Improve Image Decoding in Lightweight Environment Using a Coroutines Based Approach

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

For more than 40 years, the relational database management system (RDBMS) and the atomicity, consistency, isolation, durability (ACID) transaction guarantees provided through its use have been the standard for data storage. The advent of Big Data created a need for new storage approaches that led to NoSQL technologies, which rely on basic availability, soft-state, eventual consistency (BASE) transactions. Over the last decade, NewSQL RDBMS technology has emerged, providing the benefits of RDBMS ACID transaction guarantees and the performance and scalability of NoSQL databases. The reliance on virtualization in IT has continued to grow, but an investigation of current academic literature identified a void regarding the performance impact of virtualization of NewSQL databases. To help address the lack of research in this area, a quantitative experimental study was designed and carried out to answer the central research question, "What is the performance impact of Type-I virtualization on a NewSQL RDBMS?" VMware ESXi virtualization software, NuoDB RDBMS, and OLTP-Bench software were used to execute a mixed-load benchmark. Performance metrics were collected comparing bare metal and virtualized environments, and the data analyzed statistically to evaluate five hypotheses related to CPU utilization, memory utilization, disk and network input-output (I/O) rates, and database transactions per second. Findings indicated a negative performance impact on CPU and memory utilization, as well as network I/O rates. Performance improvements were noted in disk I/O rates and database transactions-per-second.

The increasing complexity of cloud computing environments necessitates robust, automated monitoring systems to ensure high availability and operational efficiency. Traditional manual anomaly detection methods are no longer sufficient due to their limited scalability, high error rates, and delayed response times. This research proposes a machine learning-based anomaly detection system designed to proactively monitor cloud operations by analyzing real-time streams of logs, system metrics, and performance indicators. The system ingests diverse data sources including timestamped logs, CPU and memory utilization, network traffic, response times, and error rates, each tagged with unique resource identifiers. It leverages both labeled and unlabelled datasets for comprehensive model training and evaluation. A hybrid approach is adopted using supervised algorithms—Support Vector Machine (SVM), Random Forest, Deep Neural Network (DNN), and Extreme Gradient Boosting (XGBoost). SVM achieves 97% accuracy on labeled historical data, while DNN reaches 99.2% by modeling complex nonlinear patterns, and XGBoost achieves 98.8% by optimizing performance through gradient boosting on decision trees. Notably, Random Forest attains 100% accuracy across both labeled and unlabelled scenarios, demonstrating exceptional generalization through ensemble learning. Detected anomalies are classified by severity and type, with each assigned a confidence score to guide timely responses—either automated or manual. This intelligent framework substantially enhances anomaly detection accuracy, minimizes false positives, and supports resilient cloud operations. Its modular, scalable architecture ensures compatibility with existing cloud infrastructures. Future enhancements will focus on real-time adaptability, cross-cloud deployment, and integration with predictive analytics and self-healing protocols to enable autonomous cloud management.

In order to overcome the problems of long response time, high CPU and memory usage, this paper proposes a new interactive platform based on open API. On the basis of open API protocol, the whole framework of B/S structure design platform is introduced. The platform hardware mainly includes three main components: microprocessor, memory, programmer, etc. through feedback elite teaching optimisation algorithm and open API protocol program, students are graded to complete the design of open API College English mobile teaching interactive practice platform. The experimental results show that the maximum response time is reduced to 0.85, and the CPU utilisation and memory utilisation are reduced to 56.4 and 64.2 when the number of users is the largest, so the evaluation of platform learning investment effect is better.

Performance efficiency is important in mobile application development because mobile devices have limitations in terms of power and resources. Performance efficiency can be improved by applying architecture patterns. In this paper, we use the Model View ViewModel (MVVM) architecture. The application of the architecture is carried out to analyze how practical the application of the MVVM architecture pattern is in increasing performance efficiency in the mobile application. Performance efficiency is measured based on CPU usage, memory usage, and execution Time. The case study shows that the CPU usage and execution Time on MVVM are smaller than Base architecture pattern from the AR Ruler. This is due to the third-party library RxSwift in the MVVM architecture that increases the application's response so that CPU usage and execution time is better than Base architecture pattern. However, the existence of the third-party library RxSwift has a negative impact on memory usage, resulting in higher memory usage than the Base Architecture Pattern. The MVVM pattern is highly recommended for mobile application development to improve performance efficiency.

In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design.

Computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

Big data systems handle vast volumes of structured and unstructured information using distributed computing frameworks like Apache Hadoop, Spark, and Flink. These platforms rely heavily on computational resources, and CPU utilization becomes a critical factor in determining the performance and efficiency of data processing operations. Effective CPU usage directly impacts throughput, latency, and the ability to meet service-level objectives. In large-scale clusters, workloads vary from CPU-intensive tasks such as real-time analytics, data mining, and machine learning to more I/O-driven operations like data ingestion or storage management. Poor CPU distribution can lead to overloaded nodes while others remain underutilized, ultimately reducing system efficiency. Traditional scheduling mechanisms in big data platforms are often rule-based and may not respond well to dynamic or unpredictable workloads, which contributes to suboptimal CPU usage. When CPU utilization remains consistently high, it can trigger job slowdowns, task failures, and increased power consumption, negatively affecting overall performance. Conversely, low CPU utilization indicates underused resources, leading to waste in computational capacity and energy. Additionally, container technologies and control groups (cgroups) support fine-tuned CPU allocation and isolation, ensuring fair usage across concurrent users or tasks in multi-tenant environments. In more advanced implementations, machine learning algorithms are used to forecast CPU needs and schedule jobs more efficiently by learning from historical patterns and usage behavior. Reinforcement learning approaches have also shown potential in achieving balanced CPU usage by adapting policies based on feedback and environment changes. These intelligent mechanisms allow big data systems to optimize CPU allocation continuously, adapting to fluctuating demand without manual intervention. Efficient CPU management contributes significantly to faster job execution, reduced operational costs, and higher cluster reliability. As data volumes and complexity continue to grow, maintaining optimal CPU utilization remains a priority for organizations aiming to derive timely insights while maximizing infrastructure value. Basic CPU utilization approaches are exhibiting performance limitations. This paper addresses performance limitations related to CPU utilization.

The Internet of Things (IoT) is one of the most important emerging technologies, spanning a myriad of possible applications, especially with the increasing number and variety of connected devices. Several network simulation tools have been developed with widely varying focuses and used in many research fields. Thus, it is critical to simulate the work of such systems and applications before actual deployment. This paper explores the landscape of available IoT and wireless sensor networks (WSNs) simulators and compares their performance using the Low Power Wide Area Network (LPWAN) communication technology called LoRa (Long Range), which has recently gained a lot of interest. Using a systematic approach, we present a chronological survey of available IoT and WSNs simulation tools. With this, we categorized and content-analyzed published scientific papers in the IoT and WSNs simulation tools research domain by highlighting the simulation tools, study type, scope of study and performance measures of the studies. Next, we present an overview of LoRa/LoRaWAN technology by considering its architecture, transmission parameters, device classes and available simulation tools. Furthermore, we discussed three popular open-source simulation tools/frameworks, namely, NS-3, OMNeT++ (FLoRa) and LoRaSim, for the simulation of LoRa/LoRaWAN networks. Finally, we evaluate their performance in terms of Packet Delivery Ratio (PDR), CPU utilization, memory usage, execution time and the number of collisions.

The Internet of Things (IoT) is revolutionizing healthcare by integrating smart devices and sensors into medical practice. These devices can monitor patient health remotely, collect real-time data, and enable healthcare providers to deliver more personalized and timely care. From wearable fitness trackers that monitor vital signs to smart pills that track medication adherence, IoT in healthcare enhances patient outcomes, improves healthcare delivery efficiency, and reduces costs. An electrocardiogram (ECG) is an essential diagnostic tool for determining heart -related problems. In this research, we have developed a real -time ECG data acquisition method using small devices like Raspberry Pi Pico (RPiPico) and AD8232 ECG sensors. Our study analyses the ECG signal transmission on small embedded devices and a standalone machine so that when such critical data is transmitted on cloud resources via edge devices, the delay taken to transmit the data impacts the significant decisions of embedding the algorithms in resource-constrained devices. We analyzed the delays while the data was transmitted through the MQTT broker. We have also compared CPU and memory usage running on small and standalone devices. We observe that when the broker runs on the Rpi, it gives less delay, CPU utilization, and memory usage compared to the laptop as a broker. There is no packet loss in both cases. Thus, transferring critical data via a small processor like RPi with an MQTT broker is faster than transferring it through a general laptop processor with an MQTT broker.

