Dynamic resource management framework for wireless LAN

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.

Effective cloud resource management is essential for modern computing, but traditional rule-based methods struggle to keep up with dynamic workloads. AI approaches like reinforcement learning and predictive analytics show promise but often work in isolation, lacking collaboration and adaptability for comprehensive management. This research introduces Agentic AI, a novel approach that integrates autonomous, goal-driven, and collaborative agents to optimize cloud resource allocation. Through a systematic literature review, the study highlights key gaps in current techniques and proposes a conceptual framework based on Agentic AI principles. The framework enables agents to predict workloads, set system-wide goals, and dynamically allocate resources in real time. By bridging existing gaps, this research offers a unified and adaptable solution to improve scalability, workload distribution, and cost efficiency, advancing intelligent cloud resource management.

Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.

The ever-increasing use of broad-band Internet and complex multimedia applications is pushing fiber closer and closer to the homes. Within the European project IST HARMONICS (Hybrid Access Reconfigurable Multi-wavelength Optical Networks for IP-based Communication Services), an optical access feeder network and resource management framework were developed that tackle this demand for bandwidth and the desire to stimulate the convergence of last-mile access technologies. To cope with the lack of bandwidth in the access and last-mile networks and the different needs of applications and users, the developed management system provides end-to-end quality of service (QoS) while integrating multiple technologies. In this paper, a detailed overview of the end-to-end QoS management framework and novel time slot/wavelength MAC protocol for the optical feeder network is given. End-to-end QoS is based on Differentiated services (DiffServ) at layer 3, various QoS supporting technologies at layer 2, and QoS mappings between both layers. The paper will also focus on the field trial results of the HARMONICS project and give some guidelines for possible problems and solutions in this area.

The next generation wireless networks will be heterogeneous wireless environments because of the coexistence of a large variety of wireless access technologies. The different networks have different architectures and protocols. So it is difficult for a user to roam from one radio system to another which can be solved by using the Internet protocol as a common interconnection protocol as it needs no assumptions about the characteristics of the underlying technologies. An all-IP wireless network is an IP-based wireless access system that makes wireless networks more robust, scalable, and cost effective. The nodes in such a network are mobile nodes as they change their location and point of attachment to the Internet frequently. The mobility management is an important research issue in an all-IP wireless network for providing seamless roaming facility to mobile nodes from one wireless system to another. The dynamic resource management is also required in this environment to ensure sufficient resource in the selected route for transmission or reception of the data packets during seamless roaming of the mobile nodes. This chapter is aimed at the researchers and the policy makers making them aware of the different means of mobility management and resource management for mobile nodes in all-IP wireless networks.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue. The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host. Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.

Ever demanding systems with restricted resources face increasingly complex applications. Additionally, changeable environments modify working conditions over time. Therefore, a dynamic resource management is required in order to provide adaptation capabilities. By using ARTICo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , a bus-based architecture with reconfigurable slots, this adaptation is accomplished in three different but dependent areas: Consumption, Confidentiality and fault tolerance, and Computation. The proposed resource management strategies rely on an architecture and a model of computation that make execution configuration to be application-independent, but context-aware, since a CUDA-like execution model is used. The inherent and explicit application-level parallelism of multithreaded CUDA kernels is used to generate hardware accelerators that act as thread blocks. Despite other modes of operation provided by the ARTICo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> architecture, like module redundancy or dual-rail operation to mitigate Side-Channel Attacks, these thread blocks are dynamically managed and their execution is scheduled using a multiobjective optimization algorithm.

