Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment

Yeo, Sungkap; Lee, Hsien-Hsin S.

doi:10.1109/mc.2011.96

Cited by 53 publications

(8 citation statements)

References 6 publications

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“…A survey on forecasting and profiling models for cloud applications can be found in [55]. An important aspect related to the workload is to consider the dynamic nature of the cloud, which can be caused by performance variation of machine instances offering the same capability, and by services that are deployed, updated and destroyed all the time giving rise to a dynamic competition for shared resources [11,12]. CDFAs on the cloud are complex systems where customers and resources have not identical characteristics , and exponential distribution does not adequately model observed inter-arrival and service times.…”

Section: Cloud Native Applicationsmentioning

confidence: 99%

“…In these cases, we can use approximate methods to compute performance bounds, and complemented with simulation [9,56,10]. In addition to the mean service time and mean inter-arrival time, the coefficient of variation of resources and inter-arrival time has been proposed to introduce the dynamic nature of cloud applications and streaming applications on the cloud [11,57,10,31]. In our case, profiling data is essential to feed our models with time distribution annotations to estimate bounds.…”

Section: Cloud Native Applicationsmentioning

confidence: 99%

“…However, if the service times are not exponential, it can be complex and it relies on a set of approximations. Such approximations are sensitive to the probability distribution of service times and they even become increasingly inaccurate when the Coefficient of Variation (CoV) increases towards the value of 1 [11,10,12]. The CoV is a measure of dispersion of a probability distribution.…”

Section: Quantitative Analysis Based On Simulationmentioning

confidence: 99%

“…The proximity of these points shows the accuracy of simulations. Assuming the computing nodes in the cloud are heterogenous and that the performance capabilities of these computing nodes are uniformly distributed [11] between the time of the faster node and the time of the slower node, we conducted different simulations in Renew. Service time CoV ranges in simulations from 0 to 0,5 with uniform and normal distributions.…”

Section: Quantitative Analysis Based On Simulationmentioning

confidence: 99%

“…On the other hand, cloud infrastructures are particularly well-suited for the computations of such applications due to their properties and they have typically represented the technology of choice for the aforementioned current framework projects. In particular, our approach considers the following properties of the cloud: (i) on-demand resource provisioning, (ii) the dynamism of the cloud execution environment caused by performance variation of machines, services competing for shared resources, and changing user quality of services requirements [11,12]. The on-demand resource provisioning characteristic enables us to configure and manage the computational cloud resources so that one of the task-to-resource mapping solutions analysed can be deployed.…”

Section: Introductionmentioning

confidence: 99%

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Model-driven development of data intensive applications over cloud resources

Tolosana-Calasanz

Bañares

Colom

2018

Future Generation Computer Systems

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The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and lowlatency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array.

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Section: Cloud Native Applicationsmentioning

confidence: 99%

Section: Cloud Native Applicationsmentioning

confidence: 99%

Section: Quantitative Analysis Based On Simulationmentioning

confidence: 99%

Section: Quantitative Analysis Based On Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-driven development of data intensive applications over cloud resources

Tolosana-Calasanz

Bañares

Colom

2018

Future Generation Computer Systems

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An adaptive replica placement approach for distributed key‐value stores

Filho

Cavalcante

Moreira

et al. 2020

Concurrency and Computation

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The use of distributed key-value stores (KVS) has experienced fast adoption by various applications in recent years due to key advantages such as hypertext transfer protocol-based RESTful application programming interface, high availability and elasticity. Due to great scalability characteristics, KVS systems commonly use consistent hashing as data placement mechanism. Although KVS systems offer many advantages, they were not designed to dynamically adapt to changing workloads which often include data access skew. Furthermore, the underlying physical storage nodes may be heterogeneous and do not expose their performance capabilities to higher level data placement layers. In this paper, we address those issues and propose an essential step toward a dynamic autonomous solution by leveraging deep reinforcement learning.We design a self-learning approach that incrementally changes the data placement, improving the load balancing. Our approach is dynamic in the sense that is capable of avoiding hot spots, that is, overloaded storage nodes when facing different workloads.Also, we design our solution to be pluggable. It assumes no previous knowledge of the storage nodes capabilities, thus different KVS deployments may make use of it. Our experiments show that our method performs well on changing workloads including data access skew aspects. We demonstrate the effectiveness of our approach through experiments in a distributed KVS deployment. K E Y W O R D Sconsistent hashing, deep reinforcement learning, key-value stores, load balancing, replica placement INTRODUCTIONDistributed key-value stores (KVS) are a well-established approach for cloud data-intensive applications 1 mainly because they are capable of successfully managing huge data traffic driven by the explosive growth of different applications such as social networks, e-commerce, and enterprise.In this work, the focus is on a particular type of KVS, also known as Object Store, which can store and serve any type of data (eg, photo, image, and video). 2 Object Store such as Dynamo 3 and OpenStack-Swift 4 have become widely accepted due to its scalability, high capacity, cost-effective storage and reliable REST programming interface. These systems take advantage of peer-to-peer architecture 5 and replication techniques in order to guarantee high availability and scalability. The data placement of KVS systems are commonly based on a distributed hash table (DHT) and consistent hashing (CHT) 6 with virtual nodes. Consistent hashing is a type of hashing that minimizes the amount of data that needs to move when adding or removing storage nodes. Using only the hash of the id of the data one can determine exactly where that data should be. This mapping of hashes to locations is usually known as "ring. " While this strategy provides item-balancing guarantees, it may be not very efficient in balancing the actual workload of the system for the following reasons.Concurrency Computat Pract Exper. 2020;32:e5675. wileyonlinelibrary.com/journal/cpe

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Resource allocation in cloud virtual machines based on empirical service traces

Lin

Chen

et al. 2013

Int J Communication

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SUMMARYOne of the key technologies in cloud computing is virtualization. Using virtualization, a system can optimize usage of resources, simplify management of infrastructure and software, and reduce hardware requirements. This research focuses on infrastructure as a service, resource allocation by providers for consumers, and explores the optimization of system utilization based on actual service traces of a real world cloud computing site. Before activating additional virtual machines (VM) for applications, the system examines CPU usage in the resource pools. The behavior of each VM can be estimated by monitoring the CPU usage for different types of services, and consequently, additional resources added or idle resources released. Based on historical observations of the required resources for each kind of service, the system can efficiently dispatch VMs. The proposed scheme can efficiently and effectively distribute resources to VMs for maximizing utilization of the cloud computing center. Copyright © 2013 John Wiley & Sons, Ltd.

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Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment

Cited by 53 publications

References 6 publications

Model-driven development of data intensive applications over cloud resources

Model-driven development of data intensive applications over cloud resources

An adaptive replica placement approach for distributed key‐value stores

Resource allocation in cloud virtual machines based on empirical service traces

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