Workload Analysis and Demand Prediction of Enterprise Data Center Applications

Gmach, Daniel; Rolia, Jerry; Cherkasova, Ludmila; Kemper, Alfons

doi:10.1109/iiswc.2007.4362193

Cited by 241 publications

(163 citation statements)

References 17 publications

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“…Finally, a workload pattern analysis (following the methodology introduced in [4]) is conducted. Figure 7 gives a summary of the pattern lengths for the 138 workloads.…”

Section: Workload Characteristicsmentioning

confidence: 99%

“…Though enterprise application workloads often have time varying loads that behave according to patterns [3,4], actual demands are statistical in nature and are likely to differ from predictions. Therefore, to further improve the efficiency and application quality of service of our approach, we manage workloads by integrating the workload placement controller with a reactive workload migration controller that observes current behaviour to i) migrate workloads off of overloaded servers and ii) free and shut down lightly-loaded servers.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Finally, a workload pattern analysis (following the methodology introduced in [4]) is conducted. Figure 7 gives a summary of the pattern lengths for the 138 workloads.…”

Section: Workload Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2009

Self Cite

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“…Besides, there are networks where the demand of resources and services can be predicted for optimisation tasks. Thus, application workloads in enterprises are predicted in [15] considering performance modelling and capacity planning, according to demand patterns in order to design recommendation services.…”

Section: Related Workmentioning

confidence: 99%

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

et al. 2017

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Some maintenance tasks in Wi-Fi networks may involve removing an access point due to several reasons. As a result, the new infrastructure registers a different number of roamings in the access points according to the users' behaviour, with a certain energy impact added to the consumption caused by the own operations of the devices. This energy effect should be understood in order to tackle the measures aimed at planning the infrastructure deployment. In this work, we propose a methodology to predict the energy consumption in the access points of a Wi-Fi network when we remove a particular device, based on a twofold support. We predict the number of roamings following a method previously validated; on the other hand, we assess the relationship between roamings and energy in the full infrastructure, using the data collected from a high number of network users during a given time in order to reflect the users' behaviour with the maximum accuracy. From this knowledge, we can infer the energy prediction for a different environment where the roamings are predicted using techniques based on recommender systems and machine learning.

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“…the Kalman Filter [18],) or studies (e.g. [19] [20]) that have been conducted can be employed if necessary. The length of T is a pre-defined constant that is decided by several factors, e.g.…”

Section: System Models and Problem Formulationmentioning

confidence: 99%

Profit Aware Load Balancing for Distributed Cloud Data Centers

Liu

Ren

Quan

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-The advent of cloud systems has spurred the emergence of an impressive assortment of Internet services. Recent pressures on enhancing the profitability by curtailing surging dollar costs on energy have posed challenges to, as well as placed a new emphasis on, designing energy-efficient request dispatching and resource management algorithms. What further adds to the design challenge is the highly diverse nature of Internet service requests in terms of Quality-of-Service (QoS) constraints and business values. Nonetheless, most of the existing job scheduling and resource management solutions are for a single type of request and are profit oblivious. They are unable to reap the benefit of multi-service profit-aware algorithm designs.In this paper, we consider a cloud service provider operating geographically distributed data centers in a multi-electricitymarket environment, and propose an energy-efficient, profit-and cost-aware request dispatching and resource allocation algorithm to maximize a service provider's net profit. We formulate the net profit maximization issue as a constrained optimization problem, using a unified task model capturing multiple cloud layers (e.g., SaaS, PaaS, IaaS.) The proposed approach maximizes a service provider's net profit by judiciously distributing service requests to data centers, powering on/off an appropriate number of servers, and allocating server resources to dispatched requests. We conduct extensive experiments to validate our proposed algorithm. Results show that our proposed approach can improve a service provider's net profit significantly. I. INTRODUCTIONWith the development of cloud computing, service providers are able to provide a variety of complex applications and services to people's daily lives, such as Google Docs and AppEngine, Amazon EC2 and S3, etc. These applications and services are all supported by service provider's data centers and delivered to a wide range of clients over the Internet.The large number of service requests drastically increases not only the need for data centers, but also the scale of data centers and their energy consumptions. The dollar cost spent on energy consumption takes a large portion of a service provider's operational cost annually. As an example, Google has more than 500K servers and it consumes more than $38M worth of electricity each year. Similarly, Microsoft has more than 200K servers and spends more than $36M on electricity annually [1]. Evidently, dollar costs on energy consumptions have been a critical part in operational cost for service providers. It is fair to say that an efficient computing resource management approach for distributed cloud data centers is essential to service providers.

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Workload Analysis and Demand Prediction of Enterprise Data Center Applications

Cited by 241 publications

References 17 publications

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

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

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

Profit Aware Load Balancing for Distributed Cloud Data Centers

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