Shifting cloud computing capabilities close to the edge enables provisioning of low latency location‐based internet services that are adapted to user behaviour. However, this can be achieved neither with a simple move of the physical hosts closer to edge networks, nor continuing to abide by the same principles as the ones implemented in traditional cloud computing approaches. In order to accomplish the promised high quality of service, changes must be made to the resource management techniques so that they are adapted to the requirements of fog computing. This paper introduces a novel location‐based handoff management and its corresponding implementation of dynamic resource management modules that introduce resource allocation and migration strategies adapted specifically to fog computing. The aim is the implementation of the follow‐me behaviour of edge resources considering a tightly coupled perspective with the user location. The proposal is based on the concept of mapping physical areas to logical resource communities where the node mobility triggers migrations to the corresponding community. The results analysis from the simulation scenarios shows that the effectiveness of our community‐based dynamic resource management proposal in following the geographical trajectory of mobile users with wearable devices is over 80% even in highly saturated environments. The comparison with traditional resource management techniques clearly presents the advantages of our proposal, while the parameter wise in‐depth analysis discusses dependencies on the number of nodes, speed, and available resources.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth‐generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction‐aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria‐Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Dynamic Resource Management (DRM) allows dynamically changing the resources assigned to a job during its execution. This flexibility can improve productivity in completed jobs per unit of time, resource utilization rate, and energy consumption, among other metrics. Despite its benefits, DRM remains complex to implement, which is one of the reasons it has not yet been widely adopted in production HPC systems. To address this challenge, we design, implement, and evaluate a methodology aimed at improving the programmability of DRM for iterative algorithm-based applications, while maintaining a generic and flexible foundation. Specifically, we interface the Dynamic Management of Resources API (DMR-API), an application-level abstraction layer that simplifies the adoption of dynamic resources—particularly in classical iteration-based HPC applications—with the Dynamic Processes with PSets (DPP) approach, which provides a set of generic design principles for dynamic resource management in high-performance parallel programming models. This integration makes DRM more accessible for iteration-based HPC applications through the DMR-API, while preserving the generality and flexibility of DPP at the system software level to support a wider range of HPC application types. Our results show that DRM can be effectively leveraged in HPC environments with minimal coding effort, unlocking the benefits of dynamic resource allocation for job throughput and system utilization.

A number of concurrent computers have been designed, such as [Ametek 1988] and [NCUBE 1986]. Taking advantage of concurrency on these computers will require new languages, operating systems, and environments to support the programming, monitoring and control of applications. The Rosette project at MCC is developing a system architecture for general-purpose concurrent computation based on the Actor model [Agha 1986]. The aim of the Rosette architecture is to support the use of concurrent computers in solving computationally intensive problems whose structure is not statically determined. The architecture will support variable grain size, dynamic resource management, reflection, and heterogeneity. A number of concurrent language proposals have employed computational models that are limited in their applicability because of assumptions such as granularity of concurrency, static process topology, or inherent communications cost. Others, for example, functional programming, cannot be used to model shared, modifiable objects. The Actor model addresses these issues directly. However, the difficulties of using concurrent computers cannot be addressed solely through programming languages. Traditional operating systems treat each process as an independent entity for the purposes of resource management (i.e., for the run-time allocation of processor, storage, and communication resources.) In the context of concurrent applications, effective operation on a concurrent computer requires that communities of processes be managed together, taking into account the ways in which they interact, and providing for the application itself to guide resource management. For example, consider that programming systems often provide only limited support for the control of resource consumption in search problems in the face of combinatorial explosion; further, it is generally recognized that to optimize search performance, it is useful to vary the resources provided depending on how promising intermediate results of a given path are judged to be. The Rosette architecture will allow the programmer to integrate high-level algorithms within the application for dividing resources dynamically among the sub-computations that are spawned. These algorithms will provide an assessment of the progress of the sub-computations, and will be used to make the large number of low-level resource management decisions that are necessary in carrying out the computation. Thus, operating system functionality will be folded into the application. The Rosette architecture has two major components: an interface layer and a system environment . The interface layer provides mechanisms for the monitoring and control of concurrent applications. It supports a uniform view of the system resources and applications through the use of actors and message-passing semantics. The interface layer includes a set of actors that represent the processing, storage and communication resources across the hardware base. The system environment contains actor communities which implement resource management policies , and support for programming concurrent applications. The environment consists of an extensible set of actors that are organized via a class-based object model. These actors provide monitoring, debugging, resource management system simulation, and compilation/transformation facilities. To demonstrate the effectiveness of the Rosette architecture, as well as to drive its development, two classes of applications are being used. The first class is the Rosette system itself: all of the system components necessary for resource management, monitoring, debugging, and program development are being constructed in the Rosette language and will take full advantage of both concurrency and object-oriented programming support. The second category of applications is chosen to be somewhat independent of the Rosette system so as to ensure adequate feed-back concerning applicability to domains other than software engineering. These applications are in the general area of knowledge-based signal interpretation. The Rosette architecture is implemented via a concurrent object-oriented programming language that provides the following features: Inherent concurrency : A declarative approach is needed which allows fine-grained concurrency to arise naturally from the program. If the programmer is forced to express all of the details of the concurrent execution of an application, it becomes difficult to re-map a given program to different concurrent computers which have varying concurrency characteristics. Dynamic creation/modification of objects : This allows the system to be extensible and reconfigurable. Sharing : The language should support mechanisms such as inheritance so that code, structure, and values need not be replicated. Classes : Objects can be organized into classes to permit more effective management of resources, as for example in a very large database. Classes should be first-class objects and hence it should be possible to create classes dynamically. The Rosette language is a high-level language that is translated into a base language that directly expresses concurrent computations in terms of the semantics of the Actor model. The Rosette language extends the base language with data abstraction facilities and a library of classes, and is used to implement the non-built-in actors of the interface layer, components of the system environment, and applications. Using data abstraction extensions to the base language, the Rosette language organizes actors into classes according to common sets of behaviors and local state. Inheritance is used as a means of sharing code and structure that is common among classes. There is considerable evidence, for example [LaLonde 1986] and [Snyder 1987], that a robust class system should support separate hierarchies that represent what is modeled by a class and how some class of objects are actually implemented. Our approach is to use three different kinds of classes to organize the various kinds of actors in a system and information about these actors: abstract classes specify observable requests, responses, and actions; representation classes specify resource management characteristics of implementations; and behavior classes contain the actual implementations. Associated with an abstract class is the set of representations that are available to implement the particular abstraction. Each representation is associated with the behavior class that contains the corresponding implementation. The power of this separation is that each of the three kinds of class may be related to others in a class hierarchy. This means that the relationships between abstract classes are not forced to have the same structure as the relationships among implementations. Sharing via inheritance among behavior classes is related to engineering concerns; relations among abstract classes are dictated by external/observable behavior. As an example, an abstract class might specify the operations available over real-matrices . There are several ways of representing a matrix as a community of actors, and each of these would be specified with appropriate representation and behavior classes. There is a well-defined abstract hierarchy of matrices such as invertible, positive , etc. These abstract kinds of matrices are independent of the various implementations, which may be chosen according to application and hardware resource constraints. The Rosette language embodies a reflective model of the interface layer that is similar to that used in ABCL/R, [Watanabe and Yonezawa 1988]. This model is t

