Improve Application Performance Research Articles

Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud Environments

With the increasing popularity of microservice architecture, there is a growing need to deploy service-based applications efficiently in cloud environments. Traditional cluster schedulers often fail to optimize service placement adequately, as they only consider resource constraints and overlook traffic demands between services. This oversight can lead to performance issues such as high response times and jitter. To address this challenge, we propose a novel approach to optimize the placement of service-based applications in clouds. Our approach involves partitioning the application into segments while minimizing overall traffic between them, and then strategically allocating these segments to machines based on their resource and traffic demands. We have developed a prototype scheduler and conducted extensive experiments on test bed clusters to evaluate its performance. The results demonstrate that our approach surpasses existing container cluster schedulers and heuristic methods, significantly reducing overall inter-machine traffic and improving application performance

THEORETICAL UNDERPINNINGS AND PRACTICAL IMPLICATIONS OF SD-WAN TECHNOLOGIES IN TELECOMMUNICATIONS

The concept paper explores the transformative impact of Software-Defined Wide Area Network (SD-WAN) technologies on the telecommunications industry. SD-WAN has emerged as a game-changer, offering a more flexible, agile, and cost-effective alternative to traditional Wide Area Network (WAN) solutions. The paper begins by providing a theoretical foundation for SD-WAN, discussing its key principles, architectural components, and underlying technologies. It explores how SD-WAN leverages software-defined networking (SDN) principles to dynamically manage and optimize network traffic across geographically dispersed locations, improving performance and reducing costs. The practical implications of SD-WAN in the telecommunications industry are then examined. The paper discusses how SD-WAN enables organizations to enhance network performance, reduce latency, and improve application performance across their networks. It also explores how SD-WAN can simplify network management, enhance security, and support the growing demand for cloud-based applications and services. Furthermore, the paper discusses the challenges and considerations associated with implementing SD-WAN, such as ensuring compatibility with existing network infrastructure, managing security risks, and ensuring compliance with regulatory requirements. It also explores the potential impact of SD-WAN on telecommunications service providers, highlighting opportunities for new revenue streams and business models. Overall, this concept paper provides a comprehensive overview of the theoretical underpinnings and practical implications of SD-WAN technologies in telecommunications. It aims to inform telecommunications professionals, researchers, and policymakers about the transformative potential of SD-WAN and its implications for the future of telecommunications. Keywords: SD-WAN, Technology, Telecom, Security.

Unveiling the Synergistic Relationship between Distributed Systems and Cloud Computing: A Review of Architectural Trends

Cloud providers use distributed systems for scalability, availability, performance, automation, multi-tenancy, and innovation. Distributed cloud computing distributes workload across multiple locations, improving application performance and responsiveness. Significantly potential computational resources are developed in cloud, where large-scale, intricate tasks are performed with the backbone of distribute infrastructure in cloud systems, similar to supercomputing. Cloud computing development has significantly impacted software development and testing, necessitating applications compatible with the cloud, large data users, and high security. Distributed applications hoist on to cloud platforms where increased efficiency, reliability and low costs are favored and further be stored in the cloud for flexibility and scalability. Cloud service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), each offering different application services, programming languages, and hosting environments. The synergistic aspects of Distributed Systems and Cloud Systems with respect to their basic capabilities are discussed and systematically reviewed.

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

Implementation of Cloud Run and Cloud Storage as REST API Service on OutfitHub Application

The development of Cloud Computing technology has progressed rapidly in recent years especially with the emergence of Google Cloud Services (GCR) which has become one of the leading cloud service providers. This research focuses on the OutfitHub application, which plays a role in assisting users in determining clothing styles using a personalized recommendation system. In developing this application, the research seeks to implement cloud computing services to improve application performance. The purpose of this research is to implement Cloud Computing, especially Cloud run and Cloud Storage services as Rest API in the Outfithub application. By implementing these two services, it is expected that there is no need to pay attention to the problem of Storage needs that are growing at any time and no need to worry about the need for server configuration because both of these things will be fully done by GCR. Implementing Cloud Computing will provide a variety of benefits in addition to those previously mentioned, such as: being able to access data from anywhere and at any time. This implementation is expected to be able to run OutfitHub applications in a Cloud environment in a serverless computing manner without requiring the design of unnecessary virtual machines.

