Application Owners Research Articles

Constrained optimal grouping of cloud application components

Cloud applications are built from a set of components often deployed as containers, which can be deployed individually on separate Virtual Machines (VMs) or grouped on a smaller set of VMs. Additionally, the application owner may have inhibition constraints regarding the co-location of components. Finding the best way to deploy an application means finding the best groups of components and the best VMs, and it is not trivial because of the complexity coming from the number of possible options. The problem can be mapped onto may known combinatorial problems as binpacking and knapsack formulations. However, these approaches often assume homogeneus resources and fail to incorporate the inhibition constraints. The main contribution of this paper are firstly a novel formulation of the grouping problem as constrained Coalition Structure Generation (CSG) problem, including the specification of the value function which fulfills the criteria of a Characteristic Function Game (CFG). The CSG problem aims to determine stable and disjoint groups of players collaborating to optimize the joint outcome of the game, and a CFG is a common representation of a CSG, where each group is assigned a value and where the value of the game is the sum of the groups’ contributions. Secondly, the Integer-Partition (IP) CSG algorithm has been modified and extended to handle constraints. The proposed approach is evaluated with the extended IP algorithm, and a novel exhaustive search algorithm establishing the optimum grouping for comparison. The evaluation shows that our approach with the modified algorithm evaluates on average significantly less combinations than the CSG state-of-the-art algorithm. The proposed approach is promising for optimized constrained Cloud application management as the modified IP algorithm can optimally solve constrained grouping problems of attainable sizes.

Fluidity: Providing flexible deployment and adaptation policy experimentation for serverless and distributed applications spanning cloud–edge–mobile environments

We introduce Fluidity, a framework enabling the flexible and adaptive deployment of serverless and modular applications in systems comprising cloud, edge, and mobile nodes. Based on a declarative description of application requirements, a custom placement policy, and a formal system infrastructure description, Fluidity plans and executes an initial deployment of application components in the cloud–edge–mobile continuum. Furthermore, at runtime, Fluidity monitors resource availability and the position of mobile nodes, and adapts the deployment of the application accordingly, without any manual intervention from the application owner or system administrator. These characteristics render Fluidity an enabler for serverless applications, allowing the application developers to focus on the application code itself while abstracting out the infrastructure management. Notably, Fluidity permits developers to provide their own deployment and adaptation policies as well as to switch between different policies while the application is running. We discuss the design and implementation of Fluidity in detail and provide a realistic evaluation using a lab testbed in which the mobile node is represented as a simulated drone. In addition, we evaluate the scalability of the proposed mechanisms. Our results show that the core mechanisms of Fluidity can support flexible application execution at a reasonable overhead and experimentation with different deployment policies with minimal effort.

ANALISA KEPUASAN PENGGUNA WEBSITE TOP UP VOUCHER GAMES ONLINE MENGGUNAKAN WEBQUAL 4.0

The online gaming industry generates revenue through the sale of games that customers pay to play. In Indonesia, there are numerous programs that offer online game vouchers at different costs and with various payment options. The objective of this study is to identify the variables that influence user satisfaction on web applications that offer online game coupons. The study used the WebQual 4.0 methodology as a tool for evaluating the caliber of web apps. The research was conducted online via the Google Form application. The data collection period spanned from June 11, 2023 to June 29, 2023, and we got 130 respondents. The data was evaluated using descriptive statistical analysis with the aid of SPSS tools. The findings of this study indicate that the variable of Interaction Quality (X3) has a significant role in determining user satisfaction for online game voucher websites, however the factors of Usability (X1) and Information Quality (X2) do not exert any influence on user satisfaction. Therefore, this research shows that hypotheses H1 and H2 are rejected for all data groups. This research provides recommendations to online game voucher application owners to focus on interaction quality variables in further web application development.

DoWNet—classification of Denial-of-Wallet attacks on serverless application traffic

Abstract Serverless computing is an ever-growing programming paradigm being adopted by developers all over the world. Its highly scalable, automatic load balancing, and pay for what you use design is a powerful tool that can also greatly reduce operational costs. However, these advantages also leave serverless computing open to a unique threat, Denial-of-Wallet (DoW). It is the intentional targeting of serverless function endpoints with request traffic in order to artificially raise the usage bills for the application owner. A subset of these attacks are leeches. They perform DoW at a rate that could go undetected as it is not a sudden violent influx of requests. We devise a means of detecting such attacks by utilizing a novel approach of representing request traffic as heat maps and training an image classification algorithm to distinguish between normal and malicious traffic behaviour. Our classifier utilizes convolutional neural networks and achieves 97.98% accuracy. We then design a system for the implementation of this model that would allow application owners to monitor their traffic in real time for suspicious behaviour.

