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

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Elasticity in Cloud Computing: State of the Art and Research Challenges

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Issues in complex event processing

Infrastructure and Requirements for Building Research-Grade Multi-Agent Systems.- MAS Infrastructure Definitions, Needs, and Prospects.- Tools for Developing and Monitoring Agents in Distributed Multi-agent Systems.- Agora: An Infrastructure for Cooperative Work Support in Multi-Agent Systems.- Sensible Agent Testbed Infrastructure for Experimentation.- The MadKit Agent Platform Architecture.- An Architecture for Modeling Internet-based Collaborative Agent Systems.- Frameworks for Reasoning about Agent Based System.- Integrating High-Level and Detailed Agent Coordination into a Layered Architecture.- Adaptive Infrastructures for Agent Integration.- RoboCup Soccer Server and CMUnited: Implemented Infrastructure for MAS Research.- An Agent Infrastructure to Build and Evaluate Multi-Agent Systems: The Java Agent Framework and Multi-Agent System Simulator.- Design-to-Criteria Scheduling: Real-Time Agent Control.- Integrating Conversational Interaction and Constraint Based Reasoning in an Agent Building Shell.- An Enabling Environment for Engineering Cooperative Agents.- Agent Mobility and Reification of Computational State: An Experiment in Migration.- As Strong as Possible Agent Mobility.- An Architecture for Adaptive Web Stores.- Performance Issues and Infrastructure Scalability in Building Multi-Agent Systems.- A Performance Analysis Framework for Mobile Agent Systems.- A Layered Agent Template for Enterprise Computing.- A Community of Agents for User Support in a Problem-Solving Environment.- Scalable Mobile Agents Supporting Dynamic Composition of Functionality.- A Formal Development and Validation Methodology Applied to Agent-Based Systems.- A Proposal for Meta-learning through a MAS (Multi-agent System).- Scalability Metrics and Analysis of Mobile Agent Systems.- Improving the Scalability of Multi-agent Systems.- Mobile Agents for Distributed Processing.- Scalability of a Transactional Infrastructure for Multi-Agent Systems.- Towards a Scalable Architecture for Knowledge Fusion.- Towards Validation of Specifications by Simulation.- Open Source, Standards and Scaleable Agencies.- Infrastructure Issues and Themes for Scalable Multi-agent Systems.

Infrastructure for agents, multi-agent systems, and scalable multi-agent systems : International Workshop on Infrastructure for Scalable Multi-Agent Systems, Barcelona, Spain, June 3-7, 2000 : revised papers

Context: In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading to potential misunderstandings. For example, cloud consumers and providers cannot negotiate reliable and quantitative service level objectives directly understood by each stakeholder. Objectives: Therefore, we examine existing definitions and metrics for these quality properties from the viewpoint of cloud consumers, cloud providers, and software architects with regard to commonly used concepts. Methods: We execute a systematic literature review (SLR), reproducibly collecting common concepts in definitions and metrics for scalability, elasticity, and efficiency. As quality selection criteria, we assess whