arXiv subjects

Maad Ebrahim

Publications and source records attributed to Maad Ebrahim.

7 recordsLinked to original sources

arXiv2024

Predicting Market Trends with Enhanced Technical Indicator Integration and Classification Models

Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a classification-based machine learning model to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and the Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model's performance, and the results show a buy/sell signal accuracy of over 92\%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market.

cs.LG↗

arXiv2024

Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators

The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions.

cs.LG↗

arXiv2024

Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload.

cs.AI↗

arXiv2023

Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach

Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and enhance the quality of service for end-users. In this work, we improve the performance of privacy-aware Reinforcement Learning (RL) agents that optimize the execution delay of IoT applications by minimizing the waiting delay. To maintain privacy, these agents optimize the waiting delay by minimizing the change in the number of queued requests in the whole system, i.e., without explicitly observing the actual number of requests that are queued in each Fog node nor observing the compute resource capabilities of those nodes. Besides improving the performance of these agents, we propose in this paper a lifelong learning framework for these agents, where lightweight inference models are used during deployment to minimize action delay and only retrained in case of significant environmental changes. To improve the performance, minimize the training cost, and adapt the agents to those changes, we explore the application of Transfer Learning (TL). TL transfers the knowledge acquired from a source domain and applies it to a target domain, enabling the reuse of learned policies and experiences. TL can be also used to pre-train the agent in simulation before fine-tuning it in the real environment; this significantly reduces failure probability compared to learning from scratch in the real environment. To our knowledge, there are no existing efforts in the literature that use TL to address lifelong learning for RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions in Fog systems.

cs.LG↗

arXiv2023

Privacy-Aware Load Balancing in Fog Networks: A Reinforcement Learning Approach

In this paper, we propose a load balancing algorithm based on Reinforcement Learning (RL) to optimize the performance of Fog Computing for real-time IoT applications. The algorithm aims to minimize the waiting delay of IoT workloads in dynamic environments with unpredictable traffic demands, using intelligent workload distribution. Unlike previous studies, our solution does not require load and resource information from Fog nodes to preserve the privacy of service providers, who may wish to hide such information to prevent competitors from calculating better pricing strategies. The proposed algorithm is evaluated on a Discrete-event Simulator (DES) to mimic practical deployment in real environments, and its generalization ability is tested on simulations longer than what it was trained on. Our results show that our proposed approach outperforms baseline load balancing methods under different workload generation rates, while ensuring the privacy of Fog service providers. Furthermore, the environment representation we proposed for the RL agent demonstrates better performance compared to the commonly used representations for RL solutions in the literature, which compromise privacy.

cs.DC↗

arXiv2022

Resilience and Load Balancing in Fog Networks: A Multi-Criteria Decision Analysis Approach

The advent of Cloud Computing enabled the proliferation of IoT applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to serve distributed IoT application workflows using the concept of stateless micro Fog service replicas, which provides resiliency and maintains service availability in the face of failures. Load balancing supports this collaboration by optimally assigning workloads to appropriate services, i.e., distributing the load among Fog nodes to fairly utilize compute and network resources and minimize execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria Decision Analysis (MCDA) approach, to efficiently balance the load in Fog environments. We considered multiple objectives to make service selection decisions, including compute and network load information. We evaluate our approach in a realistic unbalanced topological setup with heterogeneous workload requirements. To the best of our knowledge, this is the first time ELECTRE-based methods are used to balance the load in Fog environments. Through simulations, we compared the performance of our proposed approach with traditional baseline methods that are commonly used in practice, namely random, Round-Robin, nearest node, and fastest service selection algorithms. In terms of the overall system performance, our approach outperforms these methods with up to 67% improvement.

cs.DC↗

arXiv2022

Blockchain as privacy and security solution for smart environments: A Survey

Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, its benefits are far beyond that. It supports technologies like the Internet-of-Things (IoT) to pave the way for futuristic smart environments, like smart homes, smart transportation, smart energy trading, smart industries, smart supply chains, and more. To enable these environments, IoT devices, machines, appliances, and vehicles, need to intercommunicate without the need for centralized trusted parties. Blockchain replaces these trusted parties in such trustless environments. It provides security enforcement, privacy assurance, authentication, and other key features to IoT ecosystems. Besides IoT-Blockchain integration, other technologies add more benefits that attract the research community. Software-Defined Networking (SDN), Fog, Edge, and Cloud Computing technologies, for example, play a key role in enabling realistic IoT applications. Moreover, the integration of Artificial Intelligence (AI) provides smart, dynamic, and autonomous decision-making capabilities for IoT devices in smart environments. To push the research further in this domain, we provide in this paper a comprehensive survey that includes state-of-the-art technological integration, challenges, and solutions for smart environments, and the role of these technologies as the building blocks of such smart environments. We also demonstrate how the level of integration between these technologies has increased over the years, which brings us closer to the futuristic view of smart environments. We further discuss the current need to provide general-purpose Blockchain platforms that can adapt to unique design requirements of different applications and solutions. Finally, we provide a simplified architecture of futuristic smart environments that integrate these technologies, showing the advantage of such integration.

cs.CR↗