Reviewerly/exHarmony huggingface dataset:
Gauging Carbon Footprint of AI/ML Implementations in Smart Cities: Methods and Challenges

A smart city aspires to enhance quality of life, optimize city operations, and promote economic growth with the use of AI/ML techniques. However, the AI/ML techniques themselves often produce carbon emission due to their high demand for computations during their training. Environmentally sustainable Smart Cities require systematic measure of its carbon footprint and approaches to reduce carbon emission from cities backbone edge networks and cloud data centers. This work studies the methods and challenges in gauging the carbon footprint produced by the AI/ML implementations in smart cities.
Performance Comparison of Big Data Processing Utilizing SciDB and Apache Accumulo Databases

Big data deals with processing massive, complex data sets and data volumes that incorporate a tremendous amount of information. Therefore, researchers created several methods, models, and databases to deal with such big data, among them is the Apache Accumulo database, which is considered an in-storage database reliant on the Hadoop processing framework to give the ability to analyze and process the data. Another big data database that is widely used in the research community is SciDB which stands for the scientific database. SciDB utilizes a PostgreSQL connection, to establish a reliable link with the database. In this paper, we will analyze and evaluate the performance of these two database systems that are specialized in handling big data and storing them for further processing and analysis. The databases' performance will be analyzed in terms of several metrics such as CPU utilization, data storing/retrieval delay, disk utilization, and the number of data ingestions per second. Furthermore, the setup and integration of the two databases are investigated. Our performance evaluation revealed the advantages and disadvantages of each database structure. Where it has been found that Apache Accumulo DB has the best performance compared with SciDB in terms of average ingestion execution time, the number of ingestions per second, and CPU utilization. Whereas, SciDB has the lowest disk utilization compared to Apache Accumulo.
A Framework of Secure Computation as a Service in Decentralized Environment

With the increasingly stringent regulatory requirements for data security all around the world, advanced privacy preserving data protection strategies and corresponding secure computation protocols are in urgent needs. The traditional methods for original data transaction and processing may conflict with the data regulation and laws. Thus, the object of data transaction nowadays is no longer the original data, but the encapsulated data services, which seal and combine various computing models and protocols that operate on local data. This paper concludes the cryptography based secure computation protocols including proxy re-encryption (PRE), multiparty computation (MPC), and zero-knowledge proof protocol ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ZK$</tex> ), then organizes these protocols into smart contract-based layers on blockchain to support verifiable credentials and presentations, authorization-based data re-encryption, and MPC-based data secure computation as well as aggregations with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ZK$</tex> property.
Deep Learning Approach For Human Emotion-Gender-Age Recognition

Deep Learning has found massive applications in the area of Computer vision. In everyday activities, each individual connect with the other via facial expressions too along with basic communication languages. Emotions allow a person to convey his or her inner state of mind. In this project, we developed a CNN model and inception V3 model to train and to identify various human emotions like anger, sadness, disgust, fear, happiness, neutral feeling along with their age and gender using image, video, and audio.
Improved Privacy Conservation Applicability for the Disturbed Data in Multi Partitioned Data Collection Environment

The perturbation technique was widely considered for the protection of the privacy for different datasets in data mining. Multi-partitioned datasets generally consist of together horizontal and vertical data Collection, that are a existing demand in the data mining environment of e- Pay and e-Commerce. In the phase of perturbation, arbitrary disturbance from a known scattering is processed as data susceptible to privacy, prior to the information miner being thrown. Consequently, the data-miner reconstitutes estimates for the specific distribution of data from the disrupted data and exercises the restored delivery of data mining principles. According to the count of noise, information loss versus privacy protection in perturbation-based techniques is a constant transaction. The problem is to what degree of privacy are consumers able to cooperate? This is a choice which is shifting from person to person. The first work is to define the technique of data perturbation with validation and authentication in order to determine a trade-off between data protection and the usability of the data. Diverse people may have different secrecy strategies, focused on customs and cultures. Inappropriately, the earlier privacy-included perturbation that preserves data mining procedures does not allow entities to choose their preferred levels of privacy. It is a negative thing, because privacy is a preference for individuals. Researchers in this study suggest an independently adaptable perturbation model that allows persons to select their own degree of confidentiality. The success of the proposed model lies in Enhancing the Privacy Conservation Applicability for Disturbed Data in Multi-partitioned datasets (PADDM), as validated by numerous studies performed on both unreal and real-world data Collection. Focused on the experimental evaluation, researchers are proposing a simple, useful and resourceful method for building data mining representations from disturbed facts and enhancing the privacy conservation process.
The Effect of Programming Language in Software Bug Prediction

