Implementation of Fuzzy Inference System with Best-Worst Method for Cost Efficiency on Amazon Web Services

Summary Public cloud providers, such as the Microsoft Azure and the Amazon Web Services (AWS), offer a wide variety of virtual machines, with different specifications, and prices, that are enabling users to run high-performance programs without buying specialized and expensive hardware. Moreover, some providers, such as AWS, allow the user to bid for lower cost virtual machines, called Spot Instances. Nonetheless, these machines may be terminated within a few minutes of warning at the provider discretion. In this work, we leveraged the SPITS programming model to implement a high-performance and fault-tolerant seismic processing application that is proper for execution on Spot instances and analyzed how different virtual machines from the AWS may affect the performance and the price of the computation. Our experimental results indicate that Spot instances have similar performance to regular instances but are roughly three times less expensive. Finally, we show that AWS groups virtual machines in Availability Zones and that selecting virtual machines from different zones may also affect the total execution cost.

The cloud computing is a computing paradigm that users can rent computing resources from service providers as much as they require. A spot instance in cloud computing helps a user to utilize resources with less expensive cost, even if it is unreliable. When a user performs tasks with unreliable spot instances, failures inevitably lead to the delay of task completion time and cause a seriously deterioration in the QoS of users. Therefore, we propose a price history based checkpointing scheme to avoid the delay of task completion time. The proposed checkpointing scheme reduces the number of checkpoint trials and improves the performance of task execution. The simulation results show that our scheme outperforms the existing checkpointing schemes in terms of the reduction of both the number of checkpoint trials and total costs per spot instance for user’s bid.KeywordsCloud computingCheckpointingSpot instancesPrice history

Cloud providers are auctioning their excess capacity using dynamically priced virtual instances. These spot instances provide significant savings compared to on-demand or fixed price instances. The users willing to use these resources are asked to provide a maximum bid price per hour, and the cloud provider runs the instances as long as the market price is below the user's bid price. By using such resources, the users are exposed explicitly to failures, and need to adapt their applications to provide some level of fault tolerance. In this paper, we expose the effect of bidding in the case of virtual HPC clusters composed of spot instances. We describe the interesting effect of uniform versus non-uniform bidding in terms of both the failure rate and the failure model. We propose an initial attempt to deal with the problem of predicting the runtime of a parallel application under various bidding strategies and various system parameters. We describe the relationship between bidding strategies and programming models, and we build a preliminary optimization model that uses real price traces from Amazon Web Services as inputs, as well as instrumented values related to the processing and network capacities of cluster instances on the EC2 services. Our results show preliminary insights into the relationship between non-uniform bidding and application scaling strategies.

Historical operations at the U.S. Department of Energy’s Hanford Site included disposal of waste fluids to the subsurface in the 200 West Area on the Hanford Central Plateau. Subsequent infiltration of fluids has resulted in groundwater contamination with carbon tetrachloride, nitrate, uranium, technetium-99, and other contaminants. A pump-and-treat (P&T) system, with an extraction/injection well network and an aboveground treatment plant, was implemented as part of interim and final remedies in the 200 West area. The HYPATIA single-page web application (part of the SOCRATES suite) is being developed to provide access to and analysis of chemistry and treatment facility sensor data for this 200 West P&T system. For the web application, analytical algorithms were developed to perform summing, differencing, smoothing, outlier detection, change-point detection, mass flow rate, and injectivity calculations on the data. Candidate algorithms were identified and tested, with the best-performing algorithms then assembled for implementation in HYPATIA. Because HYPATIA is hosted on the Amazon Web Services (AWS) cloud computing platform, algorithms were implemented in a back-end AWS Lambda function that can be called by the HYPATIA front end. The Lambda function applies the requested data processing to specified data via functions written in R, Python, and JavaScript. Development, testing, and review of the data analysis algorithms was completed under an NQA-1 quality program. This new HYPATIA functionality will provide information to support site decisions regarding P&T system performance and optimization.

Cloud computing has brought a paradigm shift in computing world. Cloud Service Providers (CSP) use several pricing models for the services they offer. These models can be of short-term or long-term requirements for Cloud Service Users (CSU). Primary objective of our research is to minimize the total cost by finding optimal resource requirements to satisfy CSUs' demand. CSPs like Amazon EC2, a major Infrastructure as a Service (IaaS) provider uses pricing scheme like reserved, on-demand and spot instances for its Virtual Machine (VM) distribution. In this paper, we proposed a QoS aware heuristic approach to minimize job completion time and cost spot instances. In the proposed model, a heuristic approach is used for provisioning spot instances and an artificial neural network (ANN) is employed to predict the pricing spot instance, price which in turn enhances the performance and validate the quality of service. Efficacy of the proposed model is tested using Amazon EC2's real price traces and the total cost of CSUs' are compared with different approaches.

