An online auction mechanism for time-varying multidimensional resource allocation in clouds
TL;DR: This work proposes a novel integer programming model for the time-varying multidimensional resource allocation problem and designs a truthful online auction mechanism for resource allocation in a competitive environment and proves that the mechanism is truthful and individual rationality.
About: This article is published in Future Generation Computer Systems.The article was published on 2020-10-01. It has received 39 citations till now. The article focuses on the topics: Resource allocation & Resource (project management).
Citations
More filters
TL;DR: Simulation results show that optimizing joint task scheduling and VM placement leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization.
43 citations
TL;DR: Wang et al. as discussed by the authors proposed two auction mechanisms for the blockchain network formed by edge computing service providers and miners to maximize the social welfare, and the experiments show that the proposed auction mechanism can effectively maximize social welfare in blockchain network and provide an effective resource allocation strategy for edge Computing service providers.
18 citations
TL;DR: This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model and introduces an auction mechanism to solve the problem of resource competition among the anchor users in live services.
Abstract: Cloud computing is characterized by strong computing and storage capabilities, and edge computing has the advantages of low latency and low power consumption. Many service providers have begun to combine the advantages of cloud and edge computing to provide better quality of service, but the heterogeneity of cloud and edge computing represents a challenge for service deployment and resource allocation. This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model. Additionally, we introduce an auction mechanism to solve the problem of resource competition among the anchor users in live services. By solving the IP resource allocation problem and Vickrey–Clarke–Groves mechanism, we obtain the optimal resource allocation mechanism. Based on the dominant resource proportion and matching model, we design a resource allocation mechanism for the online environment. These mechanisms can be used for reservation and live webcast scenarios. Furthermore, we prove that the two mechanisms have individual rationality and truthfulness. Our approach is characterized by high social welfare, high resource utilization and a short execution time.
18 citations
TL;DR: A comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment is presented to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations.
Abstract: Resource management (RM) is a challenging task in a cloud computing environment where a large number of virtualized, heterogeneous, and distributed resources are hosted in the datacentres. The uncertainty, heterogeneity, and the dynamic nature of such resources affect the efficiency of provisioning, allocation, scheduling, and monitoring tasks of RM. The most existing RM techniques and strategies have insufficiency in handling such cloud resources dynamic behaviour. To resolve these limitations, there is a need for the design and development of intelligent and efficient autonomic RM techniques to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations. This paper presents a comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment. The taxonomy classifies the existing autonomic and elastic RM techniques into different categories based on their design, objective, function, and applications. Moreover, a comparison and qualitative analysis is provided to illustrate their strengths and weaknesses. Finally, the open issues and challenges are highlighted to help researchers in finding significant future research options.
11 citations
TL;DR: A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented.
Abstract: Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.
11 citations
References
More filters
TL;DR: A set of techniques are developed that allow constructing efficiently computable truthful mechanisms for combinatorial auctions in the special case where each bidder desires a specific known subset of items and only the valuation is unknown by the mechanism (the single parameter case).
312 citations
16 Jun 2014
TL;DR: This work represents the first online combinatorial auction designed in the cloud computing paradigm, which is general and expressive enough to both optimize system efficiency across the temporal domain instead of at an isolated time point, and model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice.
Abstract: 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.
165 citations
TL;DR: Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.
Abstract: We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no coalition behavior of misreporting resource demands can benefit all its members. DRFH also ensures some level of service isolation among the users. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.
135 citations
TL;DR: This work forms the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources and designs truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments.
Abstract: A major challenging problem for cloud providers is designing efficient mechanisms for virtual machine (VM) provisioning and allocation. Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. Recently, cloud providers have introduced auction-based models for VM provisioning and allocation which allow users to submit bids for their requested VMs. We formulate the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources. We then design truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to manipulate the system by lying about their requested bundles of VM instances and their valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanisms. Our proposed mechanisms achieve promising results in terms of revenue for the cloud provider.
132 citations
TL;DR: An auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources is designed and it is proved that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests.
Abstract: Cloud providers provision their various resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.
129 citations