A Lightweight Security-Aware Resource Scheduler for Edge Computing Environments

Deep learning (DL) is gaining popularity in various fields, such as computer vision, speech identification, and self-driving vehicles. Blackwell graphical processing units (GPUs) and Grace central processing units (CPUs) are widely utilized in computing platforms, including portable gadgets and supercomputers. Optimizing performance for multiple-programming workloads on such devices is crucial, as a single application cannot typically utilize all available resources. Developing effective GPU resource administration and scheduling systems is crucial for effective distribution among several clients. Nevertheless, current versions have several flaws. Clients often provide wrong task resource requirements, resulting in cluster resource low utilization. DL-based resource scheduling without considering cluster network features leads to poor operational efficiency. To address these difficulties, this study develops a distributed data center (DDC) framework using Blackwell GPUs and Grace CPUs for resource scheduling and accelerated computing with DL. This study offers a regression model-based technique for estimating task resource requirements, preventing clients from overallocating computer resources. This paper demonstrates smart cluster network-effective scheduling approaches for both prompt and batch forms based on the resource need assessment methodologies described. Study findings demonstrate that the suggested system can improve task computation by 19% overall and reduce mean job completion duration (JCD) by 22% when evaluated against modern approaches. Furthermore, the presented optimization approaches are useful in a variety of contexts.

Currently, the scheduling mode used by most TT&C resource scheduling system was based on the mission requirements offered by users. After the scheduling plan is formulated, the resource redundancy has been basically determined, without the advance planning of the resources. This article analyzed the data of the scheduling system, by dividing the requirements of resources into two types of “period” and “peak”. Through analyzing the periodic use of resource historical data, the periodic resource demand will be predicted and guided resources for time-dividing and domain-planning. By analyzing the allocation data of dynamic resource scheduling, the peak resource will be reserved to enable and recycle dynamically in actual. Finally the strategy was verified by simulation, which provided a different approach to improve the utilization rate of the entire network resources.KeywordsData miningPeriodic resourcePeak resource

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

: The Range Commanders Council (RCC), specifically the Data Reduction and Computing Group (DR&CG), recognized the need for an integrated Range Resource Management and Scheduling capability that would facilitate support of all phases of test or training events. Such a tool would span the initial planning phases, the execution and evaluation of an exercise, and finally, the accounting and billing process. Currently, each range not only performs its functions differently, but the tools, systems, and steps followed are also unique to each facility. This study was initiated with the goal of providing the ranges with a document describing the areas to consider when designing and implementing such an integrated tool. A three-step approach was used in the conduct of this study First, documentation was solicited from the different RCC members who represent the DoD ranges. Information was gathered that focused on the functionality and capability of current range resource management and scheduling systems. Interviews with subject matter experts complemented the study when actual written material was not available. Second, the information from each individual range was analyzed, organized, and then compared with the other ranges. Actual requirements were identified from systems documentation or from interviews and generalized in order to present a broader definition of required capabilities. These requirements include the processes, types of data, design documentation, system architecture, and user interfaces in current use. Finally, a recommended approach for use in developing a resource management and scheduling tool was defined that takes into consideration future requirements as well as current systems development and design practices.

In order to resolve resource management and scheduling problem in Manufacturing Grid (MG) – an application of Grid technology, we develop a resource management and scheduling system with the interaction of Manufacturing Grid Information Service (MGIS) and the Manufacturing Grid Resource Scheduler (MGRS). The former, MGIS, provides fundamental mechanisms for remote resource encapsulating, registration, and monitoring, and the latter, MGRS, performs scheduling roles as Global Process Planning (GPP) analyzing, resource discovery, resource selection, and resource mapping.

SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.

Efficient scheduling is crucial for time and cost-effective utilization of compute resources especially for high end systems. A variety of factors need to be considered during the scheduling decisions. Power variation across the compute resources of homogeneous large-scale systems has not been considered so far. This paper discusses the impact of the power variation for parallel application scheduling. It addresses the problem of finding the optimal resource configuration for a given application that will minimize the amount of consumed energy, under pre-defined constraints on application execution time and instantaneous average power consumption. This paper presents an efficient algorithm to do so, which also considers the existing power diversity among the compute nodes (modified also at different operating CPU frequencies) of a given homogeneous High Performance Computing system. Based on this algorithm, the paper presents a plug-in, referred to as Configuration Adviser, which operates on top of a given resource management and scheduling system to advise on energy-wise optimal resource configuration for a given application, execution using which, will adhere to the specified execution time and power consumption constraints. The main goal of this plug-in is to enhance the current resource management and scheduling tools for the support of power capping for future Exascale systems, where a data center might not be able to provide cooling or electrical power for system peak consumption but only for the expected power bands.

Resource scheduling is one of the main problems for multi-satellite Tracking, Telemetry and Command (TT&C) networks. Traditional multi-resource joint scheduling algorithms are with long solution time, low efficiency, high computational cost, and simple description on the system. Deep Neural Network (DNN) provides a possible new way to solve those problems, but it is difficult to handle correlations among the input data. This motivates our work to solve the strong correlation problem based on the accumulated historical data, and thus enables DNN for TT&C resource scheduling. By discretizing the data, multiple constraints and related attributes are transformed into different flags, and some binary bits of the data are used to reflect the constraint relationship. Then, we can use DNN model and construct an intelligent TT&C resource scheduling system to handle multiple constraints and data attributes (such as priorities among tasks and others). This improves the efficiency of TT&C resources utilization and automation. Effectiveness of the proposed model is verified by simulations.