Software maintenance is a very important phase in the life cycle of software development. As part of maintenance, we need to identify bugs within the code and fix them for every release. Software bug prediction (SBP) allows us to identify modules in the software that may have the tendency to be buggy. This enables us to perform targeted testing and properly plan maintenance cycles. In most research performed, we observed that dataset of software programs written in C language were used. Each programming language are inherently different and has it's own constructs, practices and nuances. In this research we focus on identifying if there exists a specific classifier that works well for each programming language which in turn is used to analyze the effect of programming language on Software Bug Prediction. Datasets of software written in C, C++ and Java has been collected and the most accurate classifier for each dataset of a programming language has been identified. The ML models used in this paper include Naive Bayes, Decision Tree and Random Forest.
Performance, Energy and Parallelism: Using Near Data Processing in Utility and Cloud Computing

Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. The paper describes our work building on ideas based on the Actor model of computation. These include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. The paper will conclude with a presentation with a summary of open research problems.
Resolution Matters: Revisiting Prediction-Based Job Co-location in Public Clouds

Overall resource utilization in public cloud data centers remains very low. To increase the efficiency of these data centers, low priority batch jobs are often co-located on the same machines as latency-sensitive jobs. Existing methodologies have used machine learning to predict the amount of resources that should be reserved for these jobs to maintain acceptable latency. However, these methodologies overlook the impact of measurement granularity on usage prediction and scheduling performance. When batch jobs have long durations, coarsegrained data can be used to make the prediction problem less challenging, but the resulting predictions may degrade scheduling performance. In this paper, we investigate the impact of measurement granularity on scheduler performance using extensive trace-driven simulation and job data generated from the Alibaba cluster trace.
Enabling Awareness of Quality of Training and Costs in Federated Machine Learning Marketplaces

The proliferation of data and machine learning (ML) as a service, coupled with advanced federated and distributed training techniques, fosters the development of federated ML marketplaces. One important, but under-researched, aspect is to enable the stakeholder interactions centered around the quality of training and costs in the marketplace and the service models in federated ML training. This paper conceptualizes a federated ML marketplace and proposes a framework to enable the awareness of the quality of training and a variety of costs where both data providers and ML model consumers can easily value the contribution of each data source to ML model performance in nearly real-time. This improves the transparency and explainability iv.r.t. the quality and costs for participation in the marketplace. Based on that, we design and implement the quality of training and cost awareness framework for an edge federated ML marketplace. Experimental realistic scenarios show the usefulness of cost and quality details that provides insightful information for various purposes, including potential budget management and training optimization.
Kubitect - a Solution for On-premise Cluster Deployment

Deploying compute clusters is easy and user-friendly when using modern public cloud solutions, however alternative open source solutions for smaller, non-enterprise setups, such as hobby projects, home labs, or small and micro enterprises are currently missing. In this paper, we identify challenges for on-premise environments and propose Kubitect- a solution that allows anyone to set up their own on-premises private cloud that can run containerized applications and is easy to maintain. Unlike the existing state of the art, the proposed solution enables the creation of clusters on multiple physical hosts using a single declarative configuration file that can be easily understood and versioned. The paper describes the user and automated workflows of Kubitect, elaborates on the design choices for implementation and provides a qualitative and quantitative evaluation. The high-availability of on on-premises cluster deployment is also validated.
Logical Optimisation and Cost Modelling of Stream-Processing Programs Written in a Purely-Functional Framework

