A multi-attribute service portfolio design problem

Knapper, Rico; Flath, Christoph M.; Sailer, Anca; Weinhardt, Christof

doi:10.1109/soca.2011.6166207

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The basis of our service broker's economic model in Chapter 6 is the utility-based algorithm, which is adopted from multi-attribute auction theory [37], to describe user preferences as a function of weighted SLA attributes taking into account the user's payment willingness. This economic model is well-established in literature in the context of web services for the optimisation of service value networks [113] and for the service portfolio design [109]. It has also been applied in the context of smart grids [80] for the evaluation of variable electricity rates in order to achieve efficient matching of electricity consumer needs and generator capabilities.…”

Section: Sla Management Methodsmentioning

confidence: 99%

A Cloud Controller for Performance-Based Pricing

Lucanin

Pietri²,

Brandić³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

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Abstract-New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when performing actions such as CPU frequency scaling. Although existing cloud controllers for managing cloud resources deploy frequency scaling, they only consider fixed virtual machine (VM) pricing. In this paper we propose a performance-based pricing model adapted for VMs with different CPU-boundedness properties. We present a cloud controller that scales CPU frequencies to achieve energy cost savings that exceed service revenue losses. We evaluate the approach in a simulation based on real VM workload, electricity price and temperature traces, estimating energy cost savings up to 32% in certain scenarios. I. INTRODUCTIONWith the wide range of VM types, heterogeneous infrastructure and different computing environments including VMs and containers [1], estimating the performance of provisioned resources is becoming increasingly challenging [2]. New cloud pricing schemes are emerging where resources are priced based on the delivered performance. For example, the CPU frequency provided to a VM at runtime determines the price [3], with high CPU frequencies being more expensive. We call this model performance-based pricing and it is used in production by cloud providers, such as ElasticHosts [4]. Though this approach mainly targets users, it could also be used by cloud providers to control their energy consumption. Energy consumption of data centers is becoming a major issue, accounting for 1.5% of global electricity usage [5]. Furthermore, modern clouds may consist of geographically-distributed data centers influenced by dynamic local factors, such as real-time electricity prices [6] and temperature-dependent cooling [7], that we call geotemporal inputs.We call the subsystem of the cloud, that determines the actions to allocate and manage the VMs, a cloud controller. Adapting the cloud controller to geotemporal inputs through actions, such as CPU frequency scaling, raises a trade-off problem between the potential energy savings and service revenue losses incurred under performance-based pricing. This is the challenge at the core of this paper.Frequency scaling is a power management technique commonly used to lower the operating frequency of hardware resources in order to reduce power consumption [8]. However, frequency reduction may degrade the performance of resources. Depending on the workload characteristics, workload

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Section: Sla Management Methodsmentioning

confidence: 99%

A Cloud Controller for Performance-Based Pricing

Lucanin

Pietri²,

Brandić³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

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show abstract

“…The lack of interoperability across cloud providers, their costs and SLAs hindered consumers' ability to deploy their services on multiple Clouds [34]. In this chapter, we address research questions as specified in Chapter-1 then present a broker-based framework to assist consumers selecting and deploying their services on multi-Cloud environments with respect to their needs in terms of SLA, location and context awareness.…”

Section: Design Assumptions Of Cloud Service Brokermentioning

confidence: 99%

Design and Implementation of a Framework for Multi-Cloud Service Broker

Ullah¹

2021

Preprint

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Cloud service broker (CSB) as an emerging technology intermediates heterogeneous multiple cloud services for both the providers and consumers. Recently, Cloud computing & mobile cloud computing applications (MCA) have gained an enormous popularity, which has led to an increasing need for the development of platform independent Middleware/CSB to support all types of cloud service consumer applications including x86*x64 based standard OS & ARM based mobile applications, web browsers, etc. Developing Platform Independent Hybrid CSB, however, is not an easy task. Developers have to deal with difficulties inherent from the different cloud controllers, cloud service providers environments, clients’ application types, network connection types (wired, wireless), GPS (Global Positioning Systems) information of cloud resources and clients’ etc. In this thesis, the proposed design of a middleware/CSB that abstracts the real-time resources of various clouds (private, public, home, Local) and stores the resources in its own Database. It will also store clients requests then analyzes the request to find the nearest available servers which is running the appropriate applications. Then the CSB will forward the destination servers information to the clients. Thesis goal is to achieve context awareness, location awareness, platform independence, portability, efficiency, and usability. Portability is achieved by following the J2ME platform specifications. The middleware has been implemented and tested on a real time Openstack cloud using by our newly designed Android Clients and platform independent Mozilla Firefox browser. The performance measurements of the middleware show that it achieves its efficiency requirements. Furthermore, the middleware’s database can be used for resource algorithm, pattern analysis, and for future requirements.

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An optimization approach for the design of time-of-use rates

Flath

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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A multi-attribute service portfolio design problem

Cited by 4 publications

References 21 publications

A Cloud Controller for Performance-Based Pricing

A Cloud Controller for Performance-Based Pricing

Design and Implementation of a Framework for Multi-Cloud Service Broker

An optimization approach for the design of time-of-use rates

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