Backup Server Research Articles

Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

AbstractWith the depletion of sources of primary energy across the world, the prospect of a global energy crisis in the near future has raised considerable concern. Among the many sources of clean energy, wind energy is considered to be the most effective means of sustainable and environmentally friendly energy. Traditional systems of energy based on oil and natural gas still operate in a discrete manner, and the promise offered by the mutual coordination between energy systems to draw complementary benefits has not yet been fully exploited. This has prompted all energy sectors to examine the optimal integration of interconnected gas–electricity energy systems. In this context, this study proposes a multi‐objective collaborative operational optimization model of P2G for an interconnected, integrated gas–electricity energy system in case of uncertain wind power. Based on the effect of carbon capture by using P2G, the effect of coupling gas and electricity, and the cost of low‐capacity reserves, the authors use conditional risk values to design four targets of optimization—the operating cost of the system, rate of consumption of the natural gas system, total carbon emissions and capacity stand‐by cost—according to the power system, natural gas system, thermal energy system, and their elements of coupling from multiple perspectives to clarify the constraints on the model. Moreover, to reduce the likelihood of the optimal solution falling into the local optimum, the improved cuckoo algorithm is used to optimize the multi‐objective scheduling model. The authors used a 10‐node power system and a six‐node natural gas system as examples for simulation to verify the proposed model. The results of empirical analysis show the following: (a) Once the P2G equipment had been connected, it reduced the total operating cost and improved the consumption of wind power of the natural gas system regardless of whether backup services were provided. (b) The P2G equipment could be combined with gas‐fired CHP units to “cut peaks and fill valleys” in the electrical load of the power system. These results verify the effectiveness of the proposed multi‐objective optimal dispatch model of an interconnected gas–electricity energy system.

A Location-Inventory-Pricing Supply Chain Network Design for Perishable Products Under Disruptions

In this study, we discuss a location-inventory-pricing model considering the capacity constraints of the warehouses, disruption, and multiple perishable products. We extend a model that assumes that warehouses may face disruption, failed warehouses cannot cover any service, and their customers are assigned to other warehouses. To decrease the risk of disruption, we examine the efficiency of markup pricing strategy and support services. The objective function of this MINLP is to maximize the total profit of warehouses. To solve this model, Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA) are used. To evaluate the recommended model, several sensitivity analyses are proposed. Finally, the results of numerical experiments implicate the high-performance of GOA in dealing with problems and achieving better results. According to the results, backup services and markup pricing strategies are very effective in reducing the damage caused by the disruption.

IMPLEMENTASI LOAD BALANCING SISTEM CLUSTER SERVER PADA WEB SERVER UNTUK JAMINAN KETERSEDIAAN LAYANAN TINGGI

Website services have become especially important thing and are the primary need of everyone’s information needs. This High information needs require website services to be reliable so that it doesn’t disrupt it’s user. Problems often happened in the website services when the infrastructure supporting it was having system failure, such as power shortage, natural disaster, hardware failure, and internet network problem etc. To increase website services’ system reliability and optimal service, infrastructure system that support server cluster schema is needed. The objective of infrastructure system that support server cluster schema is to increase website services’ system reliability and minimize downtime. Server cluster allow website services to look undisturbed on the user’s view even though there are some problems happening in the main server such as power shortage, because the website services is being serve by backup server using failover function. Infrastructure’s requirement for implementation of cluster server system at least needs to be double from the main system used, so that when the main system had hardware or software problems the backup system can serve website services just as good as the main system. It is hoped that website services will always available and the services is undisturbed even though there are problems in the infrastructure.

Pengamanan Backup dan Restore Basis Data dengan Penambahan Enkripsi Advanced Encryption Standard (Studi Kasus: Analisis Jabatan Bagian Organisasi Kabupaten Balangan)

Job analysis is needed to analyze and design what work to do, how to do it, and why the work should be done. Job analysis will provide information about job descriptions, job specifications, and job evaluations and can even predict job enrichment or expansion and job simplification in the future. This study aims to implement database security management procedures as a measure to prevent damage (error), loss and theft of data through backup and restore methods accompanied by encryption in the Job Analysis System application of the Balangan Regency Organization Division (Simanja). The backup process by adding Advanced Encryption Standard (AES) encryption which is stored to remote cloud hosting as a backup server using SSH Transfer Protocol (SFTP) provides adequate and efficient security.

