A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.

Cloud computing is an Internet-based computing and network model, which provides a market for sharing computing and storage resources. To motivate the cloud providers, the allocation of resources and their pricing becomes one of the challenges in this market. In this situation, designing auction-based mechanisms for cloud resource allocation have attracted a wide interest. Most of prior works focus on designing strategy-proof (a.k.a. truthful) cloud auction mechanisms to eliminate market manipulation. Strategy-proof auctions ensure that no bidders can improve their utilities by untruthful bidding. However, these strategy-proof auctions are fragile when false-name-bids are allowed. This is a new type of cheating where a bidder can gain profit by submitting bids using multiple fictitious names, and it is easy to form in cloud environment. Therefore, a new series of auction mechanisms are needed to guarantee the false-name-proofness. To tackle this issue, in this paper, we designed CACC, a combinatorial auction for resource allocation in Cloud Computing. To address the heterogeneity of cloud resource, the combinatorial auction model is adopted. We show CACC is both strategy-proof and false-name-proof. Moreover, CACC is computationally efficient. Through simulation experiments, we show that CACC is effective and efficient.

The applications that are being installed and run on the mobile devices need a platform of services offered on-demand and be reliable. These services are promised by cloud computing, where resource allocation plays a major role in providing the services and delivering the quality of service (QoS) to the cloud users. The task of provisioning the resources to the cloud users becomes challenging considering the proliferation in the number of users using the cloud on every day basis. The users in this context of proliferation can be considered as mobile nodes that need faster access to communicate among different devices and services. In this paper, the resource allocation on cloud is considered as a game between the clients or the users of the cloud submitting their tasks onto the cloud. Further, a utility maximisation approach as a game theoretic model for an efficient resource allocation in cloud computing is explored in this paper.

Efficient and intelligent resource allocation remains a fundamental challenge in cloud computing due to the dynamic, heterogeneous, and high-dimensional nature of cloud environments. This chapter presents an adaptive hybrid optimization framework that integrates Swarm Intelligence (SI) algorithms with Deep Q Learning (DQL) to address the complexity of real-time decision-making in large-scale cloud systems. The proposed architecture leverages the global exploration capabilities of swarm-based models and the policy-learning strengths of reinforcement learning to enable adaptive and SLA-aware resource management. By combining collective search behavior with neural network-driven policy refinement, the hybrid system dynamically allocates computational resources, minimizes latency, and optimizes long-term performance under varying workload conditions. Extensive analysis demonstrates that the model efficiently navigates high-dimensional state spaces, balances exploration and exploitation, and improves convergence speed while reducing SLA violations. The chapter also includes complexity analysis and performance evaluations, confirming the scalability and responsiveness of the hybrid framework in distributed cloud infrastructures. This work contributes to the development of nextgeneration autonomous systems for cloud optimization by bridging bio-inspired computation and deep learning.

The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing. On the other hand, the resources on the cloud are pooled in order to serve multiple subscribers. The provider use multi-tenancy model where the resources (physical and virtual) are reassigned dynamically based on the tenant requirement (5). The assigning of the resources will be based on the lease and SLA agreement, whereby different clients will need more or less amount of virtual resources. Subsequently, the growth of demands for cloud services is bringing more challenge for the provider to provide the resources to the client subscriber. Therefore, in this paper we provide a review on cloud computing which focus on resource management: allocation and monitoring. Our methodologies for this review are as follows:  We provide a cloud computing taxonomy covers the cloud definitions, characteristics and deployment models.  We then analyze the literatures and discuss about resource management, the process and the elements.  We then concentrate literatures on resource allocation and monitoring. We derived the problems, challenge and the approach solution for resource allocation and monitoring in the cloud. This paper organizes as follows: Section II introduces an overview of Cloud Computing, Section III discuss about resource management and its processes, Section IV discuss about related work with resource management in the cloud, section, Section V describes about approach solution to resource allocation and monitoring and, finally Section VI concludes the paper.

To support any services in all-IP networks, it is necessary to allocate both computing resource and network resource (bandwidth) simultaneously and effectively. The authors proposed the joint resource allocation method for multiple resource types, assuming the non-delay resource allocation. Although the proposed method can achieve an efficient use of resources, it may result in an unfair use of resources in which resources may be monopolized by a specific service. Therefore, the authors proposed the fair joint multiple resource allocation method which tries to equalize the total amount of resource allocated to each service. This paper proposes to enhance the fair joint resource allocation method, assuming three or more services and unbalanced generation traffic. It is demonstrated by basic simulation evaluations that the enhanced fair joint resource allocation method could enable fair resource allocation among multiple services without a large decline in resource efficiency.

