Reviewerly/exHarmony huggingface dataset:
Inducing Huge Tail Latency on a MongoDB deployment

The NoSQL paradigm has emerged as the leading design choice for cloud providers offering highly scalable storage services. Contrary to traditional relational databases, NoSQL architectures are capable of ingesting the ever-growing volume of nowadays’ data-driven applications characterized by low-latency and high-throughput requirements. However, it is difficult to build an ultra-scalable, high-performance storage engine that can sustain an arbitrary number of concurrent clients. A common technique to increase throughput is minimizing the OS overhead, quantified as the number of context switches, through busy waiting (or "spinning"). While this simple synchronization mechanism proves to be beneficial in the high-performance computing community, it requires special care to avoid wasting resources and counter-intuitive behaviors. In this paper, we address an instance of "unsafe" busy waiting in WiredTiger, the underlying storage engine of MongoDB, which leads to a consistent, excessive increase of tail latency in high contention scenarios.
Evaluation of Data Enrichment Methods for Distributed Stream Processing Systems

Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Things, business intelligence, and telecommunications, real-time processing of unbounded data streams has become a necessity. DSP systems provide a solution to this challenge, offering high horizontal scalability, fault-tolerant execution, and the ability to process data streams from multiple sources in a single DSP job. Often enough though, data streams need to be enriched with extra information for correct processing, which introduces additional dependencies and potential bottlenecks.In this paper, we present an in-depth evaluation of data enrichment methods for DSP systems and identify the different use cases for stream processing in modern systems. Using a representative DSP system and conducting the evaluation in a realistic cloud environment, we found that outsourcing enrichment data to the DSP system can improve performance for specific use cases. However, this increased resource consumption highlights the need for stream processing solutions specifically designed for the performance-intensive workloads of cloud-based applications.
GreenCoin: A Renewable Energy-Aware Cryptocurrency

In this paper, we propose GreenCoin – an energy-efficient cryptocurrency system with mining protocols designed to favor locations with relatively higher availability of renewable energy. Traditionally, crypto coin mining involves solving complex mathematical problems by high-end computing devices consuming an enormous amount of electricity, thus adversely affecting net carbon emissions. To reduce cost and emissions, GreenCoin uses a modified proof of stake (PoS) consensus algorithm, which itself is more energy efficient compared to other state-of-the-art methods. Our modified PoS algorithm, called Green PoS (GPoS), allows GreenCoin to favor nodes (with reward and privilege) located in regions with higher availability of renewable energy. We present a detailed system architecture of GreenCoin and explain the operating method of GPoS. We also provide results from empirical studies demonstrating the renewable energy-aware approach of GreenCoin.
Semantic Communication Based on Entity Information Enhancement

Recently, the rapid development of deep learning has provided strong support for the implementation of semantic communication. A noteworthy issue is the application of the knowledge base. Deploying and generating the knowledge base necessitate substantial caching resources, and requesting it frequently may increase communication overhead. We consider the issue of improving the performance of text semantic communication systems in the context of a small-scale knowledge base, and propose a system based on entity information enhancement. In particular, we design an entity recognizer to identify the entity information in the source text and enhance the information via secondary coding. Simulation results demonstrate that the proposed system can effectively extract the entity information and achieve performance improvement at the receiver.
Efficient Container Image Updating in Low-bandwidth Networks with Delta Encoding

Containers are the technology for Linux to isolate execution environments. By distributing a container image, which is a collection of files contained in the container, users can use an execution environment that includes the necessary files and libraries. However, container images are tens to hundreds of megabytes in size and require many network resources to be transferred. Especially in low-bandwidth network environments like edge computing, frequent image updating can be difficult and affect other services’ communication. In this paper, we propose a method to reduce the data size required for image updates using delta encoding. We use delta encoding to reduce data size and finish updating quickly, but generating and applying deltas is a time-consuming operation. Our method proposes DeltaMerging which enables faster delta generation by merging existing deltas, and Di3FS which applies deltas lazily. The proposed method reduces the data size required to update container images from 5 to 40% of that of existing methods. Also, the time required to generate and apply deltas is greatly reduced with DeltaMerging and Di3FS. Furthermore, the performance degradation of the application in the container was almost negligible.
Studying the Energy Consumption of Stream Processing Engines in the Cloud

