Cloud Resource Allocation Research Articles

Research on Resource Allocation Technology of Cluster Virtual Cloud Based on SDN

The ever-expanding demand for cloud computing needs to build a large-scale data center. How to operate the data center efficiently is an urgent problem to be solved. Statistical reuse is a prominent feature in cloud computing, and the utilization of physical resources can be improved by using virtualization technology. The core technology of network virtualization is to decouple the virtual network based on software from the underlying network based on hardware, and the proposal of Software Defined Network (SDN) provides a practical solution to solve this problem. Cloud computing puts forward the highest goal of sharing resources in the whole Internet, makes full use of computing/storage resources to the maximum extent, and is an effective method to integrate high-performance computing resources of the whole society. This chapter describes the background and significance of virtual resource allocation in cloud computing environment, studies the cluster virtual cloud resource allocation technology based on SDN, and analyzes how to realize reasonable cloud computing resource allocation to ensure network performance.

A Two-Stage Auction Mechanism for Cloud Resource Allocation

With the recent growth in the size of cloud computing business, handling the interactions between customers and cloud providers has become more challenging. Auction theory has been proposed to model these interactions due to its simplicity and a good match with real-world scenarios. In this paper, we consider cloud of clouds networks (CCNs) with different types of servers along with customers with heterogeneous demands. For each CCN, a CCN manager is designated to handle the cloud resources. A comprehensive framework is introduced in which the process of resource gathering and allocation is addressed via two stages, where the first stage models the interactions between customers and CCN managers, and the second stage examines the interactions between CCN managers and private cloud providers (CPs). For the first stage, an options-based sequential auction (OBSA) is adapted to the examined market, which is capable of providing truthfulness as the dominant strategy and resolving the entrance time problem. An analytical foundation for OBSAs is presented and multiple performance metrics are derived. For the second stage, two parallel markets are assumed: flat-price and auction-based market. A theoretical framework for market analysis is provided and the bidding behavior of CCN managers is described.

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Adaptive IWD-based algorithm for deployment of business processes into cloud federations

PurposeThe concept of business process (BP) as a service is a new solution in enterprises for the purpose of using specific BPs. BPs represent combinations of software services that must be properly executed by the resources provided by a company’s information technology infrastructure. As the policy requirements are different in each enterprise, processes are constantly evolving and demanding new resources in terms of computation and storage. To support more agility and flexibility, it is common today for enterprises to outsource their processes to clouds and, more recently, to cloud federation environment. Ensuring the optimal allocation of cloud resources to process service during the execution of workflows in accordance with user policy requirements is a major concern. Given the diversity of resources available in a cloud federation environment and the ongoing process changes required based on policies, reallocating cloud resources for service processing may lead to high computational costs and increased overheads in communication costs.Design/methodology/approachThis paper presents a new adaptive resource allocation approach that uses a novel algorithm extending the natural-based intelligent water drops (IWD) algorithm that optimizes the resource allocation of workflows on the cloud federation which can estimate and optimize final deployment costs. The proposed algorithm is implemented and embedded within the WokflowSim simulation toolkit and tested in different simulated cloud environments with different workflow models.FindingsThe algorithm showed noticeable enhancements over the classical workflow deployment algorithms taking into account the challenges of data transfer. This paper made a comparison between the proposed IWD-based workflow deployment (IWFD) algorithm with other proposed algorithms. IWFD presented considerable improvements in the makespan, cost and data transfer in most situations in the cloud federation environment.Originality/valueAn extension for WorkflowSim to support the implementation of BPs in a federation cloud space regarding BP policy. Optimize workflow execution performance in Federated clouds by means of IWFD algorithm.

Software-defined networks for resource allocation in cloud computing: A survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

Optimized resource allocation in full-duplex cloud radio access network using firefly algorithm

ABSTRACT The main objective of the study is to allocate the resources in a Full-duplex cloud radio access network. Also, the use of C-RAN suppresses the self-interference because of the distributed behaviour of Remote Radio Head in C-RAN. Full-duplex communication system allows data to be transmitted and received between stations simultaneously. It has double the spectral efficiency of half-duplex communication system. The work aims to find the optimal value of the power that is allocated to the remote radio head when data rate is maximum. For the optimization, Firefly Algorithm is employed. It is an optimization algorithm that works similar to the firefly’s attraction towards flashing light. The brighter firefly attracts the less bright one to move towards them. It is an iterative process and eventually, the population of firefly converges to brightest one. When maximum inverse data rate is achieved, the corresponding power value is noted and they are employed for the distribution among the single antenna and multiple antenna remote radio heads. In order to find the best cost value, power constraints are applied to it. Finally, the proposed work is compared with generalized benders decomposition (GBD)-based resource allocation (GRA) algorithm by plotting graphs using MATLAB.

