Proceedings Article•
Elasticity in Cloud Computing: What It Is, and What It Is Not.
01 Jan 2013-pp 23-27
TL;DR: A precise definition of elasticity is proposed and its core properties and requirements explicitly distinguishing from related terms such as scalability and efficiency are analyzed.
Abstract: Originating from the field of physics and economics, the term elasticity is nowadays heavily used in the context of cloud computing. In this context, elasticity is commonly understood as the ability of a system to automatically provision and deprovision computing resources on demand as workloads change. However, elasticity still lacks a precise definition as well as representative metrics coupled with a benchmarking methodology to enable comparability of systems. Existing definitions of elasticity are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related terms such as scalability and efficiency; the proposed measurement methodologies do not provide means to quantify elasticity without mixing it with efficiency or scalability aspects. In this short paper, we propose a precise definition of elasticity and analyze its core properties and requirements explicitly distinguishing from related terms such as scalability and efficiency. Furthermore, we present a set of appropriate elasticity metrics and sketch a new elasticity tailored benchmarking methodology addressing the special requirements on workload design and calibration.
Citations
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TL;DR: This paper outlines a conceptual framework for cloud resource management and uses it to structure the state-of-the-art review, and identifies five challenges for future investigation that relate to providing predictable performance for cloud-hosted applications.
Abstract: Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .
506 citations
Cites background from "Elasticity in Cloud Computing: What..."
...[102] discuss elasticity in detail and propose an alternative definition)....
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TL;DR: This article provides an overview of containerization, a new technological trend in lightweight virtualization, and provides a taxonomy of elasticity mechanisms according to the identified works and key properties.
Abstract: Elasticity is a fundamental property in cloud computing that has recently witnessed major developments This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization It also discusses major issues and research challenges related to elasticity in cloud computing We comprehensively review and analyze the proposals developed in this field We provide a taxonomy of elasticity mechanisms according to the identified works and key properties Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers
272 citations
Cites background from "Elasticity in Cloud Computing: What..."
...increasing workloads by making use of additional resources [5], it is time independent and it is similar to the provisioning state in elasticity but the time has no effect on the system...
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TL;DR: In this paper, the authors point out the influence and strong impact of the extended cloud (i.e., the MEC and fog) on existing communication and networking service models of the cloud, and examine the technologies that implement these models and architectures, and analyze them with respect to security and resilience requirements.
Abstract: Mobile edge computing (MEC) and fog are emerging computing models that extend the cloud and its services to the edge of the network. The emergence of both MEC and fog introduce new requirements, which mean their supported deployment models must be investigated. In this paper, we point out the influence and strong impact of the extended cloud (i.e., the MEC and fog) on existing communication and networking service models of the cloud. Although the relation between them is fairly evident, there are important properties, notably those of security and resilience, that we study in relation to the newly posed requirements from the MEC and fog. Although security and resilience have been already investigated in the context of the cloud-to a certain extent-existing solutions may not be applicable in the context of the extended cloud. Our approach includes the examination of models and architectures that underpin the extended cloud, and we provide a contemporary discussion on the most evident characteristics associated with them. We examine the technologies that implement these models and architectures, and analyze them with respect to security and resilience requirements. Furthermore, approaches to security and resilience-related mechanisms are examined in the cloud (specifically, anomaly detection and policy-based resilience management), and we argue that these can also be applied in order to improve security and achieve resilience in the extended cloud environment.
228 citations
TL;DR: A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively.
Abstract: Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.
190 citations
Cites background from "Elasticity in Cloud Computing: What..."
...In other words, resources in cloud computing are almost unlimited for users and can be leased in any amount at any time [22], while...
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TL;DR: This paper summarizes the outcomes of a systematic mapping study analyzing research papers covering “cloud-native” topics, research questions and engineering methodologies and provides a definition for the term “ cloud-native application” which takes all findings, insights of analyzed publications and already existing and well-defined terminology into account.
172 citations
Cites background from "Elasticity in Cloud Computing: What..."
...Of course this definition can only be understood in a context of further terms 580 already defined or characterized by other authors and standardizing initiatives: • Elasticity is ”the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible” and has been already defined by 585 [69]....