Implementation of Service Oriented Architecture in Mobile Applications to Improve System Flexibility, Interoperability, and Scalability

This research aims to apply Service Oriented Architecture (SOA) to mobile applications to improve the flexibility, interoperability, and scalability of the system. SOA is an approach that enables better integration between different components in an application architecture. Mobile applications, as an integral part of everyday life, require high flexibility and adaptability to changing user needs. The research method used involves application requirements analysis, SOA architecture design, and implementation on an existing mobile application. Through the implementation of SOA, resource management and service distribution become more efficient, making it easy to add or change features without affecting the entire system. Test results show improved application performance and responsiveness, along with the ability to easily integrate services from multiple sources. In addition, the SOA architecture provides flexibility in the development and maintenance of mobile applications, accelerating the development process and minimizing the impact of changes. This research contributes to the development of mobile applications by introducing the SOA approach as an architectural foundation. The implementation of SOA in mobile applications is expected to guide developers in creating systems that are more resilient, adaptive, and easy to integrate with rapidly evolving software environments.

Hybrid Elastic Scaling Strategy for Container Cloud based on Load Prediction and Reinforcement Learning

To harness the advantages of both proactive and responsive scaling, adapting to various workload scenarios, this paper introduces a container hybrid scaling strategy called HyPredRL, rooted in load prediction and reinforcement learning. Within the proactive scaling module RL-PM, a load prediction model, MSC-LSTM, predict workloads and, in conjunction with current workload states, leverages reinforcement learning agents for intelligent scaling decisions. The responsive scaling strategy, SLA-HPA, enhances Kubernetes’ native scaling strategy, which primarily considers resource utilization, by incorporating response time metrics. Ultimately, a hybrid scaling controller is designed, applying the principles of “rapid scaling out” and “balanced conflicts” to coordinate proactive and responsive scaling. Experimental results demonstrate that HyPredRL outperforms existing methods in SLA violation rate, resource utilization, and request response time, effectively improving application performance and scalability.

Effect of chemical treatment on physio-mechanical properties of lignocellulose natural fiber extracted from the bark of careya arborea tree

For the first time, the current work has carried out a chemical treatment of a novel ligno-cellulose fiber that is extracted from the bark of an unexplored plant of Careya arborea. Careya arborea (CA), a flowering tree known for its green berries, thrives in the Indian subcontinent and Afghanistan. This research was focused on extracting fibers from the bark of the Cary tree for the first time to corroborate the influence of chemical treatment on its different characteristics. These CA fibers have a high proportion of cellulose, consisting of 71.17 wt percent, together with 27.86 wt percent of hemicellulose, and a reduced density of 1140 kg/m3, making them a suitable candidate for creating lightweight applications in a variety of industries. Chemical treatment has done on the cay fiber with the concentrations of NaOH 5 (wt%), 10 (wt%), and 15 (wt%) solution mixture to improve their characteristics. Estimated the difference between Chemically processed and non-processed Cary fibers and corroborated in results. We performed a number of experiments, including FTIR, XRD, SEM, EDAX, AFM, and TGA, to fully comprehend the changing properties. Chemical testing showed that cellulose changed from its non-crystalline state to cellulose, proving that the treatment was successful in changing the fibre structure. Additionally, the thermo-gravimetric examination showed higher thermal stability 248 °C–325 °C and a rise in the crystallinity index, indicating the treated fibers’ improved potential for high-temperature applications. The treated Cary fibers exhibited excellent surface properties, promising improved adhesion, mechanical performance, offering lightweight and sustainable solutions for diverse applications.

Exploiting Flat Namespace to Improve File System Metadata Performance on Ultra-Fast, Byte-Addressable NVMs

The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and superior sequential performance provided by non-volatile memories (NVMs). This article proposes FlatFS+, an NVM file system that features a flat namespace architecture while providing a compatible hierarchical namespace view. FlatFS+ incorporates three novel techniques: the direct file path walk model, range-optimized B r tree, and compressed index key design with scan and write dual optimization, to fully exploit flat namespace to improve file system metadata performance on ultra-fast, byte-addressable NVMs. Evaluation results demonstrate that FlatFS+ achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems.

Analisa Kinerja Individu Berdasarkan Kepuasan Penggunaan E-Government di Indonesia (Studi Kasus DIKUPASI Diskopukmperindag Kota Mojokerto)

This study aims to determine the perceived usefulness of users of the DIKUPASI application at Diskopukmperindag in Mojokerto City by using the Usability model approach with 4 variables, namely Learnability, Flexibility, Effectiveness, and Attitude, and to analyze this usefulness on application user satisfaction and performance improvement. DIKUPASI is an institutional application that functions to monitor and update data on UKM in Mojokerto City. The research was conducted on surveyors in the City of Mojokerto using a sampling technique of 42 surveyors spread across the City of Mojokerto. The collection of research data was carried out by distributing questionnaires. Data processing is done with the SmartPLS application. The data processing that has been done shows that there are 2 hypotheses H4 and H5 obtained from the Usability model which have a significant and positive influence. Based on the results of hypothesis testing, the perception of satisfaction with the DIKUPASI application is positively and significantly influenced by user attitudes. Perceived performance improvement for DIKUPASI application users is significantly influenced positively by user satisfaction. Participation in research on the Department of Cooperatives, Small and Medium Enterprises, Industry and Trade in Mojokerto City which adopted the DIKUPASI application is necessary to pay attention to the perceptions of user attitudes so that users feel satisfied in using the application and can improve user performance. The perception of satisfaction can be created through encouragement of surveyors' willingness and how the DIKUPASI application has benefits that can help surveyors' work.

A Resource-Efficient Feature Extraction Framework for Image Processing in IoT Devices

Extracting features from image data on Internet of Things (IoT) devices to reduce the amount of data that needs to be uploaded to cloud/edge servers has received increasing attention. However, most of the existing related approaches suffer from two major limitations, (i) low performance and high network traffic, and (ii) a lot of storage resource consumption. To this end, we propose a resource-efficient feature extraction framework for image processing in IoT devices. The proposed framework consists of the edge-assisted extractor generation method and the NestE method. The extractor generated by the edge-assisted extractor generation method can extract the features required by the application, which can not only avoid the IoT device uploading useless feature data but also improve application performance. The proposed NestE generates a nonredundant subextractor by splitting the extractor into multiple subextractors, removing redundant subextractors, and nesting small-capacity subextractors in large-capacity subextractors in a parameter-sharing manner. Compared with deploying multiple independent subextractors on IoT devices, deploying the nonredundant multifunctional extractor can save considerable storage resources and switching overhead. Extensive experimental results show that the proposed framework reduces the storage footprint by approximately 90.7% and switching overhead by approximately 92.4% compared with deploying independent subextractors when using the classical principal component analysis algorithm.

Factors Influencing Indonesians' Intentions to Use the Tokopedia Online Marketplace

Manuscript type: Research paper Objective: Many factors influence people’s intention to use online marketplaces, especially Tokopedia in Indonesia. The purpose of this study is to explain these influencing factors of usage intention by simulating the impact of innovativeness, mobility, perceived ease of use, and perceived usefulness. Research Design and Methods: A quantitative approach was used through an online survey and resulted in 250 responses which were analysed with a structural equation model to test construct reliability, average convergent validity, and structural model fit. Findings: According to the findings, mobility, innovativeness, perceived utility, and perceived simplicity of use are characteristics that influence user intention. The perception of utility has the biggest influence on the intention to use something, whereas perceived ease of use has the least. Implications and Recommendations: These findings have significance for online marketplaces, such as Tokopedia in Indonesia, in terms of developing and improving application performance based on user demands.

Improvement of AlGaN/GaN High-Electron-Mobility Transistor Radio Frequency Performance Using Ohmic Etching Patterns for Ka-Band Applications.

In this paper, AlGaN/GaN high-electron-mobility transistors (HEMTs) with ohmic etching patterns (OEPs) "fabricated to improve device radio frequency (RF) performance for Ka-band applications" are reported. The fabricated AlGaN/GaN HEMTs with OEP structures were used to reduce the source and drain resistances (Rs and Rd) for RF performance improvements. Within the proposed study using 1 μm hole, 3 μm hole, 1 μm line, and 3 μm line OEP HEMTs with 2 × 25 μm gate widths, the small signal performance, large signal performance, and minimum noise figure (NFmin) with optimized values were measured for 1 μm line OEP HEMTs. The cut-off frequency (fT) and maximum oscillation frequency (fmax) value of the 1 μm line OEP device exhibited optimized values of 36.4 GHz and 158.29 GHz, respectively. The load-pull results show that the 1 μm line OEP HEMTs exhibited an optimized maximum output power density (Pout, max) of 1.94 W/mm at 28 GHz. The 1 μm line OEP HEMTs also exhibited an optimized NFmin of 1.75 dB at 28 GHz. The increase in the contact area between the ohmic metal and the AlGaN barrier layer was used to reduce the contact resistance of the OEP HEMTs, and the results show that the 1 μm line OEP HEMT could be fabricated, producing the best improvement in RF performance for Ka-band applications.

