ABSTRACT With the increase in resource heterogeneity and dynamic workloads, cloud environments have become more complex, making it challenging to efficiently manage diverse resources. Limited availability and improper allocation often result in task failures, particularly when systems lack the capacity to meet strict deadlines. To address this challenge, effective task allocation algorithms with integrated fault tolerance are essential. This paper presents DTRM that is, Deadline Constrained Task Allocation using Resubmission and preemptive Migration, a novel task allocation algorithm for heterogeneous cloud environments, designed specifically for deadline‐constrained independent tasks. Unlike existing approaches, DTRM introduces a hybrid fault‐tolerant strategy that combines reactive resubmission and proactive preemptive migration, thereby minimizing task failures under deadline constraints. The algorithm operates in three phases: (i) classification of tasks and VMs according to task length and computation power, (ii) prioritization of tasks based on execution time and deadlines, and (iii) allocation to the most suitable VM category. By aligning task requirements with VM capabilities, DTRM achieves better utilization and reduces task rejection. Simulation results on varying task sizes and VM counts demonstrate that DTRM consistently outperforms baseline algorithms such as FCFS, SJF, Priority‐based scheduling, DyMaxMin, RADL, and FTTA. Specifically, DTRM significantly improves makespan reduction, task acceptance, speedup, and efficiency, validating its potential for real‐world deployment in large‐scale cloud environments where meeting deadlines is critical.

Summary In distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault‐tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi‐agent system that consists of multiple hosts. Each host in a multi‐agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault‐tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT‐SHE, LBTS, DEPSPACE, and E‐DEPSPACE.

Abstract Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

Summary Many real‐world application datasets such as medical diagnostics, fraud detection, biological classification, risk analysis and so forth are facing class imbalance and overlapping problems. It seriously affects the learning of the classification model on these datasets because minority instances are not visible to the learner in the overlapped region and the performance of learners is biased towards the majority. Undersampling‐based methods are the most commonly used techniques to handle the above‐mentioned problems. The major problem with these methods is excessive elimination and information loss, that is, unable to retain potential informative majority instances. We propose a novel entropy and neighborhood‐based undersampling (ENU) that removed only those majority instances from the overlapped region which are having less informativeness (entropy) score than the threshold entropy. Most of such existing methods improved sensitivity scores significantly but not in many other performance contexts. ENU first computes entropy and threshold score for majority instances and, a local density‐based improved KNN search is used to identify overlapped majority instances. To tackle the problem effectively ENU defined four improved KNN‐based procedures (ENUB, ENUT, ENUC, and ENUR) for effective undersampling. ENU outperformed in sensitivity, G‐mean, and F1‐score average ranking with reduced information loss as compared to the existing state‐of‐the‐art methods.

Abstract Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.

Abstract The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.

In recent years, several applications of Internet of Things (IoT) have been observed in various areas including environmental monitoring, healthcare systems, cognitive smart agriculture, industrial control, smart homes , intelligent transportation systems , and traffic management. For such applications, wireless sensor networks (WSNs) are generally deployed to gather the sensed data from the targeted application field. In order to transfer the sensor node data to the gateway (sink node), novel routing protocols need to be developed, leading to reduced data transmission delay, high data throughput , and improved energy efficiency across the network. In this context, geographical routing protocol has been considered as a promising approach for the path selection in WSNs. This approach is full of scalability and multi-hop routing is performed using local decisions. However, geographical routing protocols suffer from the void node problem (VNP) i.e., a region where active nodes are not available in the direction closer to the destination. Numerous protocols have been designed to get recovery from VNP in 2D networks which cannot be directly applied to 3D networks. The 3D routing includes the networks deployed in the hilly area, high buildings, airborne region, underground, underwater and so forth. On applying the 2D routing protocols on complex 3D topology, the network may face additional problems like packet looping, routing failure, ambiguity, or increased data latency due to longer path. Further, the majority of geographical routing protocols follow the boundary of void which leads to a longer path. In order to address the aforementioned challenges, this paper presents a novel anchor-based void detouring routing (AVDR) protocol where anchor node is treated as a sub-destination which provides the direct smaller path between source and gateway nodes. The proposed method bypasses the void boundaries and directly connects source to anchor, anchor to destination, or two successive anchors. Further, anchor information is distributed to the desired region to reduce the periodic anchor advertisement process. The effectiveness of the proposed method has been tested over both, real field data set and simulated testbed with OMNET++ simulator. The results obtained over real field data set claim that the proposed method takes only 29.09 ms (ms) for transferring the data on an average. However, this value is 32.37 ms, 34.32 ms, 33.61 ms, 37.20 ms, and 38.73 ms, respectively, using A3DR, EDGR, GPSR-3D, BSMH, and RPL methods. Moreover, it is also noted that the proposed method achieves an improvement of 8.2%, 7.54%, 7.66%, 8.49%, and 8.22%, in routing stretch when compared to aforementioned methods, respectively. This improvement with respect to network overhead is 30.25%, 57.45%, 51.05%, 75.56%, and 58.89% using the proposed method.

p ‐cycles have been widely investigated for survivability of wave division multiplexing (WDM) optical networks. It combines fast recovery feature of ring and spare capacity efficiency of mesh. However, it provides a longer restoration path (RP) which increases the transmission time and causes other impairments. Researchers in this field have mentioned that the loopbacks are the main reason behind the longer length of RPs. Two predominant algorithms have been reported in the literature for the removal of loopbacks (RLB). Both algorithms determine the loopbacks by the route information dynamically aggregated at incident nodes of the failed span. Dynamically acquiring route information of affected working paths and candidate p ‐cycles causes communication and node processing overhead. In this study, we have conducted an analytical study to ascertain the node which forms the longest loopback in RP. Further, an efficient algorithm has been proposed for RLB directly without gathering the route information and tedious loopback calculation at the end nodes of failed span. The simulation result shows that the proposed approach substantially reduces RPs length and promptly removes the loopbacks. Further, it has reduced communication and other overheads to a great extent.