We present a vision for the automatic optimisation of distributed stream processing programs. StrIoT -a distributed stream-processing framework built using purely-functional programming – enables a set of validated logical optimisation rules to generate a set of possible deployment plans. A cost model then filters and ranks the plans before the best is automatically deployed across the cloud and edge devices. We describe StrIoT’s functional operators for writing stream-processing programs; the design, implementation and performance of StrIoT’s logical optimiser; and the cost model, which filters and ranks re-written programs and deployment plans in terms of two non-functional requirements: bandwidth and cost. The StrIoT vision is being explored through an open-source proof-of-concept implementation. We present our initial results with a motivating example before outlining the success criteria for future work in this area.
CNN-assisted Road Sign Inspection on the Computing Continuum

Processing rapidly growing data encompasses complex workflows that utilize the Cloud for high-performance computing and the Fog and Edge devices for low-latency communication. For example, autonomous driving applications require inspection, recognition, and classification of road signs for safety inspection assessments, especially on crowded roads. Such applications are among the famous research and industrial exploration topics in computer vision and machine learning. In this work, we design a road sign inspection workflow consisting of 1) encoding and framing tasks of video streams captured by camera sensors embedded in the vehicles, and 2) convolutional neural network (CNN) training and inference models for accurate visual object recognition. We explore a matching theoretic algorithm named CODA [1] to place the workflow on the computing continuum, targeting the workflow processing time, data transfer intensity, and energy consumption as objectives. Evaluation results on a real computing continuum testbed federated among four Cloud, Fog, and Edge providers reveal that CODA achieves 50%-60% lower completion time, 33%-59% lower CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, and 19%-45% lower data transfer intensity compared to two stateof-the-art methods.
Understanding Microquanta Process Scheduling for Cloud Applications

Process schedulers are responsible for arbitrating CPU resources among services. Unfortunately, traditional sched-ulers, working at millisecond scale and characterized by strict priority schemes, are no longer suitable to meet increasingly stringent and diverse requirements imposed by many workloads. Recognizing this aspect, the research community has recently proposed new schedulers operating at microsecond granularity. This work studies microsecond-scale schedulers and policies for data center applications from the configuration versus perfor-mance standpoint. We demonstrate that for the best performance, workload-dependent parameter tuning is fundamental. Specifically, even a slight misconfiguration can also lead to 110% higher tail latency with respect to its best-case scenario. Our results call for a new set of process scheduling schemes that are workload-aware.
CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation Systems

In this modern era, the popularity of autonomous systems has increased manifold because they are replacing people in various jobs and are expected to become the backbone of modern society. Specifically, self-driving vehicles and robots are gaining popularity in a wide range of applications. However, traffic sign detection, a critical component of intelligent transportation systems, remains quite challenging since it needs to be done quickly, with high precision, and with high dependability. A fast, real-time, robust automatic traffic sign detection and recognition can support and relieve the driver and significantly increase driving safety and comfort. Out of many algorithms and frameworks available for traffic sign identification, i.e., object detection, one of the most popular ones is You Only Look Once (YOLO) since it provides accurate results with minimal background errors in most real-time processing tasks and has excellent learning capabilities. Motivated by this, this research article provides a novel framework, CLEAR, for traffic sign identification in adverse climate conditions using YOLOv5. We compare different models’ speed, accuracy, and other metrics on a Traffic Sign Dataset obtained from the Open Image Dataset V6. Experimental results demonstrated that the proposed CLEAR model achieved the best performance with a Mean Average Precision of 0.73392 and Recall of 0.74194 compared to the existing schemes.
Geofence-Based Service Discovery in the Computing Continuum

Service discovery is a vital process that enables low latency provisioning of Internet of Things applications across the computing continuum. Unfortunately, due to the high heterogeneity of the computing continuum, it becomes increasingly difficult to identify a proper service within strict time constraints. To address these issues, in the paper, we introduce a mobile edge service discovery approach named MESDD. The MESDD service discovery algorithm utilizes the notion of intermediate code to identify the best suitable instance of a service across the computing continuum. The core function of this approach is the selection of the most siutable location descriptor based on the naming scheme used to identify the users’ location. To aid this process, the MESDD approach utilizes geofences, which enable fine-grained resource discovery.
An inter-cell resource usage analysis of large-scale datacentre trace logs