Reducing the energy consumption of the global IT industry requires one to understand and optimize the large software infrastructures the modern data economy relies on. Among them are the data stream processing systems that are deployed in cloud data centers by companies, such as Twitter, to process billion of events per day in real time. However, studying the energy consumption of such infrastructures is difficult because they rely on a complex virtualized software ecosystem where attributing energy consumption to individual software components is a challenge, and because the space of possible configurations is large. We present GreenFlow, a principled methodology and tool designed to automate the deployment of energy measurement experiments for data stream processing systems in cloud environments. GreenFlow is designed to deliver reproducible results while remaining flexible enough to support a wide range of experiments. We illustrate its usage and show in particular that consolidating a DSP system in the smallest number of servers that are capable of processing it is an effective way to reduce energy consumption.
Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning

Efficiently allocating incoming jobs to nodes in large-scale clusters can lead to substantial improvements in both cluster utilization and job performance. In order to allocate incoming jobs, cluster schedulers usually rely on a set of scoring functions to rank feasible nodes. Results from individual scoring functions are usually weighted equally, which could lead to suboptimal deployments as the one-size-fits-all solution does not take into account the characteristics of each workload. Tuning the weights of scoring functions, however, requires expert knowledge and is computationally expensive.This paper proposes a reinforcement learning approach for learning the weights in scheduler scoring algorithms with the overall objective of improving the end-to-end performance of jobs for a given cluster. Our approach is based on percentage improvement reward, frame-stacking, and limiting domain information. We propose a percentage improvement reward to address the objective of multi-step parameter tuning. The inclusion of frame-stacking allows for carrying information across an optimization experiment. Limiting domain information prevents overfitting and improves performance in unseen clusters and workloads. The policy is trained on different combinations of workloads and cluster setups. We demonstrate the proposed approach improves performance on average by 33% compared to fixed weights and 12% compared to the best-performing baseline in a lab-based serverless scenario.
Enabling Serverless Sky Computing

The Sky Computing vision represents a unified multi-cloud environment where applications can be deployed to utilize resources from different cloud regions, resource configurations, and cloud providers. Serverless computing platforms have recently emerged, offering automatic elastic scaling, high performance, and reduced costs but often utilize proprietary deployment tools and services locking users into platform-specific services. This research aims to apply Sky Computing to serverless computing platforms offered by major cloud providers such as Amazon Web Services, Google Cloud, Azure, and more. This research will build a serverless sky architecture to enable the aggregation of serverless resources to achieve service-level objectives such as low hosting costs, high performance, fault tolerance, high throughput, and low carbon footprint. The research will focus on evaluating the performance implications of serverless aggregation (Thrust-1), design and evaluation of Sky Computing architectures and aggregation strategies (Thrust-2), and finally autonomous resource aggregation for intelligent self-management of applications deployed to the sky (Thrust-3).
Cost-Effective Scheduling for Kubernetes in the Edge-to-Cloud Continuum

The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g. close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for container orchestration. It is very efficient in a data center environment, but it fails to give the same performance when adding edge resources. At the edge, resources are more limited, and networking conditions are changing over time.In this paper, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. A cost-aware scheduler enables this optimization. We are also monitoring the Key Performance Indicators of the applications to ensure that cost optimizations do not impact negatively their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the application. To demonstrate the performance of our scheduling approach, we evaluate it on a vehicle cooperative perception use case, a representative 5G application.
Adaptive Gradient Methods For Over-the-Air Federated Learning

