benefits such as easier maintenance and update of services. However, deploying a microservice-based application is often more complicated than a monolithic application. While a monolithic application can be deployed one at a time on a group of similar servers behind a load balancer, a microservice-based application consists of different microservices and each microservice usually has more than one runtime instance that needs to be configured and deployed. For a small number of microservices and applications, the deployment could be done manually. However, a large number of microservices are frequently observed in practice. In such cases, the deployment becomes cumbersome and error-prone and does not scale with the increased number of services. To cope with this problem, we present a systematic approach and the corresponding tool support for enabling the deployment of microservices to resources that have limited capacity. Hereby, we model and define the design space given the deployment parameters and automatically derive the feasible deployment solution. The approach is validated using a taxi-hailing system case study inspired by Uber which has spread all over the world in recent years.