Semantic based Inter-Operable Cloud Resource Allocation

Cloud computing is the process of lending the required resources that includes computing power, network access, database storage, applications, and other IT resources in on demand basis through a cloud services platform with pay-as- you-go pricing. Here, users access services according to their requirements, without knowing where the services are hosted or how they’re delivered. So Resource Management is tedious process between different clouds. Since different clouds will have different types of cloud virtualization techniques with corresponding middleware's with unique mechanism and protocols, we face lot of interoperability issues. So there is a need to address these interoperability issues between different clouds to achieve the highest availability of cloud resources. This motivates us to conceive a proposal Semantic based Interoperable Cloud Resource Allocation (SICRA) that provides a common mechanism to access the resources of different clouds Proposed work also incorporates the semantic services for identifying best potential resource. Thus an ontology based indexing and retrieving algorithms are designed and provided with ranking which in turn increases the success rate.

Application of a Long Short Term Memory neural predictor with asymmetric loss function for the resource allocation in NFV network architectures

Traffic and cloud resource prediction methodologies have been recently used in Network Function Virtualization environment for cloud and bandwidth resource allocation purposes. Both traditional and innovative prediction methodologies have been proposed for the application of allocation procedures. For instance Long Short Term Memory-based prediction techniques have been shown to be very effectiveness to allocate the resources. All of these techniques are based on the minimization of a symmetric cost function as the Root Mean Square Error that equally weights positive and negative prediction errors. However the error sign can differently impact the cost increase due to prediction errors. For instance when the Quality of Service degradation cost due to traffic loss is prevalent with respect to the cloud resource allocation cost, an algorithm is preferable that overestimates the offered traffic; conversely the traffic underestimation is preferable in the opposite case when the cloud allocation cost is higher than the QoS degradation one. For this reason we propose an Asymmetric LSTM traffic prediction procedure in which the cost function is defined so as to take into account both the QoS degradation and cloud resource allocation costs. In a typical network and traffic scenario, we show how the proposed solution allows for cost decrease by 40% with respect to classical LSTM prediction methodology based on the Root Mean Square Error.

A Review of Dynamic Resource Allocation Framework for Large Amount of Cloud Enterprises

Cloud computing is an on-demand service because it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner. Because of the consistently increasing demands of the clients for services or resources, it gets hard to allocate resources accurately to the client demands to satisfy their solicitations and also to take care of the Service Level Agreements (SLA) gave by the service suppliers. Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research network in the last couple of years. Many researchers around the world have thought of new ways of facing this challenge. Ad-hoc parallel data handling has arisen to be one of the executioner applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud supplier companies has started to incorporate frameworks for parallel data handling in their item which making it easy for clients to access these services and to convey their programs. The handling frameworks which are at present utilized have been intended for static and homogeneous bunch arrangements. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase preparing cost and time. Again because of opaque nature of cloud, static allocation of resources is conceivable, yet the other way around in dynamic situations. The proposed new generic data handling framework is expected to expressly misuse the dynamic resource allocation in cloud for task scheduling and execution.

An efficient mobility prediction model for resource allocation in mobile cloud computing

Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.

A novel multiclass priority algorithm for task scheduling in cloud computing

Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the dynamic and heterogeneous nature of user’s request and provider’s resource in cloud computing, the scheduling process still needs intelligent algorithms to achieve an efficient cloud resource allocation and to guarantee a good Quality of Service (QoS) for the users and their request classes. An important aspect for meeting these objectives is to design an effective task scheduling scheme which can not only satisfy users’ varying priorities and QoS requirements, but also enhance providers’ profit and system performances. In this paper, we introduce a new strategy to address the priority issue in both users’ requests and providers’ resources. We propose an efficient priority tasks scheduling called MCPTS, where the priority is adjusted according to four tasks’ parameters including length, waiting time, deadline and burst time. MCPTS scheme consists of three sub-models such as tasks priority, task queueing priority and resources priority. A new hybrid multi-criteria decision-making (MCDM) method, namely ELECTRE III, and a meta-heuristic algorithm called differential evolution are proposed to evaluate and determine tasks’ priorities. Further, we introduce a novel dynamic priority-queue algorithm based on queueing model. Furthermore, we adjust dynamically the resources priority based on tasks priority model in order to design an efficient and flexible relation between both resources and tasks classes. The proposed algorithm is validated through the CloudSim simulator. The experimental results indicate the superiority of MCPTS algorithm compared to other existing algorithms. Also, it shows the effectiveness of our algorithm in providing good system performance, satisfying users’ priorities as well as QoS requirements, enhancing load balancing and improving resources utilization.