Federated learning (FL) provides a privacy-preserving approach to realizing networked intelligence. However, the performance of FL is often constrained by the limited communication resources, especially in the context of a wireless system. To tackle this communication bottleneck, recent studies propose an analog over-the-air (A-OTA) FL paradigm which employs A-OTA computations in the model aggregation step that significantly enhances scalability. The existing architectures mainly conduct model training via (stochastic) gradient descent, while adaptive optimization methods, which have achieved notable success in deep learning, remain unexplored. In this paper, we establish a distributed training paradigm that incorporates adaptive gradient methods into the A-OTA FL framework, aiming to enhance the system’s convergence performance. We derive an analytical expression for the convergence rate, capturing the effects of various system parameters on the convergence performance of the proposed method. We also perform several experiments to validate the efficacy of the proposed method.
Semantic Knowledge Base Synchronization by Limited Feedback Assistance

To address the challenge of maintaining fast and reliable communication in the face of a staggering amount of data, a new communication paradigm called semantic communication has gained significant interest. Semantic communication is expected to play a crucial role in future communication systems. A major challenge in semantic communication is dealing with the mismatched knowledge base between the transmitter and receiver. To overcome this problem, this paper proposes a feedback-aided semantic communication system. In this system, the receiver can enhance semantic decoding performance through simple query and answer interactions with the transmitter. The interpretation of meaning at the knowledge base of the receiver could be updated based on the feedback received from the transmitter. This paper presents a theoretical analysis that establishes a relationship between the number of feedback and the correct semantic decoding probability. This paper also analyzes the performance of the greedy policy and identifies the conditions under which the greedy policy is the optimal policy. Simulation results validate this result.
See-Through HMD With Triangular Microstructure for High Visibility

An optical design for a see-through head-mounted display with high visibility is described. This design, which is based on a light guide plate with triangular microstructures, can overcome the limitations of the balance in optical efficiency. The optical efficiency of the virtual image can be larger than the optical efficiency of the real image.
High-Performance Optical Convolutional Neural Network Accelerator with High-Speed Optical Modulator and Carrier-Injection-Type Attenuator

Optical convolutional neural networks have attracted attention because of their parallel processing capability. We demonstrated image restoration with sufficiently small MSE value using high-speed optical transmission system with a high-speed optical modulator and carrier-injection-type attenuators. 0.72 TOPS of multiple-accumulate operations were demonstrated both in simulation and experiment.
An Attention Based LSTM Model: Automated Requirement Classification from User Story

A significant obstacle in Software Requirement Specification (SRS) is the manual classification of software requirements, which can be a subjective and time-consuming endeavor. It can result in inconsistency and the possibility of classification errors in the requirement collection process. Moreover, as the number of requirements rises, the required manual labor becomes increasingly difficult and impracticable. To surmount these obstacles, therefore, the classification of software requirements must be automated. Hence, in this research, an attention-based LSTM model is proposed, for automatic classification with the goal of addressing the significance of automation in software requirement classification. An experimental study has been conducted on two datasets containing nearly 1,039 Functional and Non-Functional Requirements (FR, NFR). The experimental results demonstrate that the proposed model classifies FR and NFR with an accuracy of 98% on the testing dataset. The outcome underscores the effectiveness of utilizing attention-based LSTM models for requirement classification and alleviates the ambiguities and challenges associated with manual classification, ultimately improving the effectiveness and reliability of software requirement analysis.
Securing Vaccination Data Using Self-Sovereign Identity, Hyperledger Fabric and Zero Trust Model

People of all ages need a variety of vaccines to avoid spreading preventable diseases. For future reference and analysis, vaccination information must be stored maintaining proper security in a Vaccination Data Management (VDM) system. In this paper, a secured data preserving framework is proposed using the Self-Sovereign Identity Management (SSIM), Hyperledger Fabric and Zero Trust Model (ZTM). SSIM is used for sharing secured data following authentication, integrity verification and control using digital identity. Besides, a proof-of-concept is added using Hyperledger Fabric platform as a permissioned blockchain which operates on a distributed network and can only be accessed by valid users. ZTM features are incorporated with this proposed model to enhance security better than the other existing data securing frameworks.
Quantum Technology: Unleashing the Power and Shaping the Future of Cybersecurity

