Cloud computing has become a crucial aspect of Information Technology, offering solutions to many of the challenges faced by internet users. However, the increasing number of users worldwide has led to congestion at certain nodes, resulting in unbalanced loads or hanging systems, commonly known as deadlock. This paper proposes an algorithm using deadlock prediction to enhance the load balancer in Cloud environment. This algorithm leverages Machine Learning and prediction techniques, specifically the Linear Regression Model, to forecast the possibility of deadlock in VMs. By predicting deadlock, the available resources can be allocated to satisfy all requests. The algorithm was deployed in the CloudSim simulation environment, which was integrated with Weka library for the Machine Learning techniques. The results were compared to well-known algorithms such as FCFS, RoundRobin, MaxMin, and MinMin. The evaluation revealed that the proposed algorithm outperformed these popular algorithms, demonstrating its effectiveness in enhancing Cloud computing’s load balancing capabilities. 

Enhancing Load Balancing in Cloud Computing Through Deadlock Prediction

Thanks to the recent breakthroughs in deep learning (DL) methods and ever-increasing computation power, nowadays DL models are being increasingly applied to all kinds of fields. They, along with other software modules, compose complex DL systems such as autonomous driving systems. Unfortunately, fatalities happen to these systems as is reported in real-life situations, e.g., traffic accidents involving autonomous driving vehicles. Further analyses show that this is because DL systems contain defects. To this end, understanding defects in DL systems is critical for preventing such accidents. To help improve the reliability of DL systems, many researchers have devoted efforts to their testing. In this paper, we investigated state-of-the-art methods through in-depth exploration. Firstly, we made a detailed analysis of deep learning system defects. We proposed a defect classification scheme, and summarized the characteristics of defects and their impacts on the system. Secondly, we systematically investigated the detection methods and tools and evaluated their capability in defect detection.

Detecting Defects in Deep Learning Systems: a Survey

A Hybrid Approach for Detecting Bugs in HPC Workloads

This paper presents a simulation model for a preemptive transportation system where vehicles can have tasks dynamically reassigned to optimize overall efficiency.The model, built using Flexsim software, includes 9 storage areas and 8 vehicles.Exponential distributions, validated with real logistics warehouse data, generate stochastic task arrivals.Recursive logic implements the preemption algorithm.Comparative analysis over a 10-hour duration shows that the preemptive mode increases average transport volume by 13 % and reduces vehicle travel distance by 13 % compared to a non-preemptive baseline.The results demonstrate the value of preemptive transportation in enhancing system responsiveness and operational efficiency. 

Simulation and Analysis of a Preemptive Transportation Model Using Flexsim Software

The aim of the study is to compare the deadlock detection algorithms in distributed systems with respective Blockchain technology presented in this paper. Today, detecting deadlocks in distributed systems is a very important challenge. Without the proper deadlock detection mechanism, the system can get stuck in a reject state. The goal of this work is to figure out which algorithm is more suitable for deadlock detection in Blockchain technology based on these factors i-e Security, Time, Performance, Communication, and Correctness. Under the positivism paradigm. The descriptive research design and distributed technique are used to minimize the occurrence of deadlock and to identify the deadlock in parallel collective operation verified up to maximum threads. The algorithms for deadlock detection that are compared in this study are Scalable deadlock detection, Push-relabel algorithm, Knapp’s, Fulgor, and Consensus based on Blockchain technology. Blockchain technology provides a secure environment and helps to build trust. According to this paper, the Fulgor algorithm is more effective at removing deadlocks and maintaining communication security between distributed systems. Both controlled and uncontrolled environments are tested with different algorithms and are compared to define efficiency side-by-side. Deadlock detection requires identifying some properties and message delays in the global state of the distributed system. The number of common parameters has been identified in the selected algorithms to detect deadlocks in a distributed system

Comparative Analysis of Deadlock Detection Algorithm based on Blockchain

Concurrency bugs are difficult to diagnose and fix, due to the nature of the bugs and how they manifest themselves during execution. Traditional approaches for diagnosing concurrency bugs attempt to reproduce the exact execution schedule which reveals the bug, resulting in high runtime overhead. In this paper, we present our work in identifying concurrency bugs using resource consumption footprints. This is based on the observation that resource access and consumption patterns are critical indications of the run-time behavior of concurrent software, and can be used as a powerful mechanism to guide the software debugging process. We demonstrate that monitoring resource footprints at runtime can effectively help detect software bugs. Specifically, for MPI programs, a simple SVM classifier can detect deadlocks with high accuracy using only the CPU usage patterns.

Deadlock Detection for Concurrent Programs Using Resource Footprints

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Deadlock Detection for Concurrent Programs Using Resource Footprints