In recent years, modern cluster management systems are facing large volumes of workloads with complex queries. Smart processing of such workloads is essential, which requires a comprehensive understanding of large-scare data centres and the way workloads are processed in them. Understanding of workload and datacentre characteristics are also expected to contribute to the development of prediction models. The recent publication of the 2019 Google trace logs is expected to provide useful insights to researchers of energy efficient datacentres. However, insights from this trace log still remains largely uncovered. This paper presents a comprehensive analysis on the distribution of machine resource utilisation across various cells encompassed in the 2019 Google cluster trace log, uncovering various essential workload behavioural insights such as execution duration, workload task composition, CPU/memory utilisation etc. along with a longitudinal comparative analysis with two other large datasets, namely the 2018 Alibaba and 2011 Google trace logs.
Scaling Data Analysis Services in an Edge-based Federated Learning Environment

Federated Learning represents among the most important techniques used in recent years. It enables the training of Machine Learning-related models without sharing sensitive data. Federated Learning mainly exploits the Edge Computing paradigm for training data acquired from the surrounding environment. The solution proposed in this paper seeks to optimize all the processes involved within a Federated Learning client through transparent scaling across different devices. The proposed architecture and implementation abstracts the Federated Learning client architecture to create a transparent cluster that can optimize the complicated computation and aggregate the data to solve the heterogeneous distribution issue of the data in Federated Learning applications.
Efficiently Improving the Performance of Serverless Applications with Microtask-based Scheduling

Serverless computing promises to make cloud computing cheaper and easier to use. However, serverless platforms use coarse-grained scheduling which decreases efficiency and application performance. We propose a fine-grained application model for serverless applications, and use it to design a scheduler to improve application performance and efficiency. We model serverless applications as being composed of microtasks, each with its own unique resource requirements. Microtasks are easily identified via distinct application phases like initialize, read, and process. We provide evidence for the existence of microtasks by experimentally evaluating a serverless online game. We design a scheduler that separates microtasks with different CPU requirements into different queues so that the appropriate amount of CPU cores could be allocated to each queue based on the CPU requirements of the microtasks in that queue. We implement and evaluate the design in an application-level proofof-concept microtask-based scheduler and compare it to taskbased scheduling commonly used by serverless platforms. For a distributed sort application, the microtask-based scheduler decreases application makespan by 37% and the duration of I/O based application stages by 81%, compared to task-based scheduling. Our work suggests that there is potential in extracting and using microtask information from serverless applications.
Cloud Auto-scaling Auditing Approach using Blockchain

Auto-scaling mechanisms are frequently activated when deploying applications in the cloud environment. They are vital to ensure the application is capable of maintaining the requisite Quality of Service. The auto-scaling tools used depend heavily on the performance indicators provided via monitoring tools. Currently, the majority of the monitoring solutions available are constructed by cloud service providers. Potential therefore exists for cloud providers’ non-compliance with the defined autoscaling configurations and dishonest behaviour. Current practice therefore requires a level of trust that the cloud provider will behave in a trustworthy manner. This paper proposes an autoscaling verification mechanism based on blockchain technology to verify resource scaling decisions made by an obligated service provider. We employed a permissioned blockchain network, Hyperledger Fabric, to evaluate the performance of the proposed system as regards transaction throughput, transaction average latency, transaction success and/or failure and transaction send rate.
Performance Impact of NVMe-Over-TCP on HDFS Workloads

Storage is one of the important components in datacenters. As the data volume rises and the service scale grows, some workloads like database demand increasing amount of storage. While a single server can only host a limited number of disks, distributed file systems (e.g., Hadoop Distributed File System referred to as HDFS) enable accessing disks mounted on the other servers in the cluster, satisfying the storage requirements. On the other side, NVMe-over-Fabric protocols (e.g., NVMe-over-TCP) have been released as a solution on the device level to provide access to remote NVMe disks. Therefore, for those applications developed on top of HDFS, there are at least two choices to make use of the storage resources distributed in datacenters. A concern is whether NVMe-over-TCP will hurt the performance. The evaluation in this paper reveals that the performance degradation NVMe-over-TCP caused on HDFS-based workloads is limited, suggesting NVMe-over-TCP a performant and economical solution in datacenter design to support workloads that needs a lot of storage (such as database applications).