Types Of Workloads Research Articles

ANALYSIS OF COOKING STAFF NEEDS USING THE INDEX STAFFING NEEDS (ISN) AND WORKLOAD INDICATORS OF STAFFING NEEDS (WISN) METHOD IN THE NUTRITION INSTALLATION AT Dr. H ABDUL MOELOEK REGIONAL HOSPITAL OF BANDAR LAMPUNG

Introduction: An examination of energy requirements is necessary because an imbalance between working hours and workload andthe amount of energy available could lead to a significant risk of workplace mishaps. The research objective was to analyze the needfor cooking staff in the nutrition installation at the Dr.H Abdul Moeloek Regional Hospital of Bandar Lampung using the ISN and WISN methods, this is because from the 2023 report data, in one day the cooking staff serving meals to an average of 339 patients,whereas currently there are 29 cooking staff. Method: This research type is a qualitative analytical method using a cross-sectional design with an observation and interviewapproach. The research period is December 2023 to January 2024. The HR unit studied in this research is 29 cooking staff at theNutrition Installation at Dr. RSUD. H Abdul Moeloek Regional Hospital of Bandar Lampung. Result: The WISN approach yields 1.731,25 hours of total working time available, but the ISN method yields 1.737,50 hours/year.When the number of employees needed is taken into account, HR finds that the sub-units cooking staff has the greatest annual mainactivity of 784 minutes/year and the lowest annual main activity of 6076 minutes/year. The total result of all annual main activities forthe typical workload is 25090 minutes/year. The normal limit covers meeting activities, conversing, making personal phone calls,using the restroom, eating, and drinking. The criterion for the total allowance is 0.85. Conclusion: In the case of the ISN technique, where 38 people are needed for cooking staff, the shortage is 9 individuals; in the case of the WISN method, where 43 people are needed for cooking staff, the shortage is 14 people. Compared to ISN calculations, WISN calculations are more accurate and practical since they take into account the workload in the field.

A MODEL FOR PLANNING THE WORKLOAD OF TEACHERS, TAKING INTO ACCOUNT RISKS AND IN ACCORDANCE WITH THE REQUIREMENTS OF THE EUROPEAN SYSTEM OF CREDIT MODULES OF HIGHER EDUCATION

A model for planning the workload of teachers is proposed to address the unique demands of a credit-modular system in higher education, aligning with the European Credit Transfer and Accumulation System (ECTS) standards. This model seeks to balance teacher workload by considering various types of associated risks, such as shortages of qualified staff, limited resources, and the risk of department overload. The primary objective is to structure teaching plans for discipline modules in a way that optimizes available university resources while adhering to credit requirements. To maintain stability in higher education institutions and support the creation of new educational programs, it is essential to address key challenges. The ongoing progressive changes in the education sector of the Republic of Kazakhstan necessitate efforts to enhance the effectiveness of higher education institutions, develop innovative educational programs, and improve the overall quality of education. Key aspects of this model involve integrating risk management into the planning process, which allows for a more adaptive and resilient approach to curriculum design. By systematically linking different types of workloads to associated risks, the model facilitates the development of balanced teaching plans that support both educational quality and staff well-being. The study concludes that this model can be a powerful tool for optimizing teacher workload distribution, potentially enhancing the stability of the educational process. Additionally, the model lays the groundwork for the creation of software tools that could automate workload planning, enabling higher education institutions to mitigate risks more effectively. The proposed approach, therefore, not only improves planning accuracy but also aligns with European higher education standards, ensuring a sustainable, high-quality educational experience.

T-Smade: A Two-Stage Smart Detector for Evasive Spectre Attacks Under Various Workloads

Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only 59.8% for realistic applications, while it is much lower 27.52% for memory stress test. Therefore, this paper proposes a two-stage smart detector T-Smade designed for evasive Spectre attacks (e.g., evasive Spectre nop and evasive Spectre memory) under various workloads. T-Smade uses the first-stage detector to identify the type of workloads and then selects the appropriate second-stage detector, which uses four hardware performance counter events to characterize the high cache miss rate and low branch miss rate of Spectre attacks. More importantly, the second stage detector adds one dimension of reusing cache miss rate and branch miss rate to exploit the characteristics of various workloads to detect evasive Spectre attacks effectively. Furthermore, to achieve the good generalization for more unseen evasive Spectre attacks, the proposed classification detector T-Smade is trained by the raw data of Spectre attacks and non-attacks in different workloads using simple Multi-Layer Perception models. The comprehensive results demonstrate that T-Smade makes the average attack detection successful rate of evasive Spectre nop under different workload return from 27.52% to 95.42%, and that of evasive Spectre memory from 59.8% up to 100%.