A unified hybrid memory system for scalable deep learning and big data applications

Emerging non-volatile memory (NVM) technologies are of dynamic random access memory (DRAM)-like, high capacity, and low cost, at the expense of slower bandwidth and higher read/write latency compared to DRAM. Typically, NVM finds its primary application in serving as an extension of conventional DRAM to create hybrid memory systems tailored to non-uniform memory access (NUMA) architectures. This strategic integration offers the potential for high performance, enhanced capacity efficiency, and a favorable balance of cost considerations. Traditional NUMA memory management policies distribute data uniformly across both DRAM and NVM, overlooking the inherent performance gap between these heterogeneous memory systems. This challenge becomes particularly pronounced when provisioning resources for deep learning and big data applications in hybrid memory systems. To tackle the performance issues in the hybrid memory systems, we propose and develop a unified memory system, UniRedl, which automatically optimizes data migration between DRAM and NVM based on data access patterns and computation graphs of applications. To improve application performance, we provide a new memory allocation strategy named HiLowAlloc. We further design two data migration strategies in UniRedl, Idle Migration and Dynamic Migration, for management of hybrid memory systems. Specifically, Idle Migration aims to manage data placed in DRAM, while Dynamic Migration manages data saved in NVM. The experimental results demonstrate that on average UniRedl improves application performance by 33.2%, 20.6%, 19.0%, and 17.5% compared to the traditional NUMA, NUMA with anb, BMPM, and OIM, respectively. It also achieves 52.0%, 34.3%, 30.6%, 22.1% on average improvement in data locality against the state-of-the-art solutions.

Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.

A Compilation Tool for Computation Offloading in ReRAM-based CIM Architectures

Computing-in-Memory (CIM) architectures using Non-volatile Memories (NVMs) have emerged as a promising way to address the “memory wall” problem in traditional Von Neumann architectures. CIM accelerators can perform arithmetic or Boolean logic operations in NVMs by fully exploiting their high parallelism for bit-wise operations. These accelerators are often used in cooperation with general-purpose processors to speed up a wide variety of artificial neural network applications. In such a heterogeneous computing architecture, the legacy software should be redesigned and re-engineered to utilize new CIM accelerators. In this article, we propose a compilation tool to automatically migrate legacy programs to such heterogeneous architectures based on the low-level virtual machine (LLVM) compiler infrastructure. To accelerate some computations such as vector-matrix multiplication in CIM accelerators, we identify several typical computing patterns from LLVM intermediate representations , which are oblivious to high-level programming paradigms. Our compilation tool can modify accelerable LLVM IRs to offload them to CIM accelerators automatically, without re-engineering legacy software. Experimental results show that our compilation tool can translate many legacy programs to CIM-supported binary executables effectively, and improve application performance and energy efficiency by up to 51× and 309×, respectively, compared with general-purpose x86 processors.

Optimization of VR Application Mahakarya Vokasi Land Using Level of Detail (LOD) Technique

The development of the VR exhibition application for mahakarya vokasi Land using Level of Detail (LOD) is the focus of this research. LOD allows for adjusting the detail of 3D objects based on the user's viewing distance, reducing computational load, and improving application performance. LOD Groups are used to categorize objects based on complexity levels and dynamically adjust the level of detail. The research results indicate that the use of LOD successfully increased FPS by 284% and reduced memory usage in several important parameters such as setPass Calls, draw calls, batches, triangles, vertices, used textures, render textures, and shadow casters. Although some parameters did not show a significant improvement in FPS, it is important to monitor and optimize them. The use of LOD can be an effective strategy to improve the performance of VR exhibition applications by reducing rendering and memory load, creating a smoother and more responsive VR experience

Facile physical modification of ultrathin polyethylene by trace poly(vinyl alcohol)

