1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center

Zhu, Xiaoyun; Young, D. E.; Watson, Brian J.; Wang, Zhikui; Rolia, Jerry; Singhal, Sharad; Mckee, Bret; Hyser, Chris; Gmach, Daniel; Gardner, R.; Christian, Tom; Cherkasova, Ludmila

doi:10.1109/icac.2008.32

Cited by 109 publications

(71 citation statements)

References 13 publications

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“…DRS uses a user space application to monitor memory usage similar to Sandpiper, but unlike Sandpiper, it does not utilize application logs to respond directly to potential application service level violations or to improve placement decisions. 1000 Islands Project [35] aims to provide an integrated capacity and workload management for the next generation data centres. In the paper, the authors evaluate one loose integration policy for different controllers, while our paper provides a detailed performance study evaluating outcome of the three different integration policies and uses a set of novel QoS metrics.…”

Section: Related Workmentioning

confidence: 99%

Resource pool management: Reactive versus proactive or let’s be friends

et al. 2009

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The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools. We employ a trace-based workload placement controller that uses historical information to periodically and proactively reassign workloads to servers subject to their quality of service objectives. A reactive migration controller is introduced that detects server overload and underload conditions. It initiates the migration of workloads when the demand for resources exceeds supply. Furthermore, it dynamically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage. A host-load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner. A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately. The study considers trade-offs between i) required capacity and power usage, ii) resource access quality of service for CPU and memory resources, and iii) the number of migrations. Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management. The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

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Section: Related Workmentioning

confidence: 99%

Resource pool management: Reactive versus proactive or let’s be friends

et al. 2009

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“…A heuristic method is used to set the overload threshold of host, which is set to 85% of CPU utilization. Those resources are determined to overload if their loads exceed this threshold [13]. However, these static thresholds cannot meet need of the dynamic resource load in cloud computing.…”

Section: B Resource Load Evaluation and Energymentioning

confidence: 99%

A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing

Zuo

Shu

Dong

et al. 2015

Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustn

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Abstract-Cloud resource and its load have dynamic characteristics. To address this challenge, a dynamic self-adaptive evaluation method (termed SDWM) is proposed in this paper. SDWM uses some dynamic evaluation indicators to evaluate resource state more accurately. And it divides the resource load into three states -Overload, N ormal and Idle by the self-adaptive threshold. Then it migrates overload resources to balance load, and releases idle resources whose idle times exceed a threshold to save energy, which can effectively improve system utilization. Experimental results demonstrate SDWM has better adaptability than other similar methods when resources dynamically join or exit. This shows the positive effect of the dynamic self-adaptive threshold.

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“…Previous consolidation approaches can be broadly classified into static consolidation over long period of time (e.g., days, weeks) and dynamic consolidation over short period of time (e.g., hours). Zhu et al proposed an integrated control approach to managing workloads at different scopes and time scales [36]. Gmach et al [17], [16] proposed an integrated workload placement solution using both peak demand based workload assignment simulation and fuzzy logic based feedback control guided workload migration.…”

Section: Related Workmentioning

confidence: 99%

PAC: Pattern-driven Application Consolidation for Efficient Cloud Computing

Gong

2010

2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-To reduce cloud system resource cost, application consolidation is a must. In this paper, we present a novel patterndriven application consolidation (PAC) system to achieve efficient resource sharing in virtualized cloud computing infrastructures. PAC employs signal processing techniques to dynamically discover significant patterns called signatures of different applications and hosts. PAC then performs dynamic application consolidation based on the extracted signatures. We have implemented a prototype of the PAC system on top of the Xen virtual machine platform and tested it on the NCSU Virtual Computing Lab. We have tested our system using RUBiS benchmarks, Hadoop data processing systems, and IBM System S stream processing system. Our experiments show that 1) PAC can efficiently discover repeating resource usage patterns in the tested applications; 2) Signatures can reduce resource prediction errors by 50-90% compared to traditional coarse-grained schemes; 3) PAC can improve application performance by up to 50% when running a large number of applications on a shared cluster.

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1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center

Cited by 109 publications

References 13 publications

Resource pool management: Reactive versus proactive or let’s be friends

Resource pool management: Reactive versus proactive or let’s be friends

A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing

PAC: Pattern-driven Application Consolidation for Efficient Cloud Computing

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