The rapid expansion of cloud computing has made it increasingly difficult for users to determine the most appropriate cloud service provider (CSP). The provider offers diverse services, typically assessed based on quality of service (QoS) attributes, including throughput, reliability, availability, latency, and response time. Researchers often present these QoS attributes in a decision matrix and apply multi-attribute decision-making (MADM) algorithms to evaluate and rank the CSPs. However, in practical scenarios, not all CSPs satisfy every QoS attribute, leading to unavailable performance measure values in a decision matrix. To address this challenge, we develop a hybrid MADM framework for CSP selection that handles an incomplete decision matrix. The framework integrates QoS-aware MADM (QMADM) algorithms, QTOPSIS-W and QVIKOR-W with attribute weights (QMADM-W). It employs three imputation techniques to determine unavailable performance values: minimum (min), maximum (max), and mean. The weights are derived using the analytic hierarchy process (AHP) and the analytic network process (ANP). Simulation results using the QoS for web services (QWS) dataset demonstrate the framework's effectiveness in QTPOSIS-W, with consistent and robust performance observed under the mean imputation technique through sensitivity analysis. The proposed algorithms offer a reliable solution for selecting an optimal CSP, even for an incomplete decision matrix.

Large language models (LLMs) have gained enormous popularity for processing and generating text. They are a subset of generative artificial intelligence (GenAI) that require higher availability of graphical processing unit (GPU) resources for inference services. However, making GPU resources available in a centralized infrastructure is quite challenging. Therefore, recent works have focused on decentralized physical infrastructure networks (DePIN) to utilize idle GPU resources, enabling scalable LLM inference services across the decentralized network. These inference services may experience inherent latency (i.e., measured in time per output token (TPOT)) due to communication overhead or time between GPU resources responsible for generating consecutive tokens. The task scheduling algorithm is crucial in decentralized LLM inference services to minimize TPOT and maximize GPU resource utilization, particularly when GPU resources are constrained by computational capacity. This paper introduces two task scheduling algorithms, the improved greedy heuristic shortest path algorithm (IGHSPA) and the dynamic programming-based task scheduling algorithm (DPTSA), for decentralized LLM serving to achieve these objectives. Each task involves assigning a layer to a GPU resource, which IGHSPA and DPTSA accomplish using greedy heuristic and dynamic programming. Both algorithms are extensively simulated and compared with one of the recent algorithms, namely the greedy heuristic shortest path algorithm (GHSPA), in terms of TPOT and execution time (ET). Our simulation results demonstrate that DPTSA improves TPOT up to 47.50% and 35.50% and ET up to 99.95% and 35.00%, compared to GHSPA and IGHSPA.

Cloud computing enables developers to deploy and host applications without focusing on installing and maintaining the infrastructure. The developers can utilize the services provided by the cloud service providers (CSPs) to offer scalable solutions to customer applications. As a result, CSPs are deluged with different batches of cloudlets (tasks) from diverse customer applications. Therefore, developing an algorithm that selects and processes applications intelligently to minimize the execution time and maximize the throughput becomes challenging. Many researchers have shown the round-robin (RR) scheduling algorithm variants to tackle this problem. One such variant is the dynamic RR heuristic algorithm (DRRHA) that utilizes the mean of the burst times (BTs) of cloudlets in the ready queue (RQ) to calculate the time quantum (TQ). However, DRRHA has not considered skewness. This paper introduces a novel skewness-based RR algorithm (SRRA) for cloudlet scheduling. The algorithm dynamically determines the TQ for each cloudlet based on the skewness of the BTs of cloudlets in the RQ. The algorithm has two variants: SRRA with minimum TQ (SRRA-Min) and SRRA with median TQ (SRRA-Med). The two variants of the proposed algorithm exhibit improved performance in terms of total execution time (TET) and throughput compared to DRRHA, individually and collectively. These comparisons are conducted using CloudSim Plus under two scenarios: constant skewness with varying cloudlets and constant cloudlets with varying skewness.

Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system.However, its computational requirements rise with the growth of distributed generations in the interconnected power system.As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation.Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis.This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs).To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies.We perform extensive experiments to evaluate the efficacy of the proposed algorithm.As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms.Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N-1 and N-2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks.

Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.

Vehicular clouds (VCs) have become a promising research area due to its on-demand solutions, resource pooling, unified services, autonomous cloud formation and transformational management. It makes use of the underutilized resources of vehicles on the parking lot, roadways, driveways and streets, and creates the infrastructure to support various services offered by the cloud service provider (CSP) by deploying virtual machines (VMs). However, these vehicles can leave the coverage/grid of VC due to its mobility and change in the environment. Therefore, the hosted VMs on those vehicles can be transferred to other potential vehicles (i.e., migration) in order to avoid disruption of services. These services can be viewed as user requests (URs) submitted to the CSP by cloud users. Here, the challenging tasks are to map the URs to the VMs (or vehicles) and identify the potential vehicles for migration, and they need immediate attention. In this paper, we propose a smart cloud service management (SCSM) algorithm for VCs and address the above challenges. This algorithm consists of three phases, namely assignment of vehicles to grids, URs to grids and URs to vehicles by considering the mobility pattern of vehicles. The performance of SCSM is assessed using three traffic congestion scenarios and thirty-six instances of four datasets, and compared with round-robin (RR) and deficit weighted RR (DWRR) using seven performance metrics. The comparison results show that SCSM achieves 58% and 57% (33% and 33%) better than RR and DWRR in makespan (number of migrations) and other performance metrics.

In the above article[1], the corresponding author was incorrectly identified. The corresponding author is the first author, S. Nithya.

The cloudlets can be deployed over mobile devices or even fixed state powerful servers that can provide services to its users in physical proximity. Executing workloads on cloudlets involves challenges centering on limited computing resources. Executing Virtual Machine (VM) based workloads for cloudlets does not scale due to the high computational demands of a VM. Another approach is to execute container-based workloads on cloudlets. However, container-based methods suffer from the cold-start problem, making it unfit for mobile edge computing scenarios. In this work, we introduce executing serverless functions on Web-assembly as workloads for both mobile and fixed state cloudlets. To execute the serverless workload on mobile cloudlets, we built a lightweight Web-assembly runtime. The orchestration of workloads and management of cloudlets or serverless runtime is done by introducing software-defined Cyber Foraging (SDCF) framework, which is a hybrid controller including a control plane for local networks and cloudlets. The SDCF framework integrates the management of cloudlets by utilizing the control plane traffic of the underlying network and thus avoids the extra overhead of cloudlet control plane traffic management. We evaluate SDCF using three use cases: (1) Price aware resource allocation (2) Energy aware resource scheduling for mobile cloudlets (3) Mobility pattern aware resource scheduling in mobile cloudlets. Through the virtualization of cloudlet resources, SDCF preserves minimal maintenance property by providing a centralized approach for configuring and management of cloudlets.