Waffle: An Online Oblivious Datastore for Protecting Data Access Patterns

We present Waffle, a datastore that protects an application's data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner's preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake-a state-of-the-art oblivious datastore under passive persistent adversaries-by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.

DyCause: Crowdsourcing to Diagnose Microservice Kernel Failure

Today many web applications in the cloud (apps) are built based on microservices. However, as the anomaly propagates in a highly dynamic and complex way, troubleshooting them becomes full of challenges. Existing diagnostic methods are mostly designed based on monitoring metrics retrieved from the microservice system kernel. Therefore, application owners and even site reliability engineers (SREs) cannot effectively resort to those methods when the microservice systems lack such a comprehensive monitoring infrastructure. In this paper, we develop DyCause, a crowdsourcing solution to the asymmetric diagnostic information problem. Our solution collects the operational status of kernel services collaboratively from the user space and initiates diagnosis on demand. Without the requirement of any architectural or functional infrastructure, it is both fast and lightweight to deploy DyCause in a microservice system. In order to discover the fine-grained dynamic causalities between services during the anomaly, we also design an efficient algorithm based on statistical analysis. Based on this algorithm, we can also analyze the anomaly propagation paths within the microservice system and generate a better interpretable diagnosis. In our evaluation, we test DyCause in a controlled simulation environment and a real-world cloud system. Our results have shown that DyCause has the best accuracy and efficiency among several state-of-the-art methods and is more robust in terms of parameters.

Model Sistem Informasi Pemesanan dan Produksi Berbasis Web Menggunakan Metode Agile

This research discusses the ordering and production application model developed with the php programming language. The application model is developed with an agile method approach that suits the circumstances that suit the needs of application development quickly but still meets the needs of application owners and users. Data collection was carried out using observation methods, interviews, and literature studies. Data collection is carried out to find out the problems that occur, while observation activities on the ongoing process and PIECES-based analysis aim to confirm the problems to be solved through application development. Application development is carried out through the stages of analysis, design, development, and testing. The design stages are carried out using the Unified Modeling Language (UML) approach. The development stage is done using the php programming language with the codeigniter 3 framework and the mysql database with visual studio code as the text editor. In the final part, testing activities are carried out with a black box testing approach. The test results show that the developed application is functioning properly and in accordance with the development goals. The application of ordering and production applications produced in this study plays a positive role in improving business process performance, and increasing the satisfaction of application users and enterprise customers.

Logistic Regression Method for Sentiment Analysis Application on Google Playstore

Now days, there are a lot of social media. Each of social media has their one purpose. Each of application has their own function. We can download social media application from Playstore like Google Play or Appstore. Each Playstore gives users the opportunity to provide rating and review. Ratings and reviews from users can give conclusions about the application. Applications with a rating of 5 usually have good comments so that they are more worthy to be downloaded and used. Ratings with a small value cause other users not to use the application. Application owners need to know the results of comments from users which sometimes do not match the value given in the rating. So that the owner or application developer conducts an analysis of user comments on various social media, one of which is on the Playstore. Social media analysis is one way to get public opinion on a topic. One of them is used to analyze various applications provided in the Playstore. Various methods are used to perform social media analysis. This study uses the logistic regression method to analyze public comments into positive or negative comments. We analyze comments from social media applications on Google Play for android users. We get an accuracy value of 81% from 4 social media applications, with a total of 2268 comment data.

Legal Liability of Influencers or Affiliates for Stock Pumping Activities and Promoting Trading in The Form of Binary Options

Trading Platform with Binary Obsi or choosing one of the two is a platform that is considered by OJK to be more similar to numbers, where this refers to Law number 10 of 2011 concerning amendments to Law 32 of 1997 in this law there is an article that can ensure that the practice of Binary Options is not in accordance with what is regulated by the law, but binary options trading can circulate rapidly because the owners of the binary options platform use the services of influencers to spread the platform. which results in trading in the form of Binary Options, the legality of which cannot be ascertained in Indonesia. This raises new legal problems in the form of activities recommending platforms that do not have legality in Indonesia and are prohibited by the OJK, this is binding on those who carry out recommendation activities or are often referred to as stock pompoms. Stock pompom activities are rampantly carried out by public figures who are known to the public who do not pay attention to the legality of Binary Options trading, as for several parties who collaborate with application owners and take advantage of the failures of Binary Option players. The approach method used is a normative legal research method, namely legal research conducted by examining library materials or secondary data. The approach used is the invitation approach, the approach and the conceptual approach. This journal finds several issues that need the government's attention, such as the need for special laws governing binary options applications in the gambling or fraud category, special rules regarding influencers who get commissions called affiliates, as well as strict rules for influencers to make recommendations, also educating the public so that they are not easily influenced by what activities are found on social media

