Dynamic Resource Allocation and Adaptability in Teamwork

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

The DVB-RCS standard provides a framework for Multi-Frequency TDMA (MF-TDMA) and dynamic resource (frequencies and time slots) allocation over satellite links. Nevertheless, MF-TDMA and dynamic resource allocation alone are not sufficient to achieve satisfactory throughput for TCP, the dominant transport protocol in IP-based networks such as the Global Information Grid (GIG) system, due to the inherent large delay-bandwidth product of the satellite links. Various mechanisms called TCP Performance Enhancement Proxies (PEP) are proposed to improve the performance of TCP over large delay-bandwidth product networks. However, typical TCP PEPs do not support GIGpsilas layer-3 security standard HAIPE (High Assurance Internet Protocol Encryption). In this paper, we identify eXplicit Control Protocol (XCP) as a desired performance enhancement mechanism, and propose a novel system architecture that integrates DVB-RCS, XCP and HAIPE. We implement XCP in the network simulator QualNet and evaluate its performance against that of TCP. Our simulation results show that XCP achieves higher throughput and fairer bandwidth allocation among competing traffic flows in DVB-RCS systems than TCP.

Traditional algorithms for dynamic OFDMA resource allocation have relatively deterministic system capacity and user fairness. Thus, in this letter, an efficient scheme is proposed to flexibly adjust quality-of-service for users, which is achieved by appropriately setting minimum data-rate of each user.

The increasing number of mobile devices with high processing power and high-resolution screens had led to an enormous growth of mobile video traffic. Mobile network operators face the requirement to efficiently support large numbers of concurrent unicast streaming sessions. In the present work, the long-term quality of experience perceived by the user, the fairness, and the overall system efficiency are addressed simultaneously from the cross-layer perspective by jointly optimizing the video adaptation and the wireless resource allocation. One fundamental challenge of the cross layer design is that the time scale of video adaptation — seconds — differs by several orders of magnitude from the one of resource allocation — milliseconds. We focus on the low-delay live streaming, which is particularly sensitive to the throughput fluctuation. We consider the streaming both in the downlink and in the uplink, explicitly taking into account the imperfect synchronization in the uplink. Our proposed solution consists of two components. First, we formulate the problem of video adaptation as a quality of experience based max-min optimization problem that leverages the link rate estimate in the lower network layers. Second, we propose a dynamic resource allocation scheme that takes into account the demands of the streaming clients. These two components together aim at a fair and efficient cross-layer streaming system. An accurate estimation of link rate on the time scale of seconds, required for this problem, is particularly difficult in mobile networks. As a separate contribution, several link rate estimation approaches are evaluated. The prediction algorithms assuming the static resource allocation, although computationally less complex, may lead to inaccurate prediction results in comparison to the one when dynamic resource allocation scheme is used. In this work, the spectral efficiency gain by dynamic resource allocation can be approximated and used to improve throughput predictions. We evaluate the proposed approach against state-of-the-art baselines. The results reveal significant improvements of quality of experience in all studied use cases.

In order to solve the problems of poor balance and low efficiency of traditional dynamic allocation method of human resources, this paper proposes a new dynamic allocation method of human resources based on improved ant colony optimization. In order to improve the convergence of the traditional ant colony optimization algorithm and avoid falling into the local optimal solution, the ant colony optimization algorithm is improved from three aspects: heuristic function, state selection strategy and pheromone allocation mechanism. Based on the improved ant colony optimization algorithm, the objective function of human resource dynamic allocation is constructed on the basis of human resource workload, work efficiency and work ability index, so as to obtain the optimal allocation results and complete the dynamic allocation of human resources. The experimental results show that, compared with the traditional human resource allocation method, the distribution equilibrium and efficiency of the proposed method are higher, and the distribution equilibrium is basically consistent with the standard value, indicating that the practical application performance of the proposed method is stronger.

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 allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

MIMODSA:Non-Pre-emptive Scheduling in Cognitive Radio Ad-Hoc Networks (CRAHNs)

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

In order to improve the management level of mobile English interactive teaching in the intelligent flipped classroom mode, a design method of mobile English interactive teaching based on deep learning is proposed. Extract the information entropy of the distribution of mobile English interactive teaching resources, analyze the feature quantities of mobile information interaction and channel equilibrium scheduling by using the methods of deep learning and enhanced tracking learning, construct the parameter set of phase space feature reorganization allocation by using the adaptive resource allocation equilibrium control and big data fusion scheduling methods, the dynamic allocation of teaching resources is realized by priority scheduling and dynamic allocation. Realize software development and design under the framework of Linux and NET Framework 4.0. The simulation results show that the proposed method can improve the dynamic allocation and mining ability of mobile English interactive teaching resources, the data fusion level of the platform is high, the channel balance of mobile English interaction is good, and the information interaction ability is strong.

Smart logistics, driven by the Internet of Things (IoT), 5G, and artificial intelligence (AI), depends on real-time data consumption. But, with the need for real-time data processing at the centralized cloud computing layer, centralized cloud computing faces bottlenecks in latency, bandwidth, and privacy. Edge computing supports smart logistics functions by bringing computing close to the edge, but the success of edge computing depends on dynamic allocation of processes to make adjustments for different task loads, devices, and network conditions in logistics scenarios. This review paper will describe the state of research and advances needed for dynamic resource allocation with edge computing research for smart logistics. First, we review the body of work that brings together edge computing and smart logistics, focusing on in-building applications to logistics scenarios such as warehousing, transport, last-mile delivery, emergency logistics scheduling, cold chain logistics monitoring, and, distributed allocation of logistics resources. Next, we discuss key areas of the human-made edge logistics systems to think about a solution for, including resource sensing, prediction, and scheduling. By seeing examples and case studies, we give examples to demonstrate ways of using and improving resource allocations in practice. In addition, we review challenges in coupling heterogeneous edge digital devices and move from low-power sensors through to high-end servers, the urgent need for a few standards or guidelines to allow for seamless interoperable systems. Finally, we discuss the practical challenges of standardization, security, and heterogeneity, and try to highlight opportunities for AI-guided optimization and energy-aware resource allocations are future trends. This review paper will provide a holistic outline to researchers and practitioners with a framework to advance investigations into the management of a dynamic resource allocation system with edge-enabled smart logistics.