Secure File Storage On Cloud Using Hybrid Cryptography

The servers that are accessed over the web and the software and databases that run on those servers are together known as "The Cloud". The amount of data needing storage is increasing every day and thus, there is a requirement for increased storage space. Cloud allows us to do the same. Storage of data on the cloud is done for various companies, colleges, for military purposes, etc. The data on the cloud is susceptible to various risks such as lack of backup services, data leakage, lack of control over your data being stored, etc. To provide a solution to these risks over cloud storage there are several ways, which include Cryptography and Steganography. Cryptography is quite popular for data security. The Cryptography technique translates original data into an unreadable form known as ciphertext. Text is converted into an unreadable form using keys. This ensures that only an authorized entity with the right key, can access the data from the cloud server. Ciphertext data is visible for everyone. There are two types of cryptography algorithms namely Symmetric Key cryptography and Asymmetric Key Cryptography. The use of a single cryptography algorithm provides basic security. In this paper, we have suggested the use of a combination of symmetric key cryptography algorithms namely: AES-GCM, Fernet, AES-CCM, CHACHA20_POLY1305 algorithm, which help to provide high-level security to the data. Here, the key is also secured using the Fernet algorithm. The file is split into N parts. Each part is encrypted simultaneously. For the file decryption purpose reverse process of encryption is applied.

Reliable Service Function Chain Deployment Method Based on Deep Reinforcement Learning.

Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.

Improving service quality in a congested network with random breakdowns

This paper employs some strategies for improving service quality in a congested network consisting of facilities and customers. The quality is measured considering both customers’ travel distances and congestion delays. The strategies include opening new facilities, increasing the service capacities, and incorporating multiple backup services for customers, empowering the network to distribute the facilities’ workload smoothly. The goal is to optimally configure the congested network while improving the service quality. Each facility is modeled as an M/M/1 queuing system and might be broken down frequently during its serving process. The recovery starts immediately, which is a multi-step process, and each step takes an exponentially-distributed random time. The problem is first modeled as a mixed-integer nonlinear program; then, reformulated as a mixed-integer second-order cone program and can be optimally solved. An efficient solution algorithm based on Lagrangian decomposition is also developed. The numerical experiments illustrate the high efficiency of the solution methodology. Several managerial implications, including determining the best backup service level, are provided. Last, an example in distributed computing system design is presented to demonstrate the applicability of the model.

Statewide Ambulance Coverage of a Mixed Region of Urban, Rural and Frontier under Travel Time Catchment Areas.

This study examines the statewide service coverage of emergency medical services (EMS) in view of public health planners, policy makers, and ambulance service managers. The study investigates the statewide service coverage in a mixed region of urban, rural, and frontier regions to address the importance of ambulance service coverage at a large scale. The study incorporated statewide road networks for ambulance travel time, census blocks for population, and backup service coverage using geographic information systems (GIS). The catchment areas were delineated by the travel time after subtracting chute time for each Census Block as an analysis zone. Using the catchment areas from the ambulance base to the centroid of Census Block, the population and land coverage were calculated. The service shortage and multiple coverage areas were identified by the catchment areas. The study found that both reducing chute time and increasing the speed of emergency vehicles at the same time was significantly more effective than improving only one of two factors. The study shows that the service is improved significantly in frontier and urban areas by increasing driving time and chute time. However, in rural areas, the improvement is marginal owing to wider distribution than urban areas and shorter threshold response time than frontier areas. The public health planners and EMS managers benefit from the study to identify underserved areas and redistribute limited public resources.

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.

Software Defined Network Function Virtualization Framework for Securing Cloud with Data Fusion and Machine Learning Techniques