Performance and Latency Efficiency Evaluation of Kubernetes Container Network Interfaces for Built-In and Custom Tuned Profiles

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI).

Agreement Between Heart Rate Variability - Derived vs. Ventilatory and Lactate Thresholds: A Systematic Review with Meta-Analyses.

Determining thresholds by measuring blood lactate levels (lactate thresholds) or gas exchange (ventilatory thresholds) that delineate the different exercise intensity domains is crucial for training prescription. This systematic review with meta-analyses aims to assess the overall validity of the first and second heart rate variability - derived threshold (HRVT1 and HRVT2, respectively) by computing global effect sizes for agreement and correlation between HRVTs and reference - lactate and ventilatory (LT-VTs) - thresholds. Furthermore, this review aims to assess the impact of subjects' characteristics, HRV methods, and study protocols on the agreement and correlation between LT-VTs and HRVTs. Systematic computerised searches for studies determining HRVTs during incremental exercise in humans were conducted. The agreements and correlations meta-analyses were conducted using a random-effect model. Causes of heterogeneity were explored by subgroup analysis and meta-regression with subjects' characteristics, incremental exercise protocols, and HRV methods variables. The methodological quality was assessed using QUADAS-2 and STARDHRV tools. The risk of bias was assessed by funnel plots, fail-safe N test, Egger's test of the intercept, and the Begg and Mazumdar rank correlation test. Fifty included studies (1160 subjects) assessed 314 agreements (95 for HRVT1, 219 for HRVT2) and 246 correlations (82 for HRVT1, 164 for HRVT2) between LT-VTs and HRVTs. The standardized mean differences were trivial between HRVT1 and LT1-VT1 (SMD = 0.08, 95% CI -0.04-0.19, n = 22) and between HRVT2 and LT2-VT2 (SMD = -0.06, 95% CI -0.15-0.03, n = 42). The correlations were very strong between HRVT1 and LT1-VT1 (r = 0.85, 95% CI 0.75-0.91, n = 22), and between HRVT2 and LT2-VT2 (r = 0.85, 95% CI 0.80-0.89, n = 41). Moreover, subjects' characteristics, type of ergometer, or initial and incremental workload had no impact on HRVTs determination. HRVTs showed trivial differences and very strong correlations with LT-VTs and might thus serve as surrogates. These results emphasize the usefulness of HRVTs as promising, accessible, and cost-effective means for exercise and clinical prescription purposes.

Efficient deep reinforcement learning based task scheduler in multi cloud environment

Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications

Time series database systems (TSDBs) are prevalent in many applications ranging from monitoring and IoT devices to scientific research. Those systems are specifically designed to efficiently manage data indexed by time. Because of the variety of workloads, the diversity of time series features, and the sophistication of existing TSDBs, there is no clear way to pick the most suitable system. In this demo, we introduce SEER, an automated, configurable, and interactive toolkit to evaluate TSDBs. SEER is based on TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. It implements an end-to-end pipeline for database benchmarking from data generation and feature contamination to workload evaluation. Users can define their portfolios by configuring and parameterizing custom queries, specifying their frequencies, controlling the type and level of data features, and indicating the type of workloads. Moreover, they can deploy new systems and/or reconfigure the pre-installed ones. SEER would process users' requests and gracefully recommend the best system on a use-case basis.

Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation

Cloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics.

DLSTM-MSF: Distributed LSTM Models for Multimedia Streaming Workload Forecasting Based on Kafka Environment

This paper introduces DLSTM-MSF, a distributed approach designed to address the challenge of demand forecasting in multimedia streaming workloads. DLSTM-MSF leverages the power of multi-LSTM networks, each tailored to predict data demand for a specific type of multimedia streaming workload. The central problem addressed in this research is the accurate prediction of workload demand in a dynamic and diverse multimedia streaming environment. To achieve specialization, the training time series set for each LSTM network comprises examples with targets belonging exclusively to the workload type it is designed to predict. This specialization ensures that each LSTM network becomes proficient at capturing the unique demand patterns associated with its designated workload category. The methodology of the proposed approach is based on building the best forecasting model for each multimedia streaming workload type by exploring various combinations of LSTM hyper-parameters using the grid search method. This enables the proposed approach to effectively capture nonlinear patterns in time series data. Furthermore, the implementation of DLSTM-MSF incorporates Apache Kafka for online demand prediction, utilizing the best-developed model for each workload type. Experimental evaluations of DLSTM-MSF compare the performance of two ensemble-learning LSTM models (Ensemble V1 and Ensemble V2) with a single LSTM model. The results unequivocally highlight the superiority of Ensemble V1, with reductions of 71.85% and 74.88% in RMSE and MAE values, respectively, compared to the single LSTM model. Index Terms— Multimedia streaming, LSTM, Ensemble learning, Forecasting, Workload Demand, Big data.

