Topic
Service-level agreement
About: Service-level agreement is a research topic. Over the lifetime, 4358 publications have been published within this topic receiving 75333 citations. The topic is also known as: SLA.
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TL;DR: This work applies knowledge management to guarantee SLAs and low resource wastage in Clouds and designs and implements two methods, Case-Based Reasoning and rule-based approach, which prove feasibility as KM techniques and shows major improvements towards CBR.
115 citations
06 Jul 2009
TL;DR: An architecture for monitoring SLAs is proposed, which satisfies the two main requirements introduced by SLA establishment: the availability of historical data for evaluating SLA offers and the assessment of the capability to monitor the terms in a SLA offer.
Abstract: In modern service economies, service provisioning needs to be regulated by complex SLA hierarchies among providers of heterogeneous services, defined at the business, software, and infrastructure layers. Starting from the SLA Management framework defined in the SLA@SOI EU FP7 Integrated Project, we focus on the relationship between establishment and monitoring of such SLAs, showing how the two processes become tightly interleaved in order to provide meaningful mechanisms for SLA management. We first describe the process for SLA establishment adopted within the framework; then,we propose an architecture for monitoring SLAs, which satisfies the two main requirements introduced by SLA establishment: the availability of historical data for evaluating SLA offers and the assessment of the capability to monitor the terms in a SLA offer.
114 citations
TL;DR: A novel service cloud architecture is presented, and an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds that can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.
Abstract: Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.
114 citations
13 May 2012
TL;DR: This paper proposes an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA).
Abstract: Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.
113 citations
IBM1
TL;DR: This work considers consolidating virtual machines on the minimum number of physical containers in a cloud where the physical network may become a bottleneck, and models the problem as a Stochastic Bin Packing problem, where each virtual machine's bandwidth demand is treated as a random variable.
Abstract: Current trends in virtualization, green computing, and cloud computing require ever increasing efficiency in consolidating virtual machines without degrading quality of service. In this work, we consider consolidating virtual machines on the minimum number of physical containers (e.g., hosts or racks) in a cloud where the physical network (e.g., network interface or top of the rack switch link) may become a bottleneck. Since virtual machines do not simultaneously use maximum of their nominal bandwidth, the capacity of the physical container can be multiplexed. We assume that each virtual machine has a probabilistic guarantee on realizing its bandwidth Requirements-as derived from its Service Level Agreement with the cloud provider. Therefore, the problem of consolidating virtual machines on the minimum number of physical containers, while preserving these bandwidth allocation guarantees, can be modeled as a Stochastic Bin Packing (SBP) problem, where each virtual machine's bandwidth demand is treated as a random variable. We consider both offline and online versions of SBP. Under the assumption that the virtual machines' bandwidth consumption obeys normal distribution, we show a 2-approximation algorithm for the offline version and improve the previously reported results by presenting a (2 +∈)-competitive algorithm for the online version. We also observe that a dual polynomial-time approximation scheme (PTAS) for SBP can be obtained via reduction to the two-dimensional vector bin packing problem. Finally, we perform a thorough performance evaluation study using both synthetic and real data to evaluate the behavior of our proposed algorithms, showing their practical applicability.
113 citations