Achieving dependability in mission-critical operating systems through adaptability and large-scale functional integration

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.

Resource provisioning and scheduling are essential challenges in handling multiple workflow requests within cloud environments, particularly given the constraints imposed by limited resource availability. Although workflow scheduling has been extensively studied, few methods effectively integrate resource provisioning with scheduling, especially under cloud resource limitations and the complexities of multiple workflows. To address this challenge, we propose an innovative two-stage genetic-based optimization approach. In the first stage, candidate cloud resources are selected for the resource pool under the given resource constraints. In the second stage, these resources are provisioned and task scheduling is optimized on the selected resources. A key advantage of our approach is that it reduces the search space in the first stage through a novel encoding scheme that enables a caching strategy, in which intermediate results are stored and reused to enhance optimization efficiency in the second stage. The proposed solution is evaluated through extensive simulation experiments, assessing both resource selection and task scheduling across a diverse range of workflows. The results demonstrate that the proposed approach outperforms existing algorithms, particularly for highly parallel workflows, highlighting its effectiveness in managing complex workflow scheduling under resource-constrained cloud environments.

Scheduling of task and resources using an efficient task scheduling algorithm improvise the efficiency and throughput of the cloud server. Scheduling in cloud is based on selection of best suitable resources for task execution, by considering various QoS parameters. Priority based task scheduling by mapping conflict-free resources and workload in cloud computing environment is the proposed scheduling method in this paper. This will result in conflict-free resource allocation to the task based on priority and a balanced workload scheduling. In this technique task scheduler and resource scheduler will work co-ordinately to achieve the desired throughput with the minimum response time. The main objective of this paper is to have maximum utilization on client and server side accessing the cloud environment.

In cloud computing, task scheduling is the primary consideration when assigning resources dynamically to minimize makespan and increase performance. Due to the fact that data centers house a large number of computers, the rapid growth of cloud computing leads in massive energy consumption and emissions of carbon dioxide. Thus, in order to lower energy usage and carbon emissions, cloud service providers are searching for environmentally friendly solutions. Task scheduling has gained popularity as a result, where minimal energy usage and effective resource utilization are given careful thought. Although there are many applications and domains where cloud computing is widely employed, task and resource scheduling is still something that has to be improved. To lower costs, makespan, and energy consumption, this study presents the Cost and Energy-aware Task Scheduling Algorithm (CETSA). The suggested approach takes into account the load on each virtual machine as well as the trade-off between cost, energy consumption, and makespan in order to prevent virtual machines from overloading. The recommended method is put into practice with the help of the Cloud Sim toolkit and evaluated for various workloads. In addition, the recommended method lowers the rate of work failure, outperforms the prior algorithms in terms of energy consumption, and strikes a reasonable compromise between the makespan and the overall execution cost. Ultimately, the experimental findings demonstrate that the CETSA algorithm outperforms other algorithms in terms of energy usage.

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.

Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.

Cloud computing has attracted both end-users and cloud service providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining quality of service (QoS) is one key challenge faced by CSPs with warehouse-scale datacenters. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud, a Hierarchical and Hybrid Online task scheduling framework for warehouse-scale Cloud service providers, to improve resource usage effectiveness while maintaining QoS. H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud is highly scalable and considers comprehensive information, such as various workload scenarios, cloud platform configurations, user request information, and dynamic pricing model. The hierarchy and hybridity of the framework, combined with its deep reinforcement learning (DRL) engines, enable H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud to efficiently start on-the-go scheduling and learning in an unpredictable environment without pretraining. Our experiments confirm the high efficiency of the proposed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud when compared to baseline approaches, in terms of energy and cost while maintaining QoS. Compared with a state-of-the-art DRL-based algorithm, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud achieves up to 201.17% energy cost efficiency improvement, 47.88% energy efficiency improvement, and 551.76% reward rate improvement.

The creation of virtual machines (VMs) is one of the procedures of resource scheduling which is the key technology in cloud computing systems. Currently it is rarely studied independently, and it is always set as a static model where the number and the type of VMs are predefined before scheduling. However, with the static model, it is difficult to consider the overall optimization for the scheduling and it is not user-friendly because of users' imprecise requirements. Therefore, in this paper a dynamic model for VMs' creation is proposed. With this model, users only submit fuzzy requirements for computing resources, while the necessary number and type of VMs are calculated according to the current environment and dynamically created for further scheduling. The model is implemented in CloudSim, where the key technologies are presented. Analysis and experiments were carried out to verify the extension of the model in a more wide research area, such as resource scheduling for multi-objective optimization and scheduling tasks with priorities.

The cyber-physical system is highly desirable for real-time perception and dynamic control, especially in task scheduling and network communication for large and complex engineering systems. In most of the applications, the resource scheduling of cyber and physical network are independent on each other, which may cause severe deterioration of the network performance. To implement the cognitive cooperation between the upper layer of industrial services and the underlying network, we propose a cooptimization method in this article. We adopt the software defined networking technology to extract the network control logic, which is integrated with the upper-level task scheduling. This cooptimization scheme can dynamically calculate optimal end-to-end virtual paths over the underlying network infrastructures. Meanwhile, it can also adjust the task scheduling of the upper-level network by signals derived from the underlying network. Simulation results show that our proposed scheme outperforms the traditional method in terms of task acceptance rate, average end-to-end delay, and network load balance degree.

PurposeIn recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.Design/methodology/approachIn this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.FindingsThe result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.Originality/valueThe authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

Waste problems often occur in big cities such as Jakarta, Surabaya, Medan, Bandung, Yogyakarta and Semarang. Urban waste is waste produced by human activities in urban areas, including various types of materials such as plastic, paper, metal and organic materials. Therefore, waste needs to be collected, transported and disposed of immediately to prevent negative impacts on the environment. With the increase in population in urban areas, it is likely that the amount of waste will also increase. The solution that can be used to solve this problem is to optimize the distance using a dynamic programming model forward recursive equation algorithm. The application of dynamic programming can be used to solve various problems such as determining the shortest distance, optimal flow, task or resource scheduling, production planning, network optimization, and problems in research and development projects. Based on determining the optimal route for transporting waste in Medan Johor District using the dynamic programming algorithm forward recursive equation model, the total distance is 26,2km.

Joint operations include situational awareness, information dissemination, command and control, etc. Resource scheduling is an important link to realize command and control and promote the combat process. The battle process will lead to the change of initial platform resources. Task scheduling cannot be carried out smoothly unless it has to be adjusted. In this paper, GA nested multi-dimensional dynamic list (MDLS) algorithm is used to model, and the platform of resource change is added and simulated. To provide a certain basis and reference for the construction of dynamic resource scheduling system in future joint operations.

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Pricing and prioritizing the Internet access can be one of the potential measures to improve the efficiency of the Internet utilization and control the Internet congestion. In order to meet a diversity of user access requirements, the IP (Internet Protocol) routers can provide users with various priority service classes. This paper explores the application of multi-agent systems (MAS) in the implementation of rational IP routers on the priced Internet. The negotiation model for task decomposition and allocation, and the payment model are described. The strategies of achieving a more optimal global-level resource allocation and a rational and fair local-level resource allocation and scheduling for tasks with different priority are proposed. The strategies for price setting and adjustment are discussed. The consumer strategy is also addressed.