Computing in the cloud is one of the platforms that may be used to provide distributed computing resources. Supplying and managing cloud resources most effectively is referred to as resource management. A recent development in technology known as fog computing is an example of an expanded and dispersed infrastructure. This architecture maintains application processes between end devices and the network edge to provide more dependable and efficient services. These services include remote data storage, allowing customers to access their data from a distant location. Providing remote storage service is an advantageous function offered by cloud suppliers. On the other hand, the data stored in the cloud is geographically dispersed and kept in various data centers, significantly increasing the risk to users' privacy and security. One of the problems that might arise with privacy is when many data centers store the same information. Many cloud service providers check their customers' data using a Third-Party Auditor (TPA) to address concerns about client privacy and data integrity. Currently, most trusted TPAs only have one validator, making it impossible to expand the data integrity across several data centers. The various verifiers used by TPAs have been reduced in number in response to Man in the Cloud (MiTC) attacks. As a result, they cannot check and authenticate the integrity of data stored in several data centers. A unique Peer to Peer (P2P) authentication protocol with Certificate Authority (CA) and Data Storage Protocol is presented as a solution to the problem that has been outlined above to check for and go around any issues that may arise (DSP). The efficiency of the proposed protocol is demonstrated by the incorporation of TPAs and Certificate Authorities. The proposed protocol has been tested with a single user and a single storage server, as well as multiple storage servers in ownCloud with one backup server, two storage servers, three clients, and two TPAs. The NoSQL server in an organization's cloud is set up to save data to storage servers in the appropriate format. The Amanda backup server is used to back up the mirror copy of the stored data on the storage servers. Automated Validation of Internet Security Protocols with Data Fusion and Applications, or AVISPA for short, is a technology that may be used to verify data stored in the cloud. The findings make it abundantly evident that the suggested protocol is strong enough to guarantee the authenticity of data kept in several data centers.

Shared backup resource assignment for middleboxes considering server protection capabilities

Middleboxes provide a wide range of functions and realize the network. If network functions fail and cannot be recovered, network services which utilize them cannot continue. In order to improve survival probabilities of functions, this paper presents six strategies which obtain an assignment of backup servers to network functions. Backup servers have capabilities of protecting functions and recovering some of them. In the previous work, the range of server capabilities, where an assignment can be obtained, is limited. By adopting the idea of temporarily dividing each server into a set of small servers, we expand the range of server capabilities in the first two strategies. We also consider the applicability of one of the first two strategies through deriving three theorems. Furthermore, in the last four strategies, we enable to obtain an assignment without any limitation of server capabilities by utilizing the knowledge of the first two strategies. To compare our strategies, we also provide a baseline strategy, which is a greedy algorithm and try to balance burden of each server. The numerical results show that in all the settings, more than one of our strategies provides the higher survival probability than the baseline strategy within an allowable computation time.

Unavailability-Aware Shared Virtual Backup Allocation for Middleboxes: A Queueing Approach

Network function virtualization provides an efficient and flexible way to implement network functions deployed in middleboxes as software running on commodity servers. However, it brings challenges for network management, one of which is how to manage the unavailability of middleboxes. This article proposes an unavailability-aware backup allocation model with the shared protection to minimize the maximum unavailability among functions. The shared protection allows multiple functions to share the backup resources, which leads to a complicated recovery mechanism and makes unavailability estimation difficult. We develop an analytical approach based on the queueing theory to compute the middlebox unavailability for a given backup allocation. The heterogeneous failure, repair, recovery, and waiting procedures of functions and backup servers, which lead to several different states for each function and for the whole system, are considered in the queueing approach. We analyze the performance bounds for a given solution and for the optimal objective value. Based on the developed analytical approach and the performance bounds, we introduce two heuristics to solve the backup allocation problem. The results reveal that, compared to a baseline model, the proposed unavailability-aware model reduces the maximum unavailability 16% in average in our examined scenarios.

Mixed fault tolerance in server assignment: Combining reinforcement and backup

We study the mixed approach to fault tolerance in the general context of server assignment in networks. The approach is based on mixing two different existing strategies, namely, reinforcement and backup. The former strategy protects clients by reinforcing the servers assigned to them and making them fault-resistant, possibly at a high cost, while the latter protects clients by assigning to them alternate low price backup servers that can replace their primary servers in case those fail. Applying the mixed approach to fault tolerance gives rise to new fault tolerant variations of known server assignment problems. We introduce several NP-hard problems of this type, including the mixed fault-tolerant dominating set problem, the mixed fault-tolerant centers problem, and the mixed fault-tolerant facility location problem, and present polynomial time approximation algorithms for them, demonstrating the viability of the mixed strategy for server assignment problems.

Re] Storage Tradeoffs in a Collaborative Backup Service for Mobile Devices

Re] Storage Tradeoffs in a Collaborative Backup Service for Mobile Devices

Value of an Emergency Care Back-Up Service for Residents and Fellows.

Value of an Emergency Care Back-Up Service for Residents and Fellows.

