DRAFAS: Dynamic Resource Allocation for AI-Native Services

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.

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.

Improved resource utilization and fault tolerance of large-scale HPC systems can be achieved through fine-grained, intelligent, and dynamic resource (re)allocation. We explore components and enabling technologies applicable to creating a system to provide this capability: specifically 1) Scalable fine-grained monitoring and analysis to inform resource allocation decisions, 2) Virtualization to enable dynamic reconfiguration, 3) Resource management for the combined physical and virtual resources and 4) Orchestration of the allocation, evaluation, and balancing of resources in a dynamic environment. We discuss both general and HPC-centric issues that impact the design of such a system. Finally, we present our prototype system, giving both design details and examples of its application in real-world scenarios.

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 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.

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.

Today Cloud computing is on demand as it offers dynamic flexible resource allocation, for reliable and guaranteed services in pay-as-you-use manner, to Cloud service users. So there must be a provision that all resources are made available to requesting users in efficient manner to satisfy their needs. This resource provision is done by considering the Service Level Agreements (SLA) and with the help of parallel processing. Recent work considers various strategies with single SLA parameter. Hence by considering multiple SLA parameter and resource allocation by preemption mechanism for high priority task execution can improve the resource utilization in Cloud. In this paper we propose an algorithm which considered Preemptable task execution and multiple SLA parameters such as memory, network bandwidth, and required CPU time. An obtained experimental results show that in a situation where resource contention is fierce our algorithm provides better utilization of resources.

In cluster-based cognitive radio networks, secondary system uses available channels that are not used temporally by primary systems. In multi-channel operational environments, each available channel may have different wireless channel gain and primary activity so that achievable data rate to secondary users (SUs) and required sensing parameter value are channel dependent. SUs also have different energy saving requirements and data traffic demands. Therefore, based on measured channel conditions and user constraints, cluster head needs to decide which channel should be allocated to which SUs and to configure the optimum MAC frame structure for satisfying each SU’s service demands. Furthermore, optimization to provide proportional fairness among the cognitive secondary users in resource allocation in terms of energy consumption and data rate is very important. In this paper, a dynamic MAC frame configuration and optimal resource allocation scheme for multi-channel ad-hoc cognitive radio network is proposed. We formulate our dynamic resource allocation model as a constrained optimization problem with multi-objective functions using particle swarm optimization (PSO) algorithm. The proposed PSO scheme guarantees that the allocation captures the individual traffic and energy saving demands and maximizes the objectives functions simultaneously. Simulation results show the proposed scheme can successfully maximize the intended utility function and provide proportional fairness between SUs.

Dynamic and fair resource allocation in a distributed ring-based WDM-PON architectures

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.

: Prior research in this program established that team performance is a consequence of how individuals allocate resources to accomplish multiple goals. This project extended that work around two primary research foci. The first focus built on our prior research on feedback as a means to influence dynamic resource allocation and adaptation. Resource allocation is a promising mechanism to explain performance adaptation to unexpected environmental perturbations. In general, the findings indicate that priming resource allocation and providing more rapid feedback updating on goal-performance discrepancies enhanced situation assessment, strategy selection, and performance adaptation. Both interventions have implications for training design and embedded decision aids. The second focus unpacked the resource allocation process by examining the dynamic interplay among the core elements of goals, effort, and performance over time. Although our prior research demonstrated that these elements were responsible for individual and team performance, the dynamic interplay was not addressed. Modeling these dynamics over time yielded important insights that will enable improved human performance in complex task domains. In particular, this extension of our research paradigm enables more precise modeling of the limits and wide variance of human performance for dynamic resource allocation. This serves as a basis for more accurate evaluation of experimental interventions designed to improve multiple goal regulation and performance adaptation, and to develop formal models of that process.

Nowadays, Device-to-Device (D2D) communication in LTE system has been proposed as a possible candidate for vehicle-to-vehicle(V2V) communication. As V2V communications share the same licensed frequency spectrum pool with typical UEs communications in one LTE cell, efficient radio resource allocation is a challenging issue. In this paper, we proposed a traffic flow based dynamic resource allocation algorithm for vehicular communication networks, which could make radio resource to be utilized in a more efficient way with a flexible node-grouping algorithm. The simulation results show, compared with random allocation and greedy allocation algorithms, the proposed algorithm can significantly reduce running time and significantly improve spectrum utilization efficiency.

Dynamic resource control capabilities have become increasingly important for academic networks that must support big scientific research projects at the same time as less data intensive research and educational activities. This paper describes the dynamic resource allocation and QoS control capabilities of the Japanese academic backbone network, called SINET3, which supports a variety of academic applications with a wide range of network services. The article describes the network architecture, networking technologies, resource allocation, QoS control, and layer-1 bandwidth on-demand services. It also details typical services developed for scientific research, including the user interface, resource control, and management functions, and includes performance evaluations.