Wireless Sensor Network (WSN) comprises of several heterogeneous sensor nodes such as sound sensor, camera sensor, temperature sensor, proximity sensor, motion sensor, light sensor, color sensor, accelerometer sensor etc. There are various limitations in sensor nodes such as less memory, power and processing capabilities. In today's world, the applications of WSN have immensely grown that includes health care monitoring, disaster monitoring, military applications, smart home monitoring and smart cities etc. In spite of a large number of applications of WSN there are several research challenges with it viz; scalability, power management of nodes, resource scheduling, mobility management and security. With increase in the demand of WSN applications, one cannot always increase the number of deployed sensors to meet the requirements. So, cloud and virtualization can help in this issue. This integration of cloud with WSN is termed as Sensor Cloud. Sensor cloud also has several research challenges and security is one of them. This paper presents a survey which focuses on some of the major security attacks in sensor cloud.

As Grid technologies evolve quickly on Internet, research related to resource scheduling faces new opportunities and challenges. These new technologies, ideas and approaches provide a new environment for researching and developing the economy-based resource scheduling system. Aiming at the hierarchical Grid model, following utility optimization thinking, while an universal flexible utility function is provided for addressing the Grid users’ QoS requirements of deadline and budget, this paper proposes a new Grid resource scheduling algorithm, which can not only increase the utilization of resources and system throughput, but also realize cost-time optimization between resource providers and users within Grid systems. On the basis of having described the different functional parts of the proposed algorithm, two kinds of simulative experiments about completion time and cost of tasks are conducted. Finally, the experiment results show that the proposed algorithm is effective.

This paper reviews the research on Traffic Engineering (TE) based on Software-Defined Networking (SDN). Firstly, the shortcomings of traditional network architectures in managing complex traffic are introduced, and it is pointed out that SDN provides a new solution for traffic engineering through the features of separation of control and data planes, centralized control, and programmability, but at the same time, it faces many challenges. Then the three-layer architecture of SDN and its role in traffic engineering are described, and the goals, research directions and application scenarios of traffic engineering are outlined. The application of machine learning (ML) methods in SDN traffic engineering is then discussed in detail, including the advantages of deep learning (DL) in traffic prediction and classification, and the potential of reinforcement learning (RL) in routing optimization and resource scheduling. In addition, four mechanisms for optimization algorithms to merge with ML are analyzed. The integration of SDN with new architectures such as edge computing and network slicing, as well as the prospects for the application of intent-driven networking are then explored. Finally, the opportunities and challenges of SDN traffic engineering are summarized, including the advantages of flexible traffic management and cross-domain optimization, as well as the problems of network scale, security, real-time and multi-tenant management, which provide directions for future research.

The modern large multi-channel broadcast facility is far more then a multiplicity of traditional single-channel television stations. Business requirements for large DBS or digital cable services demand that such a system manage hundreds of play-out channels, sharing content across output streams, with extraordinary reliability and maximized operational efficiency. Achieving these goals requires an entirely new approach to resource management, automation and scheduling systems. This paper describes a new architecture for the Multichannel Broadcast System, using a shared object-oriented database to support broadcast workflow starting from the acquisition of program content through on-air operation. Automated systems support the tracking of channel personalities, program content, physical media and broadcast resources such as video file server ports, all on a timeline basis. Communication with the many external subsystems required in a DVB environment is fully integrated into the system architecture: Electronic Program Guides, Conditional Access Systems, Transmission Bandwidth control and machine automation are all choreographed for maximum efficiency. This system has now been implemented at two sites worldwide. Examples from the most sophisticated project - the 320 channel ASkyB Broadcast Center in Gilbert Arizona - will be used to illustrate a typical deployment of the System.

The problems of development and construction of a universal consolidated strategy of interaction of IT services and information security, which is based on the study and in-depth analysis of statistics on the current situation with the interaction of corporate IT services and security in different international companies in different industries and countries, are considered. specialists, experts and analysts. The subject of research in the article is the issue of optimal interaction of IT and security services in ensuring a high level of corporate information security in the EMEA region. The goal is to analyze the issues related to the construction and implementation of consolidated strategies for IT and security management in order to eliminate inconsistencies and increase the efficiency of the relevant services to ensure effective information security of organizations on the example of the EMEA region. Objectives: processing and analysis of the results of an online survey on the interaction of IT and security services of companies and organizations in various industries in different countries and regions of the world, including the EMEA region; research of problems and advantages of building a consolidated strategy of IT management and information security in the EMEA region; development of recommendations for solving existing problems in the development and construction of corporate strategies for effective IT management and information security. The general scientific method of systems analysis is used to determine the features of interaction between IT services and security of companies and organizations of different industries in different regions of the world, as well as to study the problems and benefits of a unified consolidated IT management strategy and information security. The following results were obtained: based on quantitative and qualitative assessment, as well as analysis of the results of the survey of IT and security specialists and experts in the EMEA region, the participation of companies in the development and implementation of information security strategies was determined; the main tasks of IT and security services in the organization of their joint work and coordination of actions among themselves are defined; the main obstacles in the organization of joint work of IT services and security services of companies are investigated, among which significant are tensions and conflicts between employees of services, global and regional shortage of qualified personnel, technical difficulties. Conclusions: it is established that the consolidated strategy of IT management and information security based on advances in security and technology can help solve the main problems of effective interaction of relevant company services; developed a number of recommendations to address existing problems of building corporate strategies for effective IT management and information security, designed to help eliminate inconsistencies and improve the efficiency of relevant services to ensure effective information security of organizations in the EMEA in various organizations and companies, regardless of their field of activity.