SLA-Adaptive Threshold Adjustment for a Kubernetes Horizontal Pod Autoscaler

Kubernetes is an open-source container orchestration system that provides a built-in module for dynamic resource provisioning named the Horizontal Pod Autoscaler (HPA). The HPA identifies the number of resources to be provisioned by calculating the ratio between the current and target utilisation metrics. The target utilisation metric, or threshold, directly impacts how many and how quickly resources will be provisioned. However, the determination of the threshold that would allow satisfying performance-based Service Level Objectives (SLOs) is a long, error-prone, manual process because it is based on the static threshold principle and requires manual configuration. This might result in underprovisioning or overprovisioning, leading to the inadequate allocation of computing resources or SLO violations. Numerous autoscaling solutions have been introduced as alternatives to the HPA to simplify the process. However, the HPA is still the most widely used solution due to its ease of setup, operation, and seamless integration with other Kubernetes functionalities. The present study proposes a method that utilises exploratory data analysis techniques along with moving average smoothing to identify the target utilisation threshold for the HPA. The objective is to ensure that the system functions without exceeding the maximum number of events that result in a violation of the response time defined in the SLO. A prototype was created to adjust the threshold values dynamically, utilising the proposed method. This prototype enables the evaluation and comparison of the proposed method with the HPA, which has the highest threshold set that meets the performance-based SLOs. The results of the experiments proved that the suggested method adjusts the thresholds to the desired service level with a 1–2% accuracy rate and only 4–10% resource overprovisioning, depending on the type of workload.

Gaze-Data-Based Probability Inference for Menu Item Position Effect on Information Search

This study examines the effect of menu items placed around a slideshow at the center of a webpage on an information search. Specifically, the study analyzes eye movements of users whose search time is long or short on a mixed-type landing page and considers the cause in relation to “directed search” (which triggers a certain type of mental workload). To this end, a Bayesian network model is developed to elucidate the relation between eye movement measures and search time. This model allows the implementation degree of directed search to be gauged from the levels of the measures that characterize a long or short search time. The model incorporates probabilistic dependencies and interactions among eye movement measures, and hence it enables the association of various combinations of these measure levels with different browsing patterns, helping judge whether directed search is implemented or not. When viewers move their eyes in the direction opposite (identical) to the side on which the target information is located, the search time increases (decreases); this movement is a result of the menu items around the slideshow capturing viewers’ attention. However, viewers’ browsing patterns are not related to the initial eye movement directions, which may be classified into either a series of orderly scans (directed search) to reach the target or long-distance eye movements derived from the desire to promptly reach the target (undirected search). These findings suggest that the menu items of a website should not be basically placed around a slideshow, except in cases where they are intentionally placed in only one direction (e.g., left, right, or below).

Academic integrity: A restorative justice approach in first year engineering

Academic integrity is a cornerstone of post-secondary education. Academic integrity violations disrupt the soundness of the assessment process, which is exacerbated in professional programs like engineering where accreditation hinges on the measurement of student proficiencies and graduate attributes. Engineering programs are typically challenging with demanding schedules and higher than typical workloads. Freshmen often face this challenge with deficient time management skills, which is coupled with increasing student mental health and wellness concerns. Combined with pressure to perform, these systemic issues can create circumstances in which students rationalize opportunities that constitute potential code of conduct violations, especially in group situations. The academic misconduct investigation process can be resource intensive, time intensive and stressful for students, instructors, and administrators. A restorative justice model was implemented as an alternative path to manage a large number of cases in first year engineering. The objective of using this approach was to educate students, emphasize the connection between academic integrity and engineering ethics and prevent further occurrences. This paper describes the development and use of this collaborative approach for first year.

