An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment

WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

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.

Recently, the development of cloud computing has received considerable attention. For cloud service providers, packing VMs onto a small number of servers is an effective way to reduce energy costs, so as to improve the efficiency of the data center. However allocating too many VMs on a physical machine may cause some hot spots which violate the SLA of applications. Load balancing of the entire system is hence needed to guarantee the SLA. In this paper, we present a simulated-annealing load balancing algorithm for solving the resource allocation and scheduling problem in a cloud computing environment. Experimental results show that this method is able to achieve load balancing, and performs better than the round robin and basic simulated-annealing algorithms.

Resource allocation is an important part of non-orthogonal multiple access technology. How to allocate resources reasonably to achieve high service quality has become an important research topic. The existing resource allocation algorithms have the disadvantages of low system performance improvement, inability to achieve dynamic resource allocation, and high complexity. In view of the limitations of the existing resource allocation methods, this paper proposes to minimize the power consumption of the base station by obtaining the minimum power required by the terminal to reach the target rate under certain fairness constraints, and at the same time obtain the channel state through the base station. The information calculates the actual rate reached by the terminal in real time. If the rate is lower than a certain value, the resources are reallocated to achieve dynamic resource allocation. Finally, the simulation shows that the performance of the proposed algorithm is improved when the terminal is fixed. When the terminal moves, the system performance is greatly improved and the base station energy consumption is reduced.

In green transportation systems, Vehicular Edge Computing (VEC) is playing a critical role by enabling real-time responses to compute-intensive tasks. Furthermore, incentivizing diverse stakeholders in VEC to share resources and ensuring equitable resource allocation are pivotal to achieving sustainability. In existing studies, a key limitation is that resource price is often simplified to a static decision factor, thus failing to function as a dynamic indicator that reflects real-time load state of the RSUs and the essential balance between the total utility of RSUs and vehicle user experience (QoE). At the same time, the issue is more challenging when facing the load balancing within multi-RSU. To resolve this issue, the paper builds a differentiated resource price model and designs a pricing strategy that combines vehicle QoE and RSUs cost, in order to maximize the total utility of RSUs. Specifically, the paper propose an Incentive-Driven Collaborative Resource Management (IDCRM) algorithm, which integrates the Soft Actor-Critic (SAC) based Computing Resource Allocation and Pricing (SCRAP) algorithm for dynamic resource allocation and pricing by individual RSU, and RSUs cooperative offloading algorithm to address multi-RSU load balancing. The experimental results demonstrate that our proposed IDCRM effectively maximizes the provider’s revenue while ensuring vehicle QoE and significantly alleviates the resource bottleneck of a single RSU through a collaborative mechanism, achieving efficient load balancing across multi-RSU.

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.

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

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

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.

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

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 this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Cloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments.

Free to read on publisher's website Utilizing dynamic resource allocation for load balancing is considered as an important optimization process in cloud computing. In order to achieve maximum resource efficiency and scalability in a speedy manner this process is concerned with multiple objectives for an effective distribution of loads among virtual machines. In this realm,exploring new algorithms, as well as development of novel algorithms, is highly desired for technological advancement and continued progress in resource allocation application in cloud computing. Accordingly, this paper explores the application of two relatively new optimization algorithms and further proposes a hybrid algorithm for load balancing which can contribute well in maximizing the throughput of the cloud provider's network. The proposed algorithm is a hybrid of teaching-learning-based optimization algorithm (TLBO) and grey wolves optimization algorithm (GW). The hybrid algorithm performs more efficiently than utilizing every single one of these algorithms. Furthermore, it well balances the priorities and effectively considers load balancing based on time, cost, and avoidance of local optimum traps, which consequently leads to minimal amount of waiting time. To evaluate the effectiveness of the proposed algorithm, a comparison with the TLBO and GW algorithms is conducted and the experimental results are presented.

Resource management is the primary issue as the demand grows for provisioning resources and computation in cloud systems. When the resource management fails to meet the QoS parameter (response time) requirements, there occurs the due-date assignment problem leading to “resource allocation problem”. This can be optimized by various scheduling algorithms. In this paper analytic hierarchy process is discussed for resource sharing. The results demonstrates that this approach reduces the execution time of algorithm and effectively handles vertical elasticity by adding resources in cloud server. Moreover, the experimental results shows that proposed method can effectively allocates the resources than any other algorithms. Finally, this algorithm is also focused on how efficiency is effectively handled in cloud computing environment while allocating resources since efficiency based resource allocation scheme has been taken into account.