Short paper: Materializing Highly Available Grids

Server is a computer system that provides the kind of service in a computer network. One of the functions of the server is as data storage. Computers servers with high availability values are generally referred to as High Availability (HA) Server. HA enables a server can be operational in a sustainable manner for long periods of time. Now, HA has been combined with a cluster technique known as High Availability Clusters or failover cluster. In this research the author intends to apply, analyze, and implement a network file server that has a high availability of service. Researchers used a pacemaker as cluster resource management, Corosync as cluster messaging service and DRBD for the synchronization of data if a failover occurs and measured network performer cluster file system that does have a high availability service. The result is a network file system or the availability of the cluster has a value of 99.998% availability, which means it has a High Availability. The server network file system failover cluster meets the conditions, which, if the primary server (master) dies it will be replaced by a slave server.

LofoSwitch: An online policy for concerted server and disk power control in content distribution networks

Mobile Network Operator (MNO)s avail the benefit of providing an isolated service with the network slice in a variety of forms like enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). However, they face challenges in handling situations of overload, congestion, and scaling with the arrival of unexpected control plane User Service Requests (USRs) leading to the dropping of USRs, and hence disturbing the slice’s High Availability (HA). This paper presents a novel High Availability supportive self Reliant NEtwork Slicing System (HARNESS) for 5G Core, powered by intelligent and autonomous Self Organizing Network (SON) paradigm. In HARNESS, we propose algorithms to intelligently schedule and serve the significant portion of control plane USRs for both delay tolerant and delay sensitive slices, to ensure their uninterrupted HA service provision. Along with efficient resource utilization, the HARNESS shows improvements over the traditional HA provisioning methods, in preventing the occluding of crucial control plane USRs by 60% and reducing the average response time for these USRs by 50%. We developed the HARNESS framework in a 5G test-bed system using eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) mechanism for coherent and optimized end-to-end working of it. With this additional gear, HARNESS productively contributes to achieving 3.2% better slice service HA in the minimal two nodes active/active cluster configuration.

Though security measurement in cloud has become more mature and self-determining in last few years, rather than being solely dependent on vendors for trusting the cloud service, trust management is still a challenging issue for cloud researchers. Instead of having some amazing qualities like virtualization potential, high storage capacity, multi tenancy , high service availability - the only obstacle to adapt the cloud technology in a huge scale, is till now its security and trust issue. Trust being a qualitative factor can be established and determined independently and several algorithms are there to establish trust in a cloud system. This paper focuses on measuring the strength of cloud security by establishing trust, using some measurement index and with the help of some known parameters.

Cloud computing has already been adopted in a broad range of application domains and has become an established building block in IT landscapes. During the process of cloud middleware development, the companies have focused mainly on the high availability of data and end-user services, but unfortunately neglected the availability of middleware components. Therefore failures of the middleware components itself usually leads to a partial or even total blackout of the cloud. In this paper, we present the design and implementation of a novel scalable and highly available multi-master pattern for cloud middlewares. In contrast to existing Infrastructure-as-a-Service cloud management frameworks, which are usually designed in a centralized tree topology composed in a three-tiered master worker architecture, we introduce a concept for a multi tree with all tree roots connected in a fully connected mesh topology. In this architecture user requests are load balanced over multiple failover servers. Furthermore, our concept includes an automatic self-healing mechanism for worker nodes of each tree.

