Resource Allocation In Cloud Computing Research Articles

Cloud service reliability modelling and optimal task scheduling

Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP-hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov-based method. They formulate the cloud scheduling problem as a multi-objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm-based chaotic ant swarm (GA-CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi-objective optimisation problem. Simulation results have demonstrated that GA-CAS generally speeds up convergence and outperforms other meta-heuristic approaches.

Towards a fairer negotiation for dynamic resource allocation in cloud by relying on trustworthiness

The automated negotiation for resource leasing contracts in cloud produces many challenges. Since we incorporate an identity for each participant, future utilities will play a major role in their strategies in a cloud marketplace. In fact, unlike traditional agents' strategies, our strategies had to take into account two additional components, namely, future utilities involving the reputation of agents' identities as well as transactions' fees in a marketplace destined for dynamic resource allocation for cloud computing. The aim of this paper is to design such strategies that prevent any practice that may cause unfairness to arise due to some drawbacks in the resource allocation protocol helping some participants to take unfair advantages over others. Comparative experiments with non-trust model show the effectiveness of our approach in terms of fairness and social welfare.

Utility maximisation-based game theoretic approach for resource allocation in clouds

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.

Dynamic Resource Allocation in Cloud Computing

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.

Towards a fairer negotiation for dynamic resource allocation in cloud by relying on trustworthiness

The automated negotiation for resource leasing contracts in cloud produces many challenges. Since we incorporate an identity for each participant, future utilities will play a major role in their strategies in a cloud marketplace. In fact, unlike traditional agents' strategies, our strategies had to take into account two additional components, namely, future utilities involving the reputation of agents' identities as well as transactions' fees in a marketplace destined for dynamic resource allocation for cloud computing. The aim of this paper is to design such strategies that prevent any practice that may cause unfairness to arise due to some drawbacks in the resource allocation protocol helping some participants to take unfair advantages over others. Comparative experiments with non-trust model show the effectiveness of our approach in terms of fairness and social welfare.

Cloud computing resources scheduling optimisation based on improved bat algorithm via wavelet perturbations

Efficient scheduling of computing resources is a fundamental issue that the cloud computing needs to solve, which involves highly challenging load-balance of multiple computing resources. In order to achieve optimal balance between the executing speed, the average response time and the overall system utilisation during cloud computing resource allocation, a cloud computing resources scheduling optimisation algorithm is proposed based on wavelet perturbation-based bat algorithm (WPBA). The algorithm first employs wavelet perturbation to enhance Bat algorithm's performance followed by population-entropy-guided substitution to control diversity and improve the converging speed and accuracy. Then it adopts WPBA to achieve resources scheduling optimisation of the cloud computing. The experiment shows that using WPBA has significantly improved the overall performance of the algorithm and has also remarkably optimised the resource scheduling capability of cloud computing and heightened the overall resource utilisation.

Cloud computing resources scheduling optimisation based on improved bat algorithm via wavelet perturbations

Efficient scheduling of computing resources is a fundamental issue that the cloud computing needs to solve, which involves highly challenging load-balance of multiple computing resources. In order to achieve optimal balance between the executing speed, the average response time and the overall system utilisation during cloud computing resource allocation, a cloud computing resources scheduling optimisation algorithm is proposed based on wavelet perturbation-based bat algorithm (WPBA). The algorithm first employs wavelet perturbation to enhance Bat algorithm's performance followed by population-entropy-guided substitution to control diversity and improve the converging speed and accuracy. Then it adopts WPBA to achieve resources scheduling optimisation of the cloud computing. The experiment shows that using WPBA has significantly improved the overall performance of the algorithm and has also remarkably optimised the resource scheduling capability of cloud computing and heightened the overall resource utilisation.

A Survey on Resource Allocation in Cloud Computing

A Survey on Resource Allocation in Cloud Computing

A Non-Cooperative Differential Game Model for Resource Allocation in Cloud Computing

A Non-Cooperative Differential Game Model for Resource Allocation in Cloud Computing

Systematic Literature Review on Resource Allocation and Resource Scheduling in Cloud Computing

The objective the work is intend to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management has progressively increased in the past decade by inspecting articles, papers from scientific and standard publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we performed a structural analysis on different author’s prominent contributions in the form of tabulation by categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also impart a summary on all resource allocations. In cloud computing environments, there are two players: cloud providers and cloud users. On one hand, providers hold massive computing resources in their large datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who have applications with fluctuating loads and lease resources from providers to run their applications. Further, delivers conclusions by conferring future research directions in the field of cloud computing, such as reduce clouds early in the Internet, combining Resource Allocation, Resource Scheduling and Resource management rather than a Cloud model for providing high quality results, etc.

