Resource Allocation In Cloud Computing Research Articles

A Game-Theoretic Based Resource Allocation Strategy for Cloud Computing Services

We propose an economics-oriented cloud computing resources allocation strategy with the use of game theory. Then we develop a resource allocation algorithm named NCGRAA (noncooperative game resource allocation algorithm) to search the Nash equilibrium solution that makes the utility of various resource providers achieve optimum. We also propose an algorithm named BGRAA (bargaining game resource allocation algorithm) to further increase the overall revenue with the constraints of efficiency and fairness. Based on numerical results, we discuss the influence of NCGRAA and BGRAA for the utility of resource on the system performance. It shows that the choice of parameters of the two algorithms is significant in improving the system performance and converging to the Nash equilibrium and Nash bargaining.

OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

Abstract Load balancing is treated as one of the important mechanisms for efficient resource allocation in cloud computing. In future there will appear a necessity of fully autonomic distributed systems to address the load balancing issues. With reference to this, we proposed a load balancing mechanism called Osmosis Load Balancing (OLB). OLB works on the principle of osmosis to reschedule the tasks in virtual machines. The solution is based on the Distributed Hash Table (DHT) with a chord overlay mechanism. The Chord overlay is used for managing bio inspired agents and status of the cloud. By simulation analysis, the proposed algorithm has shown better performance in different scenarios, both in heterogeneous and homogeneous clouds.

Research of Resource Allocation in Cloud Computing Based on Improved Dual Bee Colony Algorithm

The allocation of resources in cloud computing has always been the focus of research, and as for how to improve the implementation speed, response time and utilization rate of system, a resource allocation scheduling optimization method in cloud computing based on improved double bee colony algorithm is proposed in this paper. To introduce difference algorithm in artificial ant colony and improve its double bee colony can avoid the bee colony to fall into local optimum, while by introducing chaos algorithm, the convergence speed and precision of the optimal solution in the improved algorithm can be accelerated and the best resources in clouding computing can be found quickly to a certain extent. The simulation experiments show that the algorithm in this paper has improved the algorithm performance efficiently, which not only optimizes the resource allocation ability in cloud computing system, but also improves the resource utilization rate in cloud computing.

Dynamic Resource Allocation for Overload Avoidance and Green Cloud Computing

Cloud Computing is a flourishing technology because of its scalability, flexibility, availability of resources and other features. Resource multiplexing is done through the virtualization technology in cloud computing. Virtualization technology acts as a backbone for provisioning requirements of the cloud based solutions. At present, load balancing is one of the challenging issues in cloud computing environment. This issue arises due to massive consumer demands variety of services as per their dynamically changing requirements. So it becomes liability of cloud service provide to facilitate all the demanded services to the cloud consumers. However, due to the availability of finite resources, it is very challenging for cloud service providers to facilitate all the demanded services efficiently. From the cloud service provider’s perspective, cloud resources must be allocated in a fair manner. This paper addresses the existing techniques for resource allocation in cloud computing and proposes the dynamic resource allocation technique to mitigate overloads. It also focuses on energy consumption issue of cloud data centres and devised technique for lower energy consumption in order to achieve green cloud computing.

Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing

The main aim of this research is to design the hybrid evolutionary algorithm for minimizing multiple problems of dynamic resource allocation in cloud computing. The resource allocation is one of the big problems in the distributed systems when the client wants to decrease the cost for the resource allocation for their task. In order to assign the resource for the task, the client must consider the monetary cost and computational cost. Allocation of resources by considering those two costs is difficult. To solve this problem in this study, we make the main task of client into many subtasks and we allocate resources for each subtask instead of selecting the single resource for the main task. The allocation of resources for the each subtask is completed through our proposed hybrid optimization algorithm. Here, we hybrid the Binary Particle Swarm Optimization (BPSO) and Binary Cuckoo Search algorithm (BCSO) by considering monetary cost and computational cost which helps to minimize the cost of the client. Finally, the experimentation is carried out and our proposed hybrid algorithm is compared with BPSO and BCSO algorithms. Also we proved the efficiency of our proposed hybrid optimization algorithm.

CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing

This paper addresses performance issues of resource allocation in cloud computing. We review requirements of different cloud applications and identify the need of considering communication processes explicitly and equally to the computing tasks. Following this observation, we propose a new communication-aware model of cloud computing applications, called CA-DAG. This model is based on Directed Acyclic Graphs that in addition to computing vertices include separate vertices to represent communications. Such a representation allows making separate resource allocation decisions: assigning processors to handle computing jobs, and network resources for information transmissions. The proposed CA-DAG model creates space for optimization of a number of existing solutions to resource allocation and for developing novel scheduling schemes of improved efficiency.

A Review on Techniques of Resource Allocation in Cloud Computing

Cloud computing is the new era of technology, it uses virtualization and automation to provide user with self-service computing products that provide infinite scale at low cost. It serves user in many ways such as Infrastructure as a Service (IaaS), Software as a Service (SaaS), Platform as a Service (PaaS) etc. While serving as an IaaS it is inevitable that resources must be provided in an efficient way in order to meet the Service Level Agreement (SLA). This paper mainly addresses challenges, performance issues and techniques for resource allocation in cloud computing.

Double Combinatorial Auction based Resource Allocation in Cloud Computing by Combinational using of ICA and Genetic Algorithms

Computational resources have become cheaper, more powerful and accessible than ever with the development of processing and strong technology and internet success. This technology has realized a new computing model called cloud computing in which resources are provided as a service to users and users can rent them or rent of them based on their demands. One of the problems with cloud computing is related to optimizing the allocated resources. Resource allocation is done with the aim of minimizing costs, times and due to the uniqueness of model. In this paper, one double combinatorial auction based resource allocation approach has been proposed. The purpose of this study is to allocate economic resources in a way that lead to increase social welfare. Resource allocation in double combinatorial auction includes two phases. These two phases are winner determination and resource allocation. The proposed approach implement ICA for winner determination and genetic algorithm for resource allocation and payment Schemes .we name this algorithm as IG. The experimental results showed that our proposed algorithm improves the profits of providers and users and works well than other algorithms in the term of increasing providers’ profit. General Terms Cloud computing, resource allocation

Solving Winner Determination Problem in Double Combinatorial Auction by ICA in Cloud Computing

There are many different ways to allocate resources in cloud computing. The economic methods are some of common ways to resource allocation. The auction-based method has some advantages compared with Fixed-Price method. The double combinatorial auction is one of the proper ways of resource allocation in cloud computing. Resource allocation in double combinatorial auction includes two phases. These two phases are winner determination and resource allocation. The proposed algorithm has implemented ICA to determine winner in double combinatorial auction-based resource allocation. The experimental results showed that in proposed algorithm the number of winner users is higher in proposed algorithm, but the number of winner providers is higher in genetic algorithm.

Early experiences with live migration of SR-IOV enabled InfiniBand

Virtualization is the key to efficient resource utilization and elastic resource allocation in cloud computing. It enables consolidation, the on-demand provisioning of resources, and elasticity through live migration. Live migration makes it possible to optimize resource usage by moving virtual machines (VMs) between physical servers in an application transparent manner. It does, however, require a flexible, high-performance, scalable virtualized I/O architecture to reach its full potential. This is challenging to achieve with high-speed networks such as InfiniBand and remote direct memory access enhanced Ethernet, because these devices usually maintain their connection state in the network device hardware. Fortunately, the single root IO virtualization (SR-IOV) specification addresses the performance and scalability issues. With SR-IOV, each VM has direct access to a hardware assisted virtual device without the overhead introduced by emulation or para-virtualization. However, SR-IOV does not address the migration of the network device state. In this paper we present and evaluate the first available prototype implementation of live migration over SR-IOV enabled InfiniBand devices.