The goal of Linux Virtual Server (LVS) is to provide a basic framework to build highly available and scalable network services such as Radius authentication and accounting in our implementation. Scalability is achieved by transparently inserting or removing a node in the cluster, and high availability is supported by detecting node or daemon failures and reconfiguring the system appropriately. This article, rather than proposing a new cluster technique, illustrates the basic concept of LVS, an implementation of LVS-based highly-available Radius server, and the performance testing and analysis of the whole system by a testing software named testrad developed by us. Through the benchmarks, we can conclude that the performance of the system can linearly increase along with the increasing number of the real servers before the director gets fully-loaded. Thus, it can provide high availability and scalability for network services. (4 pages)

The grid and Web service are converging as the WSRF is proposed. The grid not only provides the computing and resources sharing pools, but also supports the electronic business and enterprise applications integration with the composition of grid service. The paper presents a novel semantic composition method and adaptive scheduling algorithm based on grid workflow, which can benefit from the workflow architecture in the composition, visualization, verification, scheduling, execution and monitoring. The method can address semantic issues and automate the composition of grid service, and the algorithm can improve scheduling efficiency when the composite grid service is executed in the grid environment. According to the simulation experiment on the prototype implemented on the GT3, the composition and scheduling have the advantage of efficiency and effectiveness.

At this cloud age, there is tremendous growth in business, services, resources, and cloud technology. This growth comes with a risk of unsafe, unordered, and uncertainty due to unauthorized access and theft of confidential propriety data. Our objective is to model around Read, Write and Execute to resolve these unordered, unsafe, and uncertain issues. We will develop a L-Shape pattern model matching UFS ACM to minimize the accessibilities based on RIGHT & ROLE of the resources and maximize the quality of services for safety and high availability. The preventive, detective, corrective (PDC) services are the major roles for all levels of management to coordinate, control the multiple technologies and resources which are working simultaneously. It will be more ordered, accountable, and actionable on real-time access control mechanism for scalabilities, reliability, performance, and high availability of computational services. We have to make safer, certain, unified, and step-by-step normalization by applying this UFS ACM mechanism on UNIX operating system. This proposed research paper covers a wide range of areas covering optimization, normalization, Fuzzy Low, and Risk assessment.

A very high service availability is important for the operator and for the user of road tunnels, too. The service availability of a structure is directly related to its quality. The earlier quality assurance measures are being considered during the life cycle, the better a structure of high quality can be guaranteed. Problems which occur during the operation period of a structure often result from design errors or from inadequate realisation during the construction phase. They may also occur as a result of wrongly planned maintenance and refurbishment works. Thus, the transfer of specific data, information and experiences through the whole life cycle is very important. In this context methods of facility management can provide efficient assistance when they have already been used throughout all three classic phases of a structure‘s life cycle - planning, construction and operation. Finally the tunnel drainage system of German road tunnels is considered as an example as practical application possibility.

Today's mobile users expect high-quality experience which involves both high quality services as well as high service availability. It may take only a few bad service experiences such as dropped calls, unavailable navigation service, or delayed emails, to cause a mobile customer to consider switching service providers. Although great progress has been made in the radio communication and operations optimization as well as in the customer services areas, there are some hard technical problems yet to be solved to offer a personalized quality of service assurance across both the basic phone service and advanced applications. Our position as a major telecommunication software provider has given us great insight into these issues and in this paper we present the theoretical and architectural details of two interrelated approaches that could provide feasible means for improved quality of mobile user experience through intelligent device and network-resident software components. For the end user it will seem as though a guardian angel is on her shoulder describing, predicting and explaining disruptive events.

ContextMany people wanted to avoid or delay childbearing during the COVID‐19 pandemic. This study sought to examine the extent COVID‐19 influenced abortion care‐seeking in a region that did not enact policy restricting abortion due to the pandemic, has high service availability, and few abortion‐restrictive policies.MethodologyWe conducted telephone surveys with adults (n=72) requesting abortion appointments between September 2020 and March 2021 at five clinics in Washington, DC, Maryland, and Virginia. We used χ 2 tests to compare sociodemographic, reproductive history, service delivery characteristics, and pandemic‐related life changes by whether COVID‐19 influenced abortion care‐seeking.ResultsMost respondents (93%) had an abortion at the time of the survey, 4% were awaiting their scheduled appointment, and 3% did not have an appointment scheduled. Nearly 40% of people reported COVID‐19 influenced their decision to have an abortion. These individuals were significantly more likely to report “not financially prepared” (44% vs. 16%) as a reason for termination compared to people reporting no influence of COVID‐19. They were also more likely to have lost or changed their health insurance due to pandemic‐related employment changes (15% vs. 2%), report substantial money difficulties due to COVID‐19 (59% vs. 33%), and report that paying for their abortion was “very difficult” (25% vs. 2%).ConclusionCOVID‐19 influenced many people to have an abortion, particularly those financially disadvantaged by the pandemic. Expansion of Medicaid abortion coverage in Washington, DC and Virginia could reduce financial barriers to care and help people to better meet their reproductive needs amid future crises.