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

As China is a major automobile manufacturing and consumption country, cost reduction is an important indicator for the operation of the automobile industry. There are many automobile-related components and parts, and each component is important to the full functionality of the automobile. This article focuses on automobiles’ heat exchanger components (HECs) because they have important connections with the engine, transmission, body, and hydraulic systems of automobiles. Since the HECs are important functional parts of automobiles, it is worthwhile to investigate the determinants affecting the cost reduction of HECs in the automobile industry. Hence, the main purpose of this article is to evaluate the determinants affecting the cost reduction of HECs in the Chinese automotive industry using the best-worst method (BWM). Firstly, based on the literature and the opinions of industry and expert interviews, five evaluation dimensions and 20 evaluation factors were developed. Secondly, an empirical survey was conducted using the BWM and expert questionnaires. Finally, the study’s results revealed that: (1) “Research and development” is the most critical evaluation dimension that affects the cost reduction of HECs in the Chinese automotive industry; (2) The determinants affecting the cost reduction of HECs in the Chinese automotive industry were “value analysis/value engineering,”“production system optimization,”“developing industry standards,”“lightweighting,”“synchronous new product development,” and “waste reduction.” The empirical findings showed that the first three determinants were particularly important in the decision-making factors of cost reduction. In addition, this article discusses and suggests these determinants as a reference for future cost reduction evaluation of HECs in the Chinese automotive industry.

Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence of which Spot prices change continuously. As such, prospective bidders can benefit from intelligent insights into the Spot market dynamics that can help them make more informed bidding decisions. To enable this, we propose a descriptive statistics approach for the analysis of Amazon EC2 Spot markets to detect typical pricing patterns including the presence of seasonal components, extremes and trends. We use three statistical measures -- the Gini coefficient, the Theil index, and the exponential weighted moving average. We also devise a model for estimating minimum bids such that the Spot instances will run for specified durations with a probability greater than a set value based on different look back periods. Experimental results show that our estimation yields on average a bidding strategy that can reliably secure an instance at least 80% of the time at minimum target guarantee between 50% and 95%.

The collected information related to the activities along the life cycle of an automation solution serves as basis for the comparative evaluation between a classically developed system and a mechatronicly developed decentralized system. The following results have been worked out with the characteristics from decentralized automation use cases as introduced in Section 3 in combination with the described mechatronic engineering approaches. The general advantages of engineering with mechatronic modules, such as, for instance, cross-discipline- work and the improved reuse or standardization of partial solutions, were already discussed in other papers and shall not be described here any further (Thramboulidis 2008, Löwen & Wagner 2009, VDI 2206). Application Effort and Complexity: The applicability and manageability of a modular, decentralized automation approach ­ particularly regarding the modularization of automation and control functionalities ­ must still be ensured. For this purpose, one must differentiate between the creation of the mechatronically modules in a predevelopment project and their application in the particular engineering projects. Overall, it can be expected that the specification and implementation effort to be performed in advance and the complexity for the creation of mechatronic modules with integrated automation and control functions, which are autonomous and can be integrated independently of each other, will increase substantially. For this purpose, bases for modules have to be created that are usable for different projects and configurable for the specific applications. This requires detailed knowledge of the domain, extensive technological expertise, and an understanding of the core functionality of the modules. One example for a starting point for this approach is the INTEGRA standard specifying first consistent mechatronic viewpoints for automotive production lines. These pre-produced modules facilitate the engineering for project-specific applications. The more plug&play modules are available the less effort is required for the engineering. In addition, project risks can be reduced by early validation of fulfillment of customer requirements due to availability of module descriptions and models. This, however, only applies if module adaptations occur which can be done by configuration, selection of variants, or extensions, and in which do not require any changes to the predefined core

The management of security credentials (e.g., passwords, secret keys) for\ncomputational science workflows is a burden for scientists and information\nsecurity officers. Problems with credentials (e.g., expiration, privilege\nmismatch) cause workflows to fail to fetch needed input data or store valuable\nscientific results, distracting scientists from their research by requiring\nthem to diagnose the problems, re-run their computations, and wait longer for\ntheir results. In this paper, we introduce SciTokens, open source software to\nhelp scientists manage their security credentials more reliably and securely.\nWe describe the SciTokens system architecture, design, and implementation\naddressing use cases from the Laser Interferometer Gravitational-Wave\nObservatory (LIGO) Scientific Collaboration and the Large Synoptic Survey\nTelescope (LSST) projects. We also present our integration with widely-used\nsoftware that supports distributed scientific computing, including HTCondor,\nCVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for\ncapability-based secure access to remote scientific data. The access tokens\nconvey the specific authorizations needed by the workflows, rather than\ngeneral-purpose authentication impersonation credentials, to address the risks\nof scientific workflows running on distributed infrastructure including NSF\nresources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds\n(e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the\ninteroperability and security of scientific workflows, SciTokens 1) enables use\nof distributed computing for scientific domains that require greater data\nprotection and 2) enables use of more widely distributed computing resources by\nreducing the risk of credential abuse on remote systems.\n

