A combinatorial double auction resource allocation model in cloud computing
TL;DR: The results proved that the combinatorial double auction-based resource allocation model is an appropriate market-based model for cloud computing because it allows double-sided competition and bidding on an unrestricted number of items, which causes it to be economically efficient.
About: This article is published in Information Sciences.The article was published on 2016-08-20. It has received 261 citations till now. The article focuses on the topics: Resource allocation & Combinatorial auction.
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TL;DR: This review investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches, observing that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes.
Abstract: There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.
118 citations
Cites background from "A combinatorial double auction reso..."
...[70] for the both user and cloud provider’s perception inefficient and intensive from....
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TL;DR: A novel fault-tolerant elastic scheduling algorithms for real-time tasks in clouds named FESTAL is designed, aiming at achieving both fault tolerance and high resource utilization in clouds, and an elastic resource provisioning mechanism is proposed for the first time.
Abstract: As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role However, few researches on the fault-tolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently To address this issue, this paper presents a fault-tolerant mechanism which extends the primary-backup model to incorporate the features of clouds Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel f ault-tolerant e lastic s cheduling algorithms for real-time ta sks in c l ouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, ie, N on- M igration-FESTAL (NMFESTAL), N on- O verlapping-FESTAL (NOFESTAL), and E lastic F irst F it (EFF) The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds
93 citations
Cites background or methods from "A combinatorial double auction reso..."
...g of hosts with different processing power Pi that is measured by Million Instructions per Second (MIPS, a widely used metric [15], [24], [25], [26]), representing the infinite heterogeneous computing resources in the cloud....
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...CDARA to optimize the resource allocation in clouds, which can generate higher revenues for both users and providers [15]....
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TL;DR: This paper uses a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem of service allocation in the cloud computing, and uses fuzzy set theory to specify the best compromise solution.
Abstract: Cloud computing is an emerging Internet-based computing paradigm, with its built-in elasticity and scalability. In cloud computing field, a service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, service allocation is one of the most challenging issues in the cloud environments. The problem of service allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem because MOPSO-CD is highly competitive in converging towards the Pareto front and generates a well-distributed set of non-dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi-objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.
91 citations
TL;DR: This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also, which reflects the usefulness of the method.
87 citations
TL;DR: A simplified model for task scheduling system in cloud computing based on game theory as a mathematical tool is established and the task scheduling algorithm considering the reliability of the balanced task is proposed.
79 citations
References
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Book•
01 Oct 1998TL;DR: The Globus Toolkit as discussed by the authors is a toolkit for high-throughput resource management for distributed supercomputing applications, focusing on real-time wide-distributed instrumentation systems.
Abstract: Preface Foreword 1. Grids in Context 2. Computational Grids I Applications 3 Distributed Supercomputing Applications 4 Real-Time Widely Distributed Instrumentation Systems 5 Data-Intensive Computing 6 Teleimmersion II Programming Tools 7 Application-Specific Tools 8 Compilers, Languages, and Libraries 9 Object-Based Approaches 10 High-Performance Commodity Computing III Services 11 The Globus Toolkit 12 High-Performance Schedulers 13 High-Throughput Resource Management 14 Instrumentation and Measurement 15 Performance Analysis and Visualization 16 Security, Accounting, and Assurance IV Infrastructure 17 Computing Platforms 18 Network Protocols 19 Network Quality of Service 20 Operating Systems and Network Interfaces 21 Network Infrastructure 22 Testbed Bridges from Research to Infrastructure Glossary Bibliography Contributor Biographies
7,569 citations
TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Abstract: Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.
4,570 citations
"A combinatorial double auction reso..." refers background in this paper
...CloudSim, a new generalized and extensible simulation framework, enabled seamless modeling, simulation, and experimentation of emerging cloud computing infrastructures and management services [25]....
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...In the cloud environment, the accessibility to the infrastructure incurred payments in real currency [25]....
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Posted Content•
TL;DR: This paper proposes CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments and allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.
Abstract: Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.
1,033 citations
"A combinatorial double auction reso..." refers methods in this paper
...The CloudSim simulation framework offered the following novel features [29]:...
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TL;DR: A computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments is proposed and some of the economic models in resource trading and scheduling are demonstrated using the Nimrod/G resource broker.
Abstract: The accelerated development in peer-to-peer and Grid computing has positioned them as promising next-generation computing platforms. They enable the creation of virtual enterprises for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply-and-demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod-G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker, with deadline and cost constrained scheduling for two different optimization strategies, on the World-Wide Grid testbed that has resources distributed across five continents.
961 citations
21 Jun 2009
TL;DR: CloudSim as mentioned in this paper is an extensible simulation toolkit that enables modelling and simulation of cloud computing environments, and it supports the creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs.
Abstract: Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.
898 citations