Modelling resource virtualisation concept in cloud computing environment using finite state machines

Resource virtualisation can enhance resource availability to the offered services in cloud computing. Modelling of virtualised resources for dynamically changing workload is an important problem to be tackled, so as to design and develop resource management algorithms. This paper presents modelling of resource virtualisation concept for cloud computing scenarios based on the formalism of finite state machines (FSMs). The resources considered in our model are processors and primary memory; however, the model can also be applied to other resources like storage and network devices. The basis for description of model behaviour is communicating extended FSMs that are represented by systems, blocks and processes. The proposed model is used to analyse the impact of loads on virtualisation in cloud computing by using Cindrella SDL tool. It is observed that resources are allocated depending on the availability without any deadlocks and terminations. The model can serve as the foundation for development of resource allocation, resource brokering, resource security, resource provisioning, and resource adaptation protocols in cloud computing.

Consumer Context based Resource Allocation in Cloud Computing

Computing has recently emerged as a compelling paradigm for managing and delivering services over the internet. It is now considered as one of the rapidly developing platforms in the various fields related to computer science in one way or another. It is provided in a pay-per-use manner as per the demand of the consumer. Resource allocation in cloud computing is performed with the objective of minimizing the costs related with the management of resources at the provider side. It also involves fulfillment of customer demands and application requirements. Numerous techniques for resource allocation have been proposed and implemented so far in the world of cloud computing but none of which has considered introducing a parameter from the consumer context. Resource allocation being one of the most critical factors which defines the overall efficiency of the cloud, usually involves an attribute on which the whole process of the allocation of resources is based upon. In this report, a policy has been proposed & implemented, keeping in mind the importance of consumer context in cloud. The importance of context in the growth and success of a business has also been discussed.

An online auction framework for dynamic resource provisioning in cloud computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first online combinatorial auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e + 1 over e -1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e -1 in competitive ratio, (2) a randomized auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in auction design for other related problems.

An optimal resource provision policy in cloud computing based on customer profiles

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement (SLA). Unfortunately, the expected response time is highly variable and it is usually longer than the value of SLA. So, it leads to a poor resource utilization and unnecessary servers migration. We develop a framework for customer-driven dynamic resource allocation in cloud computing. Termed CDSMS (customer-driven service manage system), and the framework’s contributions are twofold. First, it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’ profiles. Second, it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization. Finally, we perform a serious experiment in a real cloud computing platform. Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.

Auction based resource allocation in cloud computing

Resource allocation is fundamental function of cloud computing. Different auction models offer a variety of market driven resource allocation mechanisms. This paper studies auction based resource allocation for cloud computing. First, a framework of

A Survey on Resource Allocation and Monitoring in Cloud Computing

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.

Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm

ABSTRACTCloud computing is a new distributed commercial computing model that aims at providing computational resources or services to users over a network in a low-cost manner. Resource allocation and scheduling (RAS) is the key focus of cloud computing, and its policy and algorithm have a direct effect on cloud performance and cost. This paper presents five major topics in cloud computing, namely locality-aware task scheduling; reliability-aware scheduling; energy-aware RAS; Software as a Service (SaaS) layer RAS; and workflow scheduling. These five topics are then classified into three parts: performance-based RAS; cost-based RAS; and performance- and cost-based RAS. A number of existing RAS policies and algorithms are discussed in detail accordingly with regard to their given parameters. In addition, a comparative analysis of five identified problems with their representative algorithms is performed. Finally, some future research directions of cloud RAS are pointed out.

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

On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms.

Optimized Task Scheduling and Resource Allocation in Cloud Computing Using PSO based Fitness Function

Optimized Task Scheduling and Resource Allocation in Cloud Computing Using PSO based Fitness Function

Achieving Quality of Service (QoS) Using Resource Allocation and Adaptive Scheduling in Cloud Computing with Grid Support

Achieving Quality of Service (QoS) Using Resource Allocation and Adaptive Scheduling in Cloud Computing with Grid Support