QRAS: efficient resource allocation for task scheduling in cloud computing

Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.

An execution environment as a service for adaptive long-running workflows

PurposeThis paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution.Design/methodology/approachLRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW.FindingsLONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs.Originality/valueThe original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

Improvising Dynamic Cloud Resource Allocation to Optimise QoS and Cost Effectiveness

Cloud computing offers streamlined instruments for outstanding business efficiency processes. Cloud distributors typically give two distinct forms of usage plans: Reserved as well as On-demand. Restricted policies provide inexpensive long-term contracting services, while order contracts were very expensive and ready for brief rather than long longer periods. In order to satisfy current customer demands with equal rates, cloud resources must be delivered wisely. Many current works depend mainly on low-cost resource-reserved strategies, which may be under-provisioning and over-provisioning rather than costly ondemand solutions. Since unfairness can cause enormous high availability costs and cloud demand variability in the distribution of cloud resources, resource allocation has become an extremely challenging issue. The hybrid approach to allocating cloud services according to complex customer orders is suggested in that article. The strategy was constructed as a two-step mechanism consisting of accommodation stages and then a versatile structure. In this way, by constructing each step primarily as an optimization problem, we minimize the total cost of implementation, thereby preserving service quality. By modeling client prerequisites as probability distributions are disseminated owing to the dubious presence of cloud requests, we set up a stochastic Optimization-based approach. Using various approaches, our technique is applied, and the results demonstrate its effectiveness when assigning individual cloud resources

Qualitative Simulation Algorithm for Resource Scheduling in Enterprise Management Cloud Mode

Aiming at the problem of resource scheduling optimization in enterprise management cloud mode, a customizable fuzzy clustering cloud resource scheduling algorithm based on trust sensitivity is proposed. Firstly, on the one hand, a fuzzy clustering method is used to divide cloud resource scheduling into two aspects: cloud user resource scheduling and cloud task resource scheduling. On the other hand, a trust-sensitive mechanism is introduced into cloud task scheduling to prevent malicious node attacks or dishonest recommendation from node providers. At the same time, in the cloud task scheduling, cloud resources are divided according to the comprehensive performance of resources, and the trust sensitivity coefficient of each type of task resources is calculated. Then, according to the trust sensitivity coefficient, the matching cloud tasks are selected for users. Through the comparison of simulation experiments, the customized fuzzy clustering cloud resource scheduling algorithm proposed in this paper reduces the user’s cost of selecting cloud service provider in the cloud resource scheduling. It not only embodies the principle of cloud resource allocation on demand but also can give full play to the advantages of cloud resources and improve the throughput of the whole cloud system and the satisfaction of cloud users.

Fair auctioning and trading framework for cloud virtual machines based on blockchain

Cloud auction provides a cost-effective approach to cloud resource allocation. Most existing cloud auction mechanisms simply assume that the auctioneer is trustable, and overlook the fairness of auctions. However, in fact, such a trustable auctioneer may not exist, and the fairness is non-trivial to guarantee. In this work, for the first time, we propose a decentralized auctioning and trading framework for cloud virtual machines based on blockchain. We realize both auction fairness and trade fairness among participants (e.g., cloud provider and cloud users), which guarantees each participant will not suffer any loss as long as it follows the protocol. Furthermore, we implement our system through the local analog network and Ethereum official test network, carry out experimental simulation, and demonstrate the feasibility of our system.

A latency-aware max-min algorithm for resource allocation in cloud

Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.

Enhancing cloud performance using task scheduling strategy based on resource ranking and resource partitioning

Cloud computing is one of the emerging models adopted in business organizations, IT Companies and mobile computing applications. Resources provisioned by cloud can be leased and utilized by clients remotely rather than purchasing it. An efficient allocation of resources is the key issue in the dynamic cloud environment due to the rapid increase in resource demand. Researchers in the literature have proposed various techniques which significantly improve the efficiency of cloud resource allocation, but still more ideal solutions are required. In this paper a resource allocation technique based on resource ranking and resource partitioning is proposed to enhance cloud performance and ensure QoS delivery. The proposed method achieved an efficient resource allocation in terms of response time and expected QoS delivery through resource ranking, resource partitioning and task classification. Simulation result of the proposed method outperforms the existing resource allocation methods.

Multi-level SLA violation-based resource allocation in cloud using kernel fuzzy C means with aid of EABC

Multi-level SLA violation-based resource allocation in cloud using kernel fuzzy C means with aid of EABC