DoWTS – Denial-of-Wallet Test Simulator: Synthetic data generation for preemptive defence

The intentional targeting of components in a cloud based application, in order to artificially inflate usage bills, is an issue application owners have faced for many years. This has occurred under many guises, such as: Economic Denial of Sustainability (EDoS), Click Fraud and even secondary effects of Denial of Service (DoS) attacks. With the advent of commercial offerings of serverless computing circa 2015, a variant of the EDoS attack has emerged, termed, Denial-of-Wallet (DoW). We describe our development of a simulation tool as safe means to research these attacks as well as to generate datasets for the training of future mitigation systems to combat DoW. We believe that DoW may become increasingly prevalent as applications further utilise services based on a pay-per-invocation cost model. Given that the damage caused is purely financial, such attacks may not be disclosed as application users are not directly effected. As such, we believe that the development of an attack simulator and specific testing of security measures against this niche attack will be able to provide previously unavailable data and insights for the research community. We have developed a prototype DoW simulator that can emulate multiple months worth of API calls in a matter of hours for ease of training data generation. Our aspiration for the future of this work is to provide a system and starting point for research on this form of attack. We present our work on such a system Denial-of-Wallet Test Simulator (DoWTS) - a system that allows for safe testing of theorised DoW attacks against serverless applications via synthetic data generation. We also expand upon prior research on DoW and provide an analysis on the lack of specific safety measures for DoW.

BUYING BEHAVIOR IN ONLINE FOOD DELIVERY APPLICATIONS DURING THE COVID-19 PANDEMIC

This study aims to review the effect of discount framing, brand reputation, purchase intention, and actual behavior based on online food delivery applications during the COVID-19 pandemic. The population, in this study are users of online food delivery applications throughout Indonesia, with the unit of analysis being the individual. Sampling in this study used the purposive method, namely respondents who filled out online questionnaires with total of 119. The data analysis technique in this study used SEM-AMOS version 23. The results showed that discount framing had a positive effect on brand reputation and purchase intention, brand reputation has a positive effect on purchase intention, and purchase intention has a positive effect on actual behavior. The implications of the results of this study for application owners are to know the effect of discount framing, brand reputation, and purchase intention so that they can take strategies and policies related to these matters. For application, users are to be able to use applications that provide discount framing according to their needs.

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

Deployment Archetypes for Cloud Applications

This is a survey article that explores six Cloud-based deployment archetypes for Cloud applications and the tradeoffs between them to achieve high availability, low end-user latency, and acceptable costs. These are (1) Zonal, (2) Regional, (3) Multi-regional, (4) Global, (5) Hybrid, and (6) Multi-cloud deployment archetypes. The goal is to classify cloud applications into a set of deployment archetypes and deployment models that tradeoff their needs around availability, latency, and geographical constraints with a focus on serving applications. This enables application owners to better examine the tradeoffs of each deployment model and what is needed for achieving the availability and latency goals for their application.

Penerapan Sistem Pakar Untuk Diagnosa Penyakit Kucing Pada Aplikasi Berbasis Android Dengan Metode Forward Cahining

Technological advances can be used in various sectors of life, one of the benefits of this technology is the use of android in expert systems. Expert systems can replace the role of an expert where users can get information or solutions to a problem without having to meet directly with an expert. One of the several uses of expert systems is to diagnose disease in animals early. Cats are cute and adorable animals that are commonly kept by many people. However, cats are often affected by diseases caused by viruses, bacteria or parasites. It is very important for cat owners to find out the disease suffered by the cat early. The use of the forward chaining method applied to an android-based cat disease expert system can be used by cat keepers to detect disease in cats early , and with this application owners can save time and energy costs because they get cat disease information faster without having to meet directly with experts

Monitoring-Aware Optimal Deployment for Applications Based on Microservices

Modern cloud applications are required to be distributed, scalable, and reliable. The microservice architectural style enables developers to achieve this goal with reduced effort. Nonetheless, microservices deployment is not trivial due to the heterogeneity of the microservices in terms of both functional and non-functional requirements. This is also true when considering the monitoring requirements that are specific to each microservice and must be satisfied in order to enable the verification of the application objectives satisfaction. However, not all providers offer the same set of metrics with the same quality. The goal of this paper is to provide an approach for supporting the deployment of microservices in multi-cloud environments focusing on the Quality of Monitoring. Adopting a multi-objective mixed integer linear optimisation problem, our approach supports the application owner in finding the optimal deployment for satisfying all the constraints and maximising the quality of monitored data, while minimising the costs. To this end, a knowledge base is introduced to mediate between the perspectives of the cloud provider and the application owner, while a Bayesian Network is adopted to enhance the provider’s monitoring capabilities by estimating metrics requested by the application owners that the cloud provider is not able to monitor.