Poly (vinyl alcohol) (PVA) modification to nonpolar polymers has been intensively investigated due to the performance improvement in membrane separation, tissue engineering, and other applications. Slightly deviated from the conventional route, no covalent bonding by crosslinking or hydrogen bonding is needed to anchor PVA onto the target matrix. This work harnessed van der Waals interaction only to achieve the modification. Owing to the high surface energy and aspect ratio of ultra-high molecular weight polyethylene (UHMWPE) ultrathin membranes, the physical binding between PVA and PE was found stable and easy to establish. This interaction is initiated by the entropy-driven hydrophobic effect at the liquid-solid interface to reduce surface energy. By encapsulation of PVA on the fibrils, the PE hydrophilicity could be overturned (from 134.6° to 48.4°) with micro-structures remaining almost intact at the low loading of modification (0.001% conc. solution dip-coating). The resultant hydrophilic ultrathin PVA-PE membranes showed high transparency, high mechanical strength (380 MPa in tensile max. stress), and high porosity with only 200 nm thickness in a freestanding state. This work provides a facile approach to switch the hydrophilicity of nonpolar polymer ultrathin membranes by establishing vdW physical bonding only. By trace-adsorption, this method could also be helpful in fine-tuning nanostructures of the target polymer matrix by a resolution of nanometer level.

New insights into selective flotation recovery of gold using dye-derived thermo-responsive polymeric surfactant: DFT calculation and adsorption mechanism

The selective flotation recovery of gold from secondary resources is a sustainable strategy to fill the ever-increasing demand for gold resources, and reduce the environmental pollution. In the present work, a novel method to realize the selective and fast recovery of Au(III) in the flotation process involving the use of thermo-responsive amphiphilic polymer poly(ethylene glycol)-b-poly(N-isopropylacrylamide-co-glycidyl methacrylate) (PEG113-b-P(NIPAM108-co-GMA62)) integrated with rhodamine B thiohydrazide (RBS) affinity ligand was studied. The polymer possessed the selective capture property toward Au(III) salt and excellent adsorption performance on bubble surfaces, driven by specific coordination interactions between Au(III) salt and RBS ligand, and hydrophobic interactions between GMA moieties and bubbles. Flotation using the thermo-responsive polymeric surfactant displayed a high separation efficiency and fast flotation kinetics, achieving a separation efficiency of 85% for Au(III) salt at 28 min, which was comparable with sulfur-functionalized mesoporous materials. This flotation process followed pseudo-second-order kinetics and Freundlich isotherm adsorption model. Furthermore, in flotation recovery of Au(III) salt form actual e-waste leaching solution, separation efficiency of 81% was achieved, while Au grade was improved from 1.55 % to 34.76%. This polymeric surfactant was easily regenerated by the reduction-induced release the bound Au(III) salt and thermal precipitation, leading to retain 81% of separation efficiency after five cycles. This work provides an effective way to fabricate flotation surfactant, which can meet the urgent demand for performance improvement and chemical consumption reduction in practical applications.

An adaptive auto-scaling framework for cloud resource provisioning

Cloud computing emerged as a technology that offers scalable access to computing resources in conjunction with low maintenance costs. In this domain, cloud users utilize virtualized resources to benefit from on-demand and long-term pricing strategies. Although the latter consists of a more cost-efficient solution, it requires accurate estimations of future workload demands, which is a challenging task. Furthermore, clouds offer threshold-based auto-scaling rules that need to be manually controlled by the users according to application requirements. Still, tuning scaling parameters is not trivial, since it is mainly based on static scaling rules that may lead to unreasonable costs and quality of service violations. In this work we introduce ADA-RP, an adaptive auto-scaling framework for reliable resource provisioning in the cloud. ADA-RP uses historical time series data for training K-means and convolutional neural networks (CNN) to categorize future workload demands as High, Medium or Low based on CPU utilization. We auto-scale cloud resources in real-time based on the predicted workload demand to reduce costs and improve application performance. The experimental analysis is based on TPC-C runs on MySQL containers deployed on the Google Cloud Platform. Experimental results are prosperous, demonstrating the ability of ADA-RP (i) to reduce MySQL deployment costs by 48% in a single-tenant environment, and (ii) to double the executed queries per second in a multi-tenant environment considering user’s budget requirements.