659 research outputs found
URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud
The low cost and rapid provisioning capabilities have made the cloud a
desirable platform to launch complex scientific applications. However, resource
utilization optimization is a significant challenge for cloud service
providers, since the earlier focus is provided on optimizing resources for the
applications that run on the cloud, with a low emphasis being provided on
optimizing resource utilization of the cloud computing internal processes. Code
refactoring has been associated with improving the maintenance and
understanding of software code. However, analyzing the impact of the
refactoring source code of the cloud and studying its impact on cloud resource
usage require further analysis. In this paper, we propose a framework called
Unified Regression Modelling (URegM) which predicts the impact of code smell
refactoring on cloud resource usage. We test our experiments in a real-life
cloud environment using a complex scientific application as a workload. Results
show that URegM is capable of accurately predicting resource consumption due to
code smell refactoring. This will permit cloud service providers with advanced
knowledge about the impact of refactoring code smells on resource consumption,
thus allowing them to plan their resource provisioning and code refactoring
more effectively
An Approach for Optimizing Resource Allocation and Usage in Cloud Computing Systems by Predicting Traffic Flow
The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation
Data-Driven Methods for Data Center Operations Support
During the last decade, cloud technologies have been evolving at
an impressive pace, such that we are now living in a cloud-native
era where developers can leverage on an unprecedented landscape
of (possibly managed) services for orchestration, compute, storage,
load-balancing, monitoring, etc. The possibility to have on-demand
access to a diverse set of configurable virtualized resources allows
for building more elastic, flexible and highly-resilient distributed
applications. Behind the scenes, cloud providers sustain the heavy
burden of maintaining the underlying infrastructures, consisting in
large-scale distributed systems, partitioned and replicated among
many geographically dislocated data centers to guarantee scalability,
robustness to failures, high availability and low latency. The larger the
scale, the more cloud providers have to deal with complex interactions
among the various components, such that monitoring, diagnosing and
troubleshooting issues become incredibly daunting tasks.
To keep up with these challenges, development and operations
practices have undergone significant transformations, especially in
terms of improving the automations that make releasing new software,
and responding to unforeseen issues, faster and sustainable at scale.
The resulting paradigm is nowadays referred to as DevOps. However,
while such automations can be very sophisticated, traditional DevOps
practices fundamentally rely on reactive mechanisms, that typically
require careful manual tuning and supervision from human experts.
To minimize the risk of outages—and the related costs—it is crucial to
provide DevOps teams with suitable tools that can enable a proactive
approach to data center operations.
This work presents a comprehensive data-driven framework to address
the most relevant problems that can be experienced in large-scale
distributed cloud infrastructures. These environments are indeed characterized
by a very large availability of diverse data, collected at each
level of the stack, such as: time-series (e.g., physical host measurements,
virtual machine or container metrics, networking components
logs, application KPIs); graphs (e.g., network topologies, fault graphs
reporting dependencies among hardware and software components,
performance issues propagation networks); and text (e.g., source code,
system logs, version control system history, code review feedbacks).
Such data are also typically updated with relatively high frequency,
and subject to distribution drifts caused by continuous configuration
changes to the underlying infrastructure. In such a highly dynamic scenario,
traditional model-driven approaches alone may be inadequate
at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust
data-driven methods to support their decisions based on historical
information. For instance, effective anomaly detection capabilities may
also help in conducting more precise and efficient root-cause analysis.
Also, leveraging on accurate forecasting and intelligent control
strategies would improve resource management.
Given their ability to deal with high-dimensional, complex data,
Deep Learning-based methods are the most straightforward option for
the realization of the aforementioned support tools. On the other hand,
because of their complexity, this kind of models often requires huge
processing power, and suitable hardware, to be operated effectively
at scale. These aspects must be carefully addressed when applying
such methods in the context of data center operations. Automated
operations approaches must be dependable and cost-efficient, not to
degrade the services they are built to improve.
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Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
Predicting crime using machine learning and deep learning techniques has
gained considerable attention from researchers in recent years, focusing on
identifying patterns and trends in crime occurrences. This review paper
examines over 150 articles to explore the various machine learning and deep
learning algorithms applied to predict crime. The study provides access to the
datasets used for crime prediction by researchers and analyzes prominent
approaches applied in machine learning and deep learning algorithms to predict
crime, offering insights into different trends and factors related to criminal
activities. Additionally, the paper highlights potential gaps and future
directions that can enhance the accuracy of crime prediction. Finally, the
comprehensive overview of research discussed in this paper on crime prediction
using machine learning and deep learning approaches serves as a valuable
reference for researchers in this field. By gaining a deeper understanding of
crime prediction techniques, law enforcement agencies can develop strategies to
prevent and respond to criminal activities more effectively.Comment: 35 Pages, 6 tables and 11 figures. Consists of Dataset links used for
crime prediction. Review Pape
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
Smart buildings are increasingly using Internet of Things (IoT)-based
wireless sensing systems to reduce their energy consumption and environmental
impact. As a result of their compact size and ability to sense, measure, and
compute all electrical properties, Internet of Things devices have become
increasingly important in our society. A major contribution of this study is
the development of a comprehensive IoT-based framework for smart city energy
management, incorporating multiple components of IoT architecture and
framework. An IoT framework for intelligent energy management applications that
employ intelligent analysis is an essential system component that collects and
stores information. Additionally, it serves as a platform for the development
of applications by other companies. Furthermore, we have studied intelligent
energy management solutions based on intelligent mechanisms. The depletion of
energy resources and the increase in energy demand have led to an increase in
energy consumption and building maintenance. The data collected is used to
monitor, control, and enhance the efficiency of the system
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
Explainable Artificial Intelligence (XAI) is transforming the field of
Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
As the number of connected devices keeps on growing, the Internet of Things
(IoT) market needs to be trustworthy for the end-users. However, existing
literature still lacks a systematic and comprehensive survey work on the use of
XAI for IoT. To bridge this lacking, in this paper, we address the XAI
frameworks with a focus on their characteristics and support for IoT. We
illustrate the widely-used XAI services for IoT applications, such as security
enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and
Internet of City Things (IoCT). We also suggest the implementation choice of
XAI models over IoT systems in these applications with appropriate examples and
summarize the key inferences for future works. Moreover, we present the
cutting-edge development in edge XAI structures and the support of
sixth-generation (6G) communication services for IoT applications, along with
key inferences. In a nutshell, this paper constitutes the first holistic
compilation on the development of XAI-based frameworks tailored for the demands
of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the
Communications Society (2022
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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