We sought to compare length-of-stay (LOS), total hospital costs, and readmissions among pulmonary embolism (PE) patients treated with rivaroxaban versus parenterally bridged warfarin. We identified adult PE (primary diagnostic code=415.1x) patients in the Premier Database (11/2012-9/2015), and included those with≥1 PE diagnostic test on days 0-2. Rivaroxaban users (allowing≤2days of prior parenteral therapy) were 1:1 propensity score matched to patients parenterally bridged to warfarin. LOS, total costs, and readmission for venous thromboembolism (VTE) or major bleeding within the same or subsequent 2months were compared between cohorts. Separate analyses were performed in low-risk PE patients. Rivaroxaban use was associated with a 1.4-day [95% confidence interval (CI) -1.47 to -1.28] shorter LOS, and $2322 (95% CI -$2499 to -$2146) reduction in costs compared to parenterally bridged warfarin (p<0.001 for both). There was no difference in readmission for VTE (1.5versus 1.7%) or major bleeding (0.3 versus 0.2%) between the rivaroxaban and parenterally bridged warfarin cohorts (p≥0.27 for both). Results were similar in low-risk patients (0.2-1.0day and $251-$1751 reductions in LOS and costs, respectively, p≤0.01 for all). In patients with PE, rivaroxaban was associated with reduced LOS and costs, without increased risk of readmission versus parenterally bridged warfarin. Similar results were observed in low-risk PE patients.

Automation and control systems include many “intelligent” automation devices, which are usually implemented as complex embedded systems. New methods and tools are demanded to cope with the increasing design complexity, while keeping the project on schedule. Proper handling of nonfunctional system requirements is a key factor during the design of industrial automation systems, since in some application domains they are as important as (sometimes, more important than) functional requirements. This paper presents a model-driven engineering approach, which combines Unified Modeling Language (UML) and aspect-oriented software development (AOSD) to design real-time and embedded automation systems. The proposed approach allows a smooth transition from the initial phases to implementation by using software tools, comprising the system specification and the automatic generation of source code. By combining UML with model-level aspects and a script-base code generation tool, it enables the use of AOSD during system design and implementation, even though the target platform does not natively support such concepts. Experimental results on using this approach to design real-world examples of automation systems are presented. The results indicate a positive impact on the design of automation systems. The encapsulation of nonfunctional requirements was improved, increasing the reuse of developed artifacts. Generated source code statistics indicate that the proposed approach can generated a fair amount of code per model element.

"Cloud" computing providers, such as the Amazon Web Services (AWS), offer stable and scalable computational resources based on hardware virtualization, with short, usually hourly, billing periods. The idea of pay-as-you-use seems appealing for biometry research units which have only limited access to university or corporate data center resources or grids. This case study compares the costs of an existing heterogeneous on-site hardware pool in a Medical Biometry and Statistics department to a comparable AWS offer. The "total cost of ownership", including all direct costs, is determined for the on-site hardware, and hourly prices are derived, based on actual system utilization during the year 2011. Indirect costs, which are difficult to quantify are not included in this comparison, but nevertheless some rough guidance from our experience is given. To indicate the scale of costs for a methodological research project, a simulation study of a permutation-based statistical approach is performed using AWS and on-site hardware. In the presented case, with a system utilization of 25-30 percent and 3-5-year amortization, on-site hardware can result in smaller costs, compared to hourly rental in the cloud dependent on the instance chosen. Renting cloud instances with sufficient main memory is a deciding factor in this comparison. Costs for on-site hardware may vary, depending on the specific infrastructure at a research unit, but have only moderate impact on the overall comparison and subsequent decision for obtaining affordable scientific computing resources. Overall utilization has a much stronger impact as it determines the actual computing hours needed per year. Taking this into ac count, cloud computing might still be a viable option for projects with limited maturity, or as a supplement for short peaks in demand.

This research aims to find out 1) The Implementation of the SLiMS automation management system in the Makassar State University FMIPA library, 2) Quality improvement with the SLiMS automation system in the Makassar State University FMIPA library and 3) advantages and disadvantages with the SLIMS automation system in the Makassar State University FMIPA library. This research uses descriptive qualitative research while the research approach used is field research. The data sources are from the head of the library and library staff as well as visitors to the FMIPA UNM Library. The data collection methods used in this research are observation, interviews and documentation. The research instrument in this research is the researcher himself. Then the data processing and analysis techniques are carried out in three stages, namely data reduction, data presentation, and drawing conclusions. The results of this research indicate that the implementation of the automation management system in the Makassar State University FMIPA Library uses the Open Source Senayan Library Management System (SLiMS) automation system. The SLiMS automation system is supported by hardware and software so that the system can run. The hardware used is generally the same, a collection of computers connected to a network, the difference is the software used in the library automation system. The Library quality by using the SLiMS automation system is way better. Users find it easier to use the system, its services, and to obtain information on the books they are seeking. In addition, the automation system of the Open Source Senayan Library Management System (SLIMS) has the benefit of facilitating readers' search for information about wanted books as well as librarians' and staff's ability to finish tasks in the library. The downside is that users are unable to access information remotely because the program is still offline.

Evaluation and selection of sustainable hydrogen production technology with hybrid uncertain sustainability indicators based on rough-fuzzy BWM-DEA