Performance analysis of adaptive buffer management mechanism using autoregressive based MPEG-4 encoded video traffic model

In this paper, we build on an established framework for dynamic multi-sensor resource management. The framework is based on the control of the posterior Cramer-Rao lower bound, which provides a bound on the achievable accuracy of target state estimation. We further develop this framework to increase the utility of each sensor by deploying new sensors ahead of the target and in its path. The modified framework is demonstrated in the deployment and utilization of azimuth-only sensors in tracking a single target. Simulation results show that the enhanced strategy is able to accurately track the target while significantly reducing the number of sensors required. However this is at a cost of a significant increase in computation time which may make the technique unsuitable for some sensor management applications.

Authors The 5G networks are designed to support a wide range of services that support the variety of requirements, performance and power utilization of services. The rapid increase in mobile network traffic, the number of connected devices, and the variety of applications being implemented require the efficient and dynamic management of network resources. Recently, several technologies have been proposed to improve 5G networks. Indeed, Mobile Edge Computing (MEC) presents a new opportunity to host applications close to users with a reduction in latency and improved performance. In addition, the concept of network slicing facilitates and the construction of multiple logical networks from a single physical infrastructure has been proposed, which optimizes flexible network evolution. In this article, we will seek to optimize the use of network resources, which are scarce resources, to meet the needs of customers and improve the delivery of services on demand by ensuring the smooth operation of services. In this sense, we start by studying the necessary needs in network resources using machine learning technology, then we apply the principle of network slicing based on the requirements of each service to ensure the dynamic and efficient management of network resources. Results show that our method improves resource utilization and ensures flexible, efficient and dynamic sharing of network resources.