Multi-Authority Secure Database for Enabling Authorized Encrypted Search with Privacy Preserving on Healthcare Databases

Now a day’s in medical field number of application’s will develop for overcome the complexity of previous work. By using information technology and computer science provide various new techniques and medical equipment’s has improved digitalization in healthcare sector. In existing system much more advancement is providing to overcome the time and money of patients and perform exact treatments and store patient’s confidential records in securely but most important issues are security. To address the existing security issues to design and develop the proposed research work on security i.e. for patient’s confidential health data records in database servers. Existing work during data transmission can only protect the patient’s data records but they can’t stop the insider attacks. In proposed research work, first implement front end security with the help of keylogging technique, second to store patient’s confidential data in multiple data servers or chunks and to prevent the insider attacks and third and most important is access policy of search for encrypted data of multi-authority. The main contribution of this research work to assign patients data records in different chunks securely and applying the cryptosystems for security goals of a patient’s confidential records. Especially, proposed work advantages of SHA hashing technique to perform each and every user for access of particular data records. This research work explores secure data storage and sharing using proposed AES 128 encryption algorithm and Role Base Access Control (RBAC) for secure data access scheme for end user. This work also carried out backup server approach it works like proxy storage server for ad hoc data recovery for all distributed data servers.

Development and Operation Experiment of a Power High availability Server System by Compound Operation of a Power saving Server System and a Multiple server Backup System

Development and Operation Experiment of a Power High availability Server System by Compound Operation of a Power saving Server System and a Multiple server Backup System

Snapshot Based Disaster Recovery on Cloud

Nowadays, data has been produced in a larger amount. So, it is important to have some recovery issues also related to it. Cloud computing refers to the interconnection of various systems for the purpose of sharing resources. Cloud is one of the largest platforms that is growing rapidly in the IT sector. There are many private data that are related to cloud. Therefor the need for data recovery in the cloud is gaining more importance day by day. For this an efficient and effective data recovery techniques are to be developed. Recovery of data helps the users to collect information on any backup servers whenever the server is down. Many solutions have been developed for the disaster recovery. This research paper mainly discusses about some of the techniques and solutions that are needed to recover the data in cloud architectures. This work proposed snapshot based backup technique for the databases and implemented successfully.

Sistem Penunjang Keputusan Sistem Operasi Untuk Layanan E-Mail Server Dengan Pendekatan Analytical Heirarchy Proses (AHP)

In the world of IT technology is growing rapidly, with the rapid development of these specialized companies engaged in the procurement of bandwidth must always conduct searches about IT technology in the field of network services. And the flow of highly needed data or information exchange continues to increase, one of which is via e-mail. In small companies that do not have their own e-mail server or email server backup, they look for services to hosting, network or service companies engaged in data services. Therefore PT NAP Info Lintas Nusa wishes to improve services by adding new services, namely the Email server service. This service is intended for companies that do not have their own e-mail server or companies that want to back up their e-mail. In this case PT NAP Info Lintas Nusa does not yet have specific criteria to determine what operating system will be used that can support the running of the service. So the company decided to use the AHP method with expert choice to determine what criteria would be chosen so as to determine the alternative to be chosen.

V2B/V2G on Energy Cost and Battery Degradation under Different Driving Scenarios, Peak Shaving, and Frequency Regulations

The energy stored in electric vehicles (EVs) would be made available to commercial buildings to actively manage energy consumption and costs in the near future. These concepts known as vehicle-to-building (V2B) and vehicle-to-grid (V2G) technologies have the potential to provide storage capacity to benefit both EV and building owners respectively, by reducing some of the high cost of EVs, buildings’ energy cost, and providing reliable emergency backup services. In this study, we considered a vehicle-to-buildings/grid (V2B/V2G) system simultaneously for peak shaving and frequency regulation via a combined multi-objective optimization strategy which captures battery state of charge (SoC), EV battery degradation, EV driving scenarios, and operational constraints. Under these assumptions, we showed that the electricity usage/bill can be reduced by a difference of 0.1 on a scale of 0 to 1 (with 1 the normalized original electricity cost), and that EV batteries can also achieve superior economic benefits under controlled SoC limits (e.g., when kept between the SoC range of SoCmin > 30% and SoCmax < 90%) and subjected to very restricted charge-discharge battery cycling.