This study aims to: develop a Scramsis game that contains basic programming material for class X TKJ SMK Muhammadiyah 1 Wates and test the quality of the Scramsis game based on the ISO 25010 quality standard. This research is a research and development research using the Waterfall model which includes communication, planning, modeling, construction, and distribution. The functional suitability quality test was carried out by 2 developers, while the usability test was carried out by students of class X TKJ SMK Muhammadiyah 1 Wates. The results of this study are: (1) Game Scramsis has six main menus, namely let's play, how to play, settings, profiles, instructions, and exit. Scramsis has 3 different levels of difficulty.. Game Scramsis got a score of 100% on the functional suitability quality test which means very high quality, 100% on the compatibility quality test which means very high quality. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. The use of scramsis memory is 129 MB and does not experience force close. The usability test was carried out on class X TKJ. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. The use of scramsis memory is 129 MB and does not experience force close. The usability test was carried out on class X TKJ SMK Muhammadiyah 1 Wates and got a score of 80.02% which was included in the quality category and the Cronbach alpha reliability value from the usability test questionnaire was 0.828 which means good.

With an exploding use of Mobiles, smartphones and IoT device, mobile edge computing (MEC) emerged as technological boon in computing paradigm. The device offloads the computationally intensive task as well as task relevant to storage to the MEC cloud server to meet the requirement of service delay, extend the battery lifespan of mobile, and resolve the problem of limited mobile device resources. With this reference we propose novel framework architecture with security to perform offloading of high storage and computationally intensive task to MEC server with minimum energy consumption and delay. For dynamic resource allocation, we employ two scheduling policy one at mobile side i.e. SJFP and other at MEC server side i.e. eSFFDRR. Before to offload task, first AES encryption technique is employed to secure input parameter, and then compressed this encrypted data to secure task data and utilize more bandwidth. Our experimental result depicts that for high storage and computationally intensive task our proposed framework can save 85–87% processing time, 70% of memory utilization with minimum energy consumption. The experimental result also proved that our proposed work improves the performance of computationally intensive mobile application with reduced consumption of mobile device resources like computation time, memory utilization, CPU usage and energy consumption.

It is a vision to integrate real-world data seamlessly into workflows and business applications using ubiquitous computing technology. A large number of deployed low-cost devices enhanced with sensors is observing the physical world. That way, real-world data is provided to business and workflow applications fully automated and in real-time without suffering from media-breaks, that means no manual and only few automated transformations of data formats are allowed. Status information like battery status, CPU and memory utilization are to be gathered in order to manage such a device fleet. In this paper a distributed monitoring service architecture to gather the required status data automatically is presented. The architecture is designed with respect to scalability requirements for a heterogeneous landscape mainly consisting of limited and low-powered devices. A prototype has been implemented and evaluated with respect to CPU and memory utilization. The performance analysis showed promising results in terms of the applicability of the chosen design.

Turbo codes were the first error-correcting codes that demonstrated reliable communications near the channel capacity with practically feasible hardware. Due to their excellent error-correcting capability, they are part of many modern communication technologies, like 3G, 4G, LTE, etc. An LTE turbo encoder is a rate-1/3 systematic encoder made of a first 8-state recursive convolutional encoder and a second 8-state recursive convolutional encoder. Recursive convolutional encoders are identical, coupled in parallel concatenation scheme, and connected with pseudo-random QPP interleaver. The decoding of LTE Turbo codes is iterative. One iteration starts with decoding of the first convolutional code and continues with decoding the second convolutional code. In order to achieve higher decoding speeds, recursive convolutional codes are decoded with the MAX-Log-MAP algorithm. In this paper, we decode LTE turbo codes with two decoding processes. In one of the decoding processes iterations start with the first convolution code, wherein in the other decoding process iterations start with the second convolution code. We combine the results of both decoding processes to improve turbo decoding process. In channel coding in many communications systems, the speed of decoding is the main design goal. Proposed decoding technique may be used to reduce the number of iterations of LTE turbo decoders, thus improving the decoding speed.

Docker is an open platform for developers and system administrators to build, ship, and run distributed applications using Docker Engine, a portable, lightweight runtime and packaging tool, and Docker Hub, a cloud service for sharing applications and automating workflows. The main advantage is that, Docker can get code tested and deployed into production as fast as possible. Different applications can be run over Docker containers with language independency. In this paper the performance of these Docker containers are evaluated based on their system performance. That is based on system resource utilization. Different benchmarking tools are used for this. Performance based on file system is evaluated using Bonnie++. Other system resources such as CPU utilization, memory utilization etc. are evaluated based on the benchmarking code (using psutil) developed using python. Detail results obtained from all these tests are also included in this paper. The results include CPU utilization, memory utilization, CPU count, CPU times, Disk partition, network I/O counter etc.