Applying economic principles to grids is deemed promising to improve the overall value provided by such systems. End users can influence the allocation of resources by reporting valuations for these resources. Current market-based schedulers, however, are static, assume the availability of complete information about jobs (in particular with respect to processing times), and do not make use of the flexibility offered by advanced computing systems. In this paper, we present the implementation of economic resource allocation principles into MOSIX, a state-of-the-art management system for computing clusters and multi-cluster organizational grids. The system is designed so as to be able to work in large-scale settings with selfish agents. Facing incomplete information about jobspsila characteristics, it dynamically allocates jobs to computing machines by leveraging preemption and job migration, two distinct features offered by MOSIX. We validate and showcase the behavior of our economic model by means of experiments in the real system.
"Cloud computing, which refers to services (hardware such as CPUs and storage, platform and application ) provisioning and consumption over the Internet in elastic approach, is becoming a hot topic both in academia and industry around the world. A modern compute cloud allows users to share the underlying computing resources (such as CPU, memory and networking bandwidth) in an elastic manner and thus cut down the costs of IT infrastructure,more and more companies has been migrating a huge amount of business into compute clouds. Typically, the price of a cloud computing resource consists of the following parts: P = P c + P s + P in + P out + P tran P c : the price of the compute instance. "
[Show abstract][Hide abstract] ABSTRACT: Cloud computing is a promising way providing users computing resources. These resources are provided by means of standard computing instances. Currently there are three price schemas including spot, reservation and on-demand in cloud market, but the average difference among these three pricing schema can be as much as 2.7 times even for the same instance type. In the premise of ensuring service level object, how many and which compute instance are needed, which price schema is needed is important for company. In this paper, an intelligent capacity planning model was proposed. Experimental result shows that our model can save more money while the quality of service does not degrade.
"However, zero cost migration is a common assumption in computer system analyses (cf. Karger et al. (1997); Amar et al. (2008a)). It can thus be hypothesized that the results also hold in realistic settings. "
[Show abstract][Hide abstract] ABSTRACT: Grid technologies and the related concepts of utility computing and cloud computing enable the dynamic sourcing of computer resources and services, thus allowing enterprises to cut down on hardware and software expenses and to focus on key competencies and processes. Resources are shared across administrative boundaries, e.g. between enterprises and/or business units. In this dynamic and inter-organizational setting, scheduling and pricing become key challenges. Market mechanisms show promise for enhancing resource allocation and pricing in grids. Current mechanisms, however, are not adequately able to handle large-scale settings with strategic users and providers who try to benefit from manipulating the mechanism. In this paper, a market-based heuristic for clearing large-scale grid settings is developed. The proposed heuristic and pricing schemes find an interesting match between scalability and strategic behavior.
European Journal of Operational Research 06/2010; 203(2):464-475. DOI:10.1016/j.ejor.2009.07.033 · 2.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Market-based resource allocation is expected to be an effective mechanism to allocate resources in a cloud computing environment, where the resources are virtualized and delivered to users as services. In this paper we propose a market mechanism to efficiently allocate multiple computation/storage services among multiple participants, or the Cloud Service Exchange. The proposed mechanism enables users (1) to order a combination of arbitrary services in a co-allocation or a workflow manner, and (2) to receive future/current services at the forward/spot market. Keyword Cloud computing, Grid computing, Scheduling, Market-based resource allocation, Combinational auctions
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