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.

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.

Cloud computing is essential for executing scientific workflows, offering scalable resources to process large volumes of data. However, efficient resource allocation remains a challenge due to fluctuations in demand and inadequate planning, which can increase makespan and thus energy consumption. In this context, infrastructure as a service is primarily used, allowing the rental of virtual machines with different characteristics. This study focuses on identifying the factors that influence the improvement of makespan for executing workflows from different disciplines and characteristics. The factors evaluated in this study are workfloF size, structure, and the number of virtual machines (VMs) required. The algorithms in this study consist of four heuristics and one metaheuristic, specifically a genetic algorithm (GA). Regarding the factors, the most relevant in CC is the number of VMs, where increasing the number of VMs reduces makespan to a certain extent, following the behavior described by the law of diminishing returns.

Mobile cloud computing is growing rapidly because its device (i.e., smart phone) is becoming one of the main processing devices for users nowadays. Due to the limitation of resources such as battery life time, CPU and memory capacity, etc., a mobile device cannot satisfy some applications which usually demand more resources than it can afford. To alleviate this, the mobile device should collaborate with external resources to increase its capacity. In order to address these problems, we introduce a collaboration of thin-thick clients which enhances thin client capacities. We further propose a strategy to optimize the data distribution, especially big data in cloud computing. Moreover, we present an algorithm to allocate resources to meet service level agreement (SLA) and conduct simulations to evaluate our approach. Our results evaluation shows that our approach can improve resource allocation efficiency and has better performance than existing approaches.KeywordsCloud computingData distributionThinThick clientResource allocation

Fair and efficient resource allocation in IEEE 802.11ah WLAN with heterogeneous data rates

Organizations need to understand the importance of resource allocation and traffic forecasting for cloud computing more than ever because of the increasing demand for online services, data storage and remote work, which makes it challenging to estimate traffic and distribute resources. In cloud computing, there is still opportunity for increasing the model's forecast accuracy. The more accurate the traffic flow, the better the resource allocation. Therefore, this study investigate the adequate data analytic frameworks for managing cloud computing resource allocation in the organisation, According to the findings, six technique were used just for prediction, while one was utilized solely for resource allocation. In this sense, cloud computing resource allocation and prediction may be achieved by combining several techniques.this paper contributes the review to identify adequate data analytic frameworks for managing cloud computing resource allocation.

This paper presents a critical evaluation of current network resource allocation strategies and their possible applicability in Cloud Computing Environment which is expected to gain a prominent profile in the Future Internet. This research attempts to focus towards network awareness and consistent optimization of network resource allocation strategies and identifies the issues which need further investigation by the research community. A framework for network resource allocation in Cloud Computing, based on tailored active measurements, has also been proposed. The main conclusion is that network topology, traffic considerations, changing optimality criteria along with dynamic user requirements will play a dominant role in determining future Internet application architectures and protocols and in shaping resource allocation and management strategies in vital applications like Cloud Computing.

Cloud computing is internet-based computing era. The resources that are provided by cloud computing is easily accessible by the cloud clients when they are demanding. The infrastructure of cloud computing is dynamic in nature and resources are optimally allocated. These resources shared in cloud computing, like any other paradigm resource management is main issue in cloud computing. It is very challenging to provide all demanding resources, as the number of available shared-resources are increasing. This paper reviews sharing of resources (like servers, applications and data) over cloud and consider techniques to make adaptive algorithms for management of resources in cloud computing.

Cloud computing is a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower the cost of their infrastructure by renting resources from the cloud for storage and other processing needs. They can also use pay-as-you-go application access available to the entire firm. Therefore, obtaining licenses for specific products is not necessary. However, allocating resources as efficiently as possible is one of the main challenges in cloud computing. Because the model is unique, resource allocation is done to lower its expenses. Meeting application and customer requirements and consumer needs presents additional issues in resource allocation. This study provides a detailed discussion of several resource allocation systems, security threats, and the difficulties they face. This work is anticipated to help researchers and cloud users overcome obstacles.