In distributed systems, high service availability can be achieved by letting a group of servers replicate the service state; if some servers fail, the surviving ones know the service state and can continue to provide the service. Group communication services, such as membership and atomic broadcast, have been proposed to solve the problem of maintaining server state replica consistency. Group membership achieves agreement on the history of server groups that provide the service over time, while atomic broadcast achieves agreement on the history of state updates performed in each group.

Fault diagnosis in telecommunication networks requires extensive expert knowledge and is key to efficient network operations and high service availability. Specifically, discovering and identifying new faults occurring in the network is a challenging task. Some dominant methods in industry are based on expert systems or Bayesian networks. Both of these methods require considerable expert knowledge and time resources to construct and maintain the diagnosis system. In this paper, we propose a data driven approach for the clustering and identification of new faults, based on existing knowledge, using neural networks and infinite mixture models. In our approach deep infinite mixture models are capable of extracting interesting features from labeled data, which are then leveraged in the clustering process to identify new relevant faults in unlabeled data. We apply our method to real operational data from Fiber-to-the Home services based on Gigabit-capable Passive Optical Networks. We show that our approach can be trained end-to-end, and allows to identify and interpret new faults.

Owing to the efficient resource management, accessibility, and high service availability, cloud computing has been leveraged by several intensive-data processing applications such as big data analytics, social media applications. These applications are typically based on the development of web service and web application. Even though web-based technology offers effective communication and implementation, it has been susceptible to various kinds of attack. In this paper, we investigate possible attacks on REST which is a commonly used protocol for the web service implementation. In REST, HTTP requests are mapped to GET, POST, PUT, and DELETE that have been proven to be prone to common attacks including Automated Brute Forcing on web-based login, HTTP flood attacks, SQL injections (SQLi), and Cross-Site Scripting (XSS). To this end, we propose a design and implementation of the cloud-based IDS to detect such attacks by employing Apache Kafka and Spark streaming to classify and process the high volume of user inputs in REST HTTP communication. To detect the anomalous inputs, we apply the signature-based approach to construct an IDS engine based on a set of known attack patterns that will be leveraged by the Spark Streaming. Specifically, we introduce a new string comparison collection that improves the False Positive (FP) rate in SQL injection detection, which has been a major issue in most proposed IDS currently available. In our experiment, the system is able to determine malicious patterns with high performance as well as to generate SMS alerts and log the event in a Google Cloud Storage Bucket in an efficient manner.

Satellite communications are expected to collaborate with 5G techniques in the future. In this paper, a detector based on the minimum mean square error (MMSE) criterion is designed at the receiver of a LEO satellite system to cancel co-channel interference (CCI) in a predetermined order successively, which is called ordered successive interference cancellation (OSIC). In order to make the detector have stronger ability to mitigate interference, low density parity check (LDPC) coding and soft demodulation schemes are applied with the OSIC jointly to improve our proposed MMSE-OSIC detector's performance. In the simulation analysis, we present the bit error rate (BER) performance of our proposed detector in an AWGN channel. Numerical results show that compared to the results of the conventional detection in existing literatures, the MMSE-OSIC detector can reduce the BERs for fixed signal-to-noise ratio (SNR) values. It means our proposed detector can be employed at the receiver with high service availability and reliability guaranteed.