A method for assessing the characteristics of the virtual machine migration process, taking into account the type of migrations and applications

Dynamic resource allocation in cloud computing is a critical aspect. It is necessary for computing resources scaling to maintain application performance, reduce costs and ensure the reliability of IT systems. Dynamic resource allocation is possible using open virtual machines (VM), but live migration is a relatively expensive and resource-intensive operation. A number of VM migration algorithms have been proposed, each with different performance characteristics depending on the state of the host system, network, and the VM workload. Recently, a large number of works have emerged that have developed models for assessing the effectiveness of of VM migration, but many of them do not provide a standard of satisfactory prediction accuracy and are built for a single migration algorithm, which limits the use of data models. This work uses a statistical approach to estimate the total migration time and VM downtime based on the approximation of probability densities by Gram-Charlier and Laguerre Series. The proposed method, in comparison with the known ones, allows one to answer the question of what is the probability of total migration time and VM downtime for a given migration type and VM application. The analysis of total migration time and VM downtime is based on the Virtual Machine Live Migration Dataset includes more than 40 000 virtual machine migration records with five different live migration algorithms: post-copy migration, pre-copy migration and its modifications: CPU throttling delta compression, and data compression. The dataset includes approximately 8000 migration records of each migration algorithm with 9 types of workloads. Analysis of the results allows us to conclude that the type of application significantly influences both the shape of the empirical distribution (histograms) of total migration time and its characteristics, therefore it is advisable to obtain an analytical expression for the distribution law of the total migration time and VM downtime taking into account these circumstances. The results of the experiments shows that this method does not depend on the migration algorithm and the type of working application. The use of the Laguerre series can be recommended as giving more reliable results compared to Gram-Charlier series. A method for estimating total migration time and VM downtime can be integrated into a management system to select the best monitoring window for servers and evaluate service-level agreement terms.

Algorithmic Proficiency in Spark Configuration Tuning: An Empirical Study using Execution Time Metrics across Varied Workloads

In the realm of big data, where datasets of immense scale pose processing challenges, distributed processing platforms like open-source Apache Spark have emerged to address these issues. Spark’s internal configuration parameters exert varying impacts on execution times based on job characteristics, making manual optimization daunting. The core focus of this study lies in optimizing Spark’s internal configurations, with specific attention directed towards three types of workloads: Iterative-intensive, Memory-intensive, and CPU-intensive. Employing Grid Search, Random Search, and Evolutionary Optimization algorithms yields substantial execution time reductions: 23.24% with Grid Search, 19.71% with Random Search, and 23.06% with Evolutionary Optimization. Notably, Evolutionary Optimization achieves optimal configurations approximately 29% faster than Grid Search. While Random Search and Evolutionary Optimization share similar time requirements, Random Search’s execution time reduction for a given Spark workload is relatively lower. This research sheds light on algorithmic configuration tuning intricacies and its influence on Spark workload execution times, contributing to the exploration of optimizing big data processing platforms.

Formation of health skills in the context of implementing the discipline “Physical education and sports” in students of non-core universities and specialties

The purpose of the study was to study the ideas of the first-year students and the content of the concept of “physical culture” in the context of health care. 150 first-year students took part in the study. At the first stage they were asked to give their understanding of the terms “physical culture” and “health”, and at the second stage the students were offered theoretical classes. During the lessons, students developed competencies related to independent health assessment skills, as well as the ability to choose appropriate types of workload. During the analysis of the students’ works, their ideas about health and ways to preserve it were revealed. The results of the study show the role of evaluating “incoming” knowledge and ideas for the selection of content and methods of working with students within academic disciplines.

A study on impacts of hardware changes on performance of different multithreading libraries based on Gem5

This paper presents a thorough investigation into the performance of various multithreading libraries on different hardware configurations utilizing gem5. The study compares the performance of commonly-used multithreading libraries, including OpenMP, Pthread, TBB, and Dlib, through several benchmarks representing distinct types of workloads. Key hardware factors that directly affect the performance of each library are identified and analyzed. The research findings provide valuable insights and recommendations for selecting an appropriate multithreading library for a given application and hardware platform. Results indicate that the performance of multithreading libraries is influenced by numerous hardware factors, such as L1 cache size, L1 cache associate, L1 cache replacement policy, number of threads, and memory type. The libraries respond differently to these factors, and the performance variability of each library depends on the nature of the workload. The study offers a detailed analysis and valuable insights into the effects of these hardware factors on each library. This study contributes by presenting a comprehensive comparison of several widely used multithreading libraries across numerous hardware configurations, identifying the key hardware factors that influence the performance of each library, and providing insights and recommendations for selecting an appropriate multithreading library for a given application and hardware platform. Ultimately, the study results can help software developers make informed decisions when selecting a multithreading library and optimizing performance for their applications.

