Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Zhan, Jianfeng; Wang, Lei; Li, Xiaona; Shi, Wei; Weng, Chuliang; Zhang, Wenyao; Zang, Xiutao

doi:10.1109/tc.2012.103

Cited by 50 publications

(26 citation statements)

References 48 publications

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“…Therefore, we believe that our accurate DVFS control will play a more important role in power saving when the concept of DVFS is extended to the other system components. [35] unreferenced paper Potential extension. In this paper, we only provide a prototype implementation of leveraging online request tracing for accurate DVFS control.…”

Section: Discussionmentioning

confidence: 99%

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

Lü

Zhan

Wang

et al. 2015

IEEE Trans. Comput.

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As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, called PowerTracer, for diagnosing energy inefficiency and saving power. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy inefficiency diagnosis and power saving in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop (1) a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency, and (2) a power saving method that enables dynamic voltage and frequency scaling (DVFS) with online request tracing. We implement a prototype of PowerTracer, and conduct extensive experiments to validate its effectiveness. Our tool analyzes several state-of-thepractice and state-of-the-art DVFS control policies and uncovers existing energy inefficiencies. Meanwhile, the experimental results demonstrate that PowerTracer outperforms its peers in power saving.

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Section: Discussionmentioning

confidence: 99%

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

Lü

Zhan

Wang

et al. 2015

IEEE Trans. Comput.

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“…A game theory based data collection method was also proposed to maximize the energy efficiency with fairness constraints in [28]. To cooperatively schedule resource, a self-adaptive strategy has been proposed for the evolution of cooperation in distributed networks in [29], and cost-aware cooperative resource provisioning for heterogeneous workloads has been studied in [30]. To increase the in-field time of a drone swarm as a sensing grid that collects heterogeneous data, we have to design an adaptive optimization method to cooperatively balance the memory and energy capacities in drone swarms so that the workload of heavy-duty drones can be delegated to others with more resources.…”

Section: Challenges and Related Workmentioning

confidence: 99%

ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Arkhipov

Kim

et al. 2016

IEEE Trans. Comput.

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Abstract-We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocals in both single-swarm and multi-swarm cases.

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“…Here focus is put more on web applications and their scalability. In paper [2], the author discusses about the grouping of homogeneous data and processes the same in chunks. The application workloads which are of similar types have been categorized under one group and are termed as homogeneous workload.…”

Section: Literature Survey 21 Existing Systemsmentioning

confidence: 99%

“…The application owner can ideally scale up the resources if the workload of a service increases (e.g. more end users start submitting requests at the same time) and thus used to maintain the Quality of Service (QoS) of their service [2]. They can also scale down the resources used when the workload eases down.…”

Section: Introductionmentioning

confidence: 99%

Impact of Elasticity on Cloud Systems

Baruah¹,

Mohanpurkar²

2015

IJCA

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Today's computing world and application market is dominated by cloud technology. Cloud computing provides a powerful computing paradigm and deliver services over the network and has emerged as a new enterprise model. With Cloud computing, the service providers can provide ondemand services to users as needed. In cloud systems, enormous resources are involved and computations are done at a very vast scale which enables users to access huge amount of resources on demand. But there is uncertainty of the demand of cloud resources by the end users as it can vary depending on the time. Also it becomes costly affair in maintaining sufficient resources to meet peak resource requirements all the time. This is where dynamic scalability or elasticity comes into picture. Elasticity of cloud is very necessary as it allows the servers to resize the virtual machine deployed in the system and thereby fulfilling the requirement of new resources. Elasticity can be considered as the next great achievement which is getting much focus. In this paper, an effort has been put to analyze the impact of elasticity on cloud systems and how it will benefit the Cloud implementers to improve the systems performance and reduce the operation cost.

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Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Cited by 50 publications

References 48 publications

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Impact of Elasticity on Cloud Systems

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