Adaptive Multi-Tenancy Policy for Enhancing Service Level Agreement Through Resource Allocation in Cloud Computing

The appearance of infinite computing resources that available on demand and fast enough to adapt with load surges makes Cloud computing favourable service infrastructure in IT market. Core feature in Cloud service infrastructures is Service Level Agreement (SLA) that led seamless service at high quality of service to client. One of the challenges in Cloud is providing heterogeneous computing services for the clients. With the increasing number of clients/tenants in the Cloud, unsatisfied agreement is becoming a critical factor. In this paper, we present an adaptive resource allocation policy which attempts to improve accountable in Cloud SLA while aiming for enhancing system performance. Specifically, our allocation incorporates dynamic matching SLA rules to deal with diverse processing requirements from tenants. Explicitly, it reduces processing overheads while achieving better service agreement. Simulation experiments proved the efficacy of our allocation policy in order to satisfy the tenants; and helps improve reliable computing.

Based on Particle Swarm Optimization Algorithm of Cloud Computing Resource Scheduling in Mobile Internet

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business; Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

The Role of Virtualization Techniques to Overcome the Challenges in Cloud Computing

oud computing is a new trend which increase application ability in terms of functionality, resource management and collaborative aspect in term of distributed as well as centralized computing environment. Cloud computing system produce new disputes because of system clusters and high volume data generated by these systems. Storage virtualization makes stockpiling a ware. This makes for some intriguing courses for organizations to lessen their expenses. The central part of cloud computing is virtualization which enables industry or academic IT resources through on-demand allocation dynamically. The main importance in cloud computing is virtualization, which perform user requests on different logical machines in term of their execution in a predefined manner. Cloud computing is unquestionably conceivable without virtualization, however requires more work and time to really pull it off; by utilizing virtualization, it would be fundamentally getting a great deal of the work accomplished for nothing. The resources can be multiple networks in a client server environment having storage as well as applications. The main purpose of this research is how to increase resource management with virtualization which will improve the performance of cloud computing resource allocation its challenges and proposed new approach which will helpful to optimize the performance in sharing network infrastructure.

Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory

Virtualization of resources on the cloud offers a scalable means of consuming services beyond the capabilities of small systems. In a cloud that offers infrastructure such as processor, memory, hard disk, etc., a coalition of virtual machines formed by grouping two or more may be needed. Economical management of cloud resources needs allocation strategies with minimum wastage, while configuring services ahead of actual requests. We propose a resource allocation mechanism for machines on the cloud, based on the principles of coalition formation and the uncertainty principle of game theory. We compare the results of applying this mechanism with existing resource allocation methods that have been deployed on the cloud. We also show that this method of resource allocation by coalition-formation of the machines on the cloud leads not only to better resource utilization but also higher request satisfaction.

Research based on Effective Resource Allocation of Improved SFLA in Cloud Computing

It is the focus of current cloud research as how to reasonably use resources in cloud computing. In this paper, shuffled frog leaping algorithm is introduced into the cloud computing resource allocation, and aiming at its slow speed in getting solution and being easy to fall into local optimum, improvement is made. First, reverse learning algorithm is used to initialize the algorithm; then, cross factor is introduced into the local research; finally, evolutionary strategy is adopted in selecting the optimal solution combining the global search and local search to improve the performance of improved algorithm. It has be proven through the Cloudsim platform that algorithm in this paper effectively solves the problem of resource allocation and improves the efficiency of resource allocation.

Optimization Approach for Resource Allocation on Cloud Computing for IoT

Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS (Quality of Service) constraints are satisfied and provider's profit is maximized. In order to increase the profit, the penalty cost for SLA (Service Level Agreement) violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job's urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider's profit and success rate of job completion with conventional mechanism using real workload data.

A Survey on Energy Aware Resource Allocation for Cloud Computing

A Survey on Energy Aware Resource Allocation for Cloud Computing

An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud

Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions.

Resource Allocation in Cloud Computing Using Imperialist Competitive Algorithm with Reliability Approach

Cloud computing has become a universal trend now. So, for users, the reliability is an effective factor to use this technology. In addition, users prefer to implement and get their work done quickly. This paper takes into account these two parameters for resource allocation due to their importance In this method, the Imperialist Competitive algorithm with the addition of a cross layer of cloud architecture to reliability evaluation is used. In this cross layer, initial reliability is considered for all the resources and the implementation of their tasks and due to the success or failure of implementation, reliability of resources is increased or reduced. Reliability and makespan are used as a cost function in ICA for resource allocation. Results show that the proposed method can search the problem space in a better manner and give a better performance when compared to other methods.

Modified Round Robin Algorithm for Resource Allocation in Cloud Computing

Cloud computing is an attractive computing model since it allows for the provision of resources on-demand. Cloud computing has emerged as a new technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. Companies are able to rent resources from cloud for storage and other computational purposes so that their infrastructure cost can be reduced significantly. Hence there is no need for getting licenses for individual products. Cloud Computing offers an interesting solution for software development and access of content with transparency of the underlying infrastructure locality. The Cloud infrastructure is usually composed of several data centers and consumers have access to only a slice of the computational power over a scalable network. The provision of these computational resources is controlled by a provider, and resources are allocated in an elastic way, according to consumers’ needs. However one of the major pitfalls in cloud computing is related to optimizing the resources being allocated. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper, modified round robin resource allocation algorithm is proposed to satisfy customer demands by reducing the waiting time.