Performance-Based Service Differentiation in Clouds

Lakew, Ewnetu Bayuh; Klein, Cristian; Hernández-Rodríguez, Francisco; Elmroth, Erik

doi:10.1109/ccgrid.2015.145

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…The RUBiS client runs on a machine with the same experimental hardware setup described in Section 5.1. A control interval of 15 seconds is used for the Optimizer as this is short enough to adapt the underlying infrastructure upon quickly workload changes and sufficiently long to observe the effects of the re‐configuration . The scheduling decision is made whenever the FPGA completes a subtask (roughly every 60 seconds).…”

Section: Discussionmentioning

confidence: 99%

“…16 The performance model, which is based on our previous work, 11 represents the relationship between the throughput of application i and the number of cores assigned as well as the frequency of the VM. The model is used to estimate the resource required by an application to meet its performance targets.…”

Section: Performance Modelmentioning

confidence: 99%

“…Similarly, for applications with other characteristics, creating alternative system models from representative benchmarks and updating the models online is a promising approach. 16 The scheduling decision is made whenever the FPGA completes a subtask (roughly every 60 seconds). † We use the features available in this tool such as think-time and number of concurrent users to stress-test the system.…”

Section: Applications and Workloadmentioning

confidence: 99%

“…The goal is to continuously adjust resource allocations to applications in order to compensate for changes in workload and resource requirements in the real system. 16 The performance model, which is based on our previous work, 11 represents the relationship between the throughput of application i and the number of cores assigned as well as the frequency of the VM. More precisely, the performance of application i is modeled as…”

Section: Performance Modelmentioning

confidence: 99%

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Power and performance optimization in FPGA‐accelerated clouds

Tesfatsion

Proaño

Tomás

et al. 2018

Concurrency and Computation

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Energy management has become increasingly necessary in data centers to address all energy-related costs, including capital costs, operating expenses, and environmental impacts.Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field-Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy-aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data-intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance-energy ratio on a mix of multimedia and e-commerce applications.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Modelmentioning

confidence: 99%

Section: Applications and Workloadmentioning

confidence: 99%

Section: Performance Modelmentioning

confidence: 99%

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Power and performance optimization in FPGA‐accelerated clouds

Tesfatsion

Proaño

Tomás

et al. 2018

Concurrency and Computation

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“…With the diversification of users requirements for network Quality of Service (QoS), service differentiation has been studied for decades under different disciplines, such as processor sharing, packet switching, storage systems and Web servers [8]. A key challenge for service differentiation is how to satisfy different users.…”

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

Xue¹,

Jin²,

Wang³

2018

TIIS

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Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

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Mining association rules for admission control and service differentiation in e‐commerce applications

Xue

Jarvis²

2018

WIREs Data Min & Knowl

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Workload demands in e-commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximize total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e-commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation, and priority scheduling. Our approach takes the following aspects into consideration: (a) only final purchase requests result in company revenue; (b) any other request can potentially lead to final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; (c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre-computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximization.

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Performance-Based Service Differentiation in Clouds

Cited by 19 publications

References 25 publications

Power and performance optimization in FPGA‐accelerated clouds

Power and performance optimization in FPGA‐accelerated clouds

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

Mining association rules for admission control and service differentiation in e‐commerce applications

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