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References
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28 Sep 2011
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
15,145 citations
TL;DR: This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
Abstract: The problem of indexing time series has attracted much interest. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry and finance. Unfortunately, however, DTW does not obey the triangular inequality and thus has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques or abandoned the idea of indexing and concentrated on speeding up sequential searches. In this work, we introduce a novel technique for the exact indexing of DTW. We prove that our method guarantees no false dismissals and we demonstrate its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
1,925 citations
"Elasticity in Cloud Computing: What..." refers methods in this paper
...As an alternative to these metrics, the dynamic time warping (DTW) distance [7] can be used as a robust distance metric to capture the similarity between the demand and supply curves as well as to approximate the technical reaction time of the adaptation mechanism....
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Book•
01 Jan 1974
TL;DR: The aim of this book is to provide a Discussion of the Foundations of Optimal Control Theory and its Applications in Economics, as well as some examples of models used in this area.
Abstract: PART 1 Introduction Chapter 1: The Nature of Mathematical Economics Chapter 2: Economic Models PART 2 Static (or Equilibrium) Analysis Chapter 3: Equilibrium Analysis in Economics Chapter 4: Linear Models and Matrix Algebra Chapter 5: Linear Models and Matrix Algebra (continued) PART 3 Comparative-Static Analysis Chapter 6: Comparative Statics and the Concept of the Derivative Chapter 7: Rules of Differentiation and their use in Comparative Statics Chapter 8: Comparative-Static Analysis of General-Function Models PART 4 Optimization Problems Chapter 9: Optimization: A Special Variety of Equilibrium Analysis Chapter 10: Exponential and Logarithmic Functions Chapter 11: The Case of More Than One Choice Variable Chapter 12: Optimization with Equality Constraints NEW Chapter 13: Further Topics in Optimization (includes Envelope Theorem and Duality PART 5 Dynamic Analysis Chapter 14 Economic Analysis and Integral Calculus Chapter 15 Continuous Time: First Order Differential Equations Chapter 16 Higher-Order Differential Equations Chapter 17 DiscreteTime: First Order Difference Equations Chapter 18 Higher Order Difference Equations Chapter 19 Simultaneous Differential Equations and Difference Equations NEW Chapter 20: Introduction to Optimal Control Theory
1,361 citations
TL;DR: A scalability metric based on cost-effectiveness, where the effectiveness is a function of the system's throughput and its quality of service is presented, which gives insight into the scaling capacity of the designs, and into how to improve the design.
Abstract: Many distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on cost-effectiveness, where the effectiveness is a function of the system's throughput and its quality of service. It is part of a framework which also includes a sealing strategy for introducing changes as a function of a scale factor, and an automated virtual design optimization at each scale factor. This is an adaptation of concepts for scalability measures in parallel computing. Scalability is measured by the range of scale factors that give a satisfactory value of the metric, and good scalability is a joint property of the initial design and the scaling strategy. The results give insight into the scaling capacity of the designs, and into how to improve the design. A rapid simple bound on the metric is also described. The metric is demonstrated in this work by applying it to some well-known idealized systems, and to real prototypes of communications software.
251 citations
"Elasticity in Cloud Computing: What..." refers background in this paper
...Scalability in the context of distributed systems has been defined in [6], as well as more recently in [3, 4], where also a measurement methodology is proposed....
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01 Jan 2007
TL;DR: A framework for precisely characterizing and analyzing the scalability of a software system is presented, which treats scalability as a multi-criteria optimization problem and captures the dependency relationships that underlie typical notions of scalability.
Abstract: The term scalability appears frequently in computing literature, but it is a term that is poorly defined and poorly understood. The lack of a clear, consistent and systematic treatment of scalability makes it difficult to evaluate claims of scalability and to compare claims from different sources. This paper presents a framework for precisely characterizing and analyzing the scalability of a software system. The framework treats scalability as a multi-criteria optimization problem and captures the dependency relationships that underlie typical notions of scalability. The paper presents the results of a case study in which the framework and analysis method were applied to a real-world system, demonstrating that it is possible to develop a precise, systematic characterization of scalability and to use the characterization to compare the scalability of alternative system designs.
54 citations
"Elasticity in Cloud Computing: What..." refers background in this paper
...Scalability in the context of distributed systems has been defined in [6], as well as more recently in [3, 4], where also a measurement methodology is proposed....
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