Ensuring the QoE-Related Fairness to Reduce the User Abandonment Ratio.

Nowadays, it is quite a challenge for app owners to keep users engaged with an app. Currently, the level of user abandonment is one of the key parameters that application owners are interested in. To meet these challenges, we conduct an extended study of a previously proposed solution that significantly reduces the abandonment rate of a given application. The investigated solution is based on the methods of fairness using the QoE and QoS approach. This paper shows that application abandonment ratios can be reduced by using an appropriate approach to fair bandwidth allocation. Adjusting the bandwidth allocation to users, taking into account the quality of the user experience, has a more effective effect on reducing app abandonment ratios than if quality of service is taken into account. This is because the users make the decision to abandon the application based on their feelings rather than technical parameters. In order to effectively reduce application abandonment ratios, a suitable bandwidth allocation algorithm must be used. This paper presents the impact of using different algorithms on the abandonment ratio and compares the popularly used algorithms and the previously proposed bandwidth allocation algorithm.

What is DOM XSS and why should you care?

For more than 10 years, cross-site scripting (XSS) has been included in OWASP's ‘Top 10 Web Application Security Risks’. 1 Although the issue is very well documented, it is sometimes overlooked by application owners. As a result, penetration testers frequently encounter this vulnerability, which takes several forms. For more than 10 years, cross-site scripting (XSS) has been included in OWASP's top web application security risks. Yet it's an issue that is often overlooked. The problem has also evolved and many people still do not understand the risks surrounding Document Object Model (DOM) or client-side XSS. Through understanding the vector itself, threat surfaces of applications will become ever-more restricted and safer for the end user. A thorough understanding of DOM-based XSS will help balance the scales of application security and usability, says Josh Hickling of Pentest People.

Local Differential Privacy-Based Federated Learning for Internet of Things

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

Cloud applications monitoring: An industrial study

Abstract Context Modern software systems employ large IT infrastructures hosted in on-premise clouds or using “rented” cloud resources from specific vendors. The unifying force across any cloud strategy is incremental product and application improvement against conservation of those resources. This is where monitoring of cloud applications becomes a key asset Objective To shed light over the status of monitoring practices in industry, we study: (a) monitoring practices and tools adoption in industry; (b) size and complexity of industrial monitoring problems; (c) the role of software architecture and software process with respect to monitoring strategies. Method We conduct mixed-methods empirical research featuring interviews and a web survey featuring 140+ practitioners from over 70 different organizations. Results Even if the market makes available a significant set of monitoring tools, our results show a rather unappealing picture of industrial monitoring: (a) industrial decision-makers do not perceive monitoring as a key asset even though the downtime of their applications correlates heavily with the level of automation and responsiveness enabled by monitoring; (b) monitoring is done with crude technology, mostly MySQL querying or similar (e.g., Nagios); finally, (c) incidents are discovered by clients rather than application owners. Conclusion We conclude that the road toward the industrial adoption of cutting-edge monitoring technology is still one of the less travelled, presumably in connection to the considerable investment required. Furthermore, the lack of industrial cloud monitoring standards does not help in addressing the proliferation of multiple tool combinations, with varying effectiveness. Further research should be invested in looking into and addressing these major concerns.

Online Marketplace Characteristics for Freelance Designers

Marketplace is one of the choice places for designers to market the products they create. Online Marketplace is a networking site or application on a gadget that provides online trading facilities from various sources. In the marketplace, application owners or website networks only provide facilities for users to display products sold. In addition, the marketplace owner also provides facilities in the form of bridging online transactions between sellers (designers) and buyers. In utilizing the marketplace, designers must know the needs and desires and design market trends. Because with accurate data related to needs, trend design will greatly help designers to create design products that are suitable for the market. Not only the idealism of designers in making works, but also still relies on dynamically changing demands.
 This study intends to determine the design trends that are in accordance with the desires and needs of the graphic design market so that they can help designers determine the making of works sold in the marketplace. This study uses a qualitative research approach to explore all phenomena that occur in the world of online design marketplace. Through this qualitative approach, researchers will understand social reality, see what conditions exist in the sale and purchase of design products and design trends that are popular in society. This qualitative approach is sought to be directed at identifying the trend design and style design that designers need for the basis of making designs that can be sold in the marketplace.
 
 Keywords: Online Marketplace, Design Marketplace, Freelance Designer