Cybersecurity refers to the practice of protecting computer systems, networks, and digital data from unauthorized access, attacks, damage, and theft. It involves implementing measures and techniques to ensure the confidentiality, integrity, and availability of information and computing resources. Cybersecurity encompasses various disciplines and activities to safeguard digital assets and prevent or mitigate potential threats [1]. Today, information security has emerged as one of the foremost challenges in our interconnected world. When we consider cyber security, the immediate concern that comes to mind is the exponential growth of cybercrimes. Governments and businesses alike are taking various measures to combat this form of criminal activity. Furthermore, there is a growing apprehension among individuals regarding cybersecurity issues. This paper primarily focuses on the concerns surrounding cyber security in relation to emerging technologies. It also delves into the advancements and ethical considerations that influence the field of cyber security [2]. Classical computers require significant time to solve these problems, making encryption secure. However, quantum computers have the potential to solve these problems much more efficiently than classical computers. However, the rapid progress in quantum computing technology necessitates proactive measures to develop and adopt quantum-resistant cryptographic solutions to ensure the long-term security of sensitive information. Ongoing research and collaboration between academia, industry, and standardization bodies are essential in addressing the quantum threat to cybersecurity [3].
BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language

Natural Language Processing (NLP) has entered a new era with the advent of pre-trained language models, paving the way for constructing robust language models. Pretrained transformer-based models such as GPT-2 have become prevalent due to their cutting-edge efficiency. However, these approaches rely heavily on resource-intensive languages, forcing other languages to adopt multilingual frameworks (mGPT). The mGPT model could perform better for low-resource languages such as Bangla because the model has been trained on a diverse dataset spanning multiple languages. Recent studies show that the language-specific GPT model outperforms the multilingual mGPT model. In this research, we have proposed a pretrained monolingual GPT model called BanglaGPT using the objective of causal language modeling (CLM). Due to the lack of available large datasets for NLP tasks in Bangla, we have created a Bangla language model dataset called BanglaCLM using a 26.24 GB Bangla corpus scraped from several public websites. We have used a subword-based tokenization algorithm named Byte-Pair Encoding (BPE) for Bangla and finally trained the Bangla-GPT2 model from scratch using the BanglaCLM dataset. Our pretrained BanglaGPT provides state-of-the-art performance for Bangla text generation with a perplexity score of 2.86 and a loss score of 0.45 on the test set.
Exploring ChatGPT in Network Management and Monitoring: Benefits and Challenges

ChatGPT is an AI-based natural language model that has made a significant impact across various technological domains, including software development, network management, and the like. Network monitoring is one of the critical tasks in network management to ensure the proper and safe functioning of the entire network. The primary objective of this research is to explore the potential benefits and challenges associated with using ChatGPT in network management and monitoring. As an outcome, this research has highlighted that ChatGPT offers diverse functionalities in networking, including automation of operations and easy code generation, enabling instant feedback to enhance the quality and accessibility of network management tasks. However, the study also acknowledges the challenges inherent in using ChatGPT. Biased output is identified as a concern that could potentially impact response accuracy and fairness. Additionally, the possibility of errors in the generated output can undermine system reliability and effectiveness. While ChatGPT demonstrates the ability to simulate human-like conversations, it is important to address these challenges promptly for responsible and effective utilization in the domain of computer network.
Association Rules Mining for a Specific Time Period in a Day in Large Transactional Database

Market basket analysis is a trendy topic in the field of data mining. Analyzing the daily transactions, several types of associations were noticed in the transacted item sets. The transactional database used in retail marketing has a large number of itemsets that are frequently purchased exclusively during specific hours of the day and are not usually considered to be frequently purchased. In some instances, association rule mining at a lower data level with uniform support doesn't reveal any notable patterns, yet it may be suppressing crucial data. For instance, the morning session sees the biggest demand for items like bread and cake, while the afternoon and evening sessions find higher demand for items like drinks and noodles. In the transaction at a supermarket, this study discovered the frequent item and the link among the frequent item sets. In this study, we used the tree-based FP-growth idea to locate the pertinent rules in the transaction based on the time of day, specifically the morning, afternoon, and evening. Our empirical analysis demonstrates that the suggested approach is computationally effective in a different time period of the day, enabling the Bangladeshi retailer Super Shop to make the best decisions and boost sales.
Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city.