Algorithmic Complexity Attacks on Dynamic Learned Indexes

Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies have focused on designing efficient dynamic learned indexes. ALEX, as the first and one of the representative dynamic learned index structures, enables dynamism by incorporating a series of design choices, including adaptive key space partitioning, dynamic model retraining, and sophisticated engineering and policies that prioritize read/write performance. While these design choices offer improved average-case performance, the emphasis on flexibility and performance increases the attack surface by allowing adversarial behaviors that maximize ALEX's memory space and time complexity in worst-case scenarios. In this work, we present the first systematic investigation of algorithmic complexity attacks (ACAs) targeting the worst-case scenarios of ALEX. We introduce new ACAs that fall into two categories, space ACAs and time ACAs, which target the memory space and time complexity, respectively. First, our space ACA on data nodes exploits ALEX's gapped array layout and uses Multiple-Choice Knapsack (MCK) to generate an optimal adversarial insertion plan for maximizing the memory consumption at the data node level. Second, our space ACA on internal nodes exploits ALEX's catastrophic cost mitigation mechanism, causing an out-of-memory (OOM) error with only a few hundred adversarial insertions. Third, our time ACA generates pathological insertions to increase the disparity between the actual key distribution and the linear models of data nodes, deteriorating the runtime performance by up to 1, 641× compared to ALEX operating under legitimate workloads.

Reviewing Cooling Strategies for Post-Dennard Era Computing and Modern Electrothermal Phenomena

Excessive thermal runaway typically manifests itself in computer component damage and other various negative side effects. As a mitigation strategy, end-users and consumers typically implement differing methods of component cooling, including fan-heatsink cooling, heatsink-only cooling, and liquid-based cooling. Different cooling methods remain impactful to modern computing, as it remains a core component in the thinking of thermal design and reliability engineering in the realm of semiconductor devices. The industry-wide acceptance of the end of Dennard scaling and the imminent end of Moore’s law are major factors that are currently impacting CPU power consumption trends and modern cooling philosophies. Transistor packing and process refinement is beginning to push against atomic boundaries in combination with phenomena such as leakage current and high current density, causing a general trend of increasing temperatures generation-to-generation in microprocessors. As a result, thermal mitigation strategies and protections must be in place to reduce damage and catastrophic failure while increasing performance of the die package. In a high-heat scenario, liquid-cooling can provide up to 38% to 48% improvement over fan-heatsink variants depending on the type of workload executed by the processor. Fan-heatsink cooling faces thermal resistance limitations in the form of spreading and air convection resistance as a result of heatsink material composition, the resistance along the path of heat flow impeding conduction rate, and the lower thermal conductivity of air compared to liquid. Currently, the best performing variant appears to be liquid-based cooling while fan-heatsink combinations provide adequate levels of thermal dissipation based on these observations.

K2vTune: A workload-aware configuration tuning for RocksDB

The explosive growth in data has triggered the emergence of NoSQL databases, which can be used to efficiently deal with unstructured and semi-structured data. RocksDB is a widely used persistent key–value store developed by Facebook to support high-throughput storage environments. It provides flexible configuration knobs that can be tuned to optimize performance. However, optimizing the RocksDB performance for specific workloads is challenging because the knobs have complicated inner relationships and different impacts according to workload type. In addition, RocksDB performance is measured using multiple metrics, such as latency and space amplification, which should be simultaneously considered. To address these issues, we designed a workload-aware configuration tuning framework called K2vTune, which can recognize the configuration knobs of RocksDB according to the workload type and effectively consider multiple performance metrics using our knob2vec method. The knob2vec method learns feature vectors for knobs that can capture both the latent relationships between knobs and workload-specific features to develop an attention neural network for accurate configuration-based performance prediction. K2vTune converts multiple metrics into a single problem and optimizes the problem using a genetic algorithm. We evaluated the proposed method using six different RocksDB workloads and confirmed that K2vTune achieved significant tuning performance improvement compared to baselines, showing 8.83% improvements over the state-of-the-art baseline.

Online learning in Indonesian higher education: New indicators during the COVID-19 pandemic

<span lang="EN-US">The COVID-19 pandemic has increasingly demanded the implementation of online learning in Indonesia. This study aimed to determine the indicators influencing the effectiveness of online learning in Indonesian higher education during the COVID-19 pandemic. The study employed a mixed-methods approach. We conducted an online survey of 201 lecturers teaching courses during the even semester of 2019/2020 and 1,983 students in 18 higher educations then followed up with an online interview with the lecturers, the head of the study program, and the institute of quality assurance of four chosen institutions. The results showed that besides the lecturer’s information technology capabilities, the new indicators of online learning effectiveness in higher education during the COVID-19 pandemic were the availability of assessment guidelines, semester learning plan guidelines, academic position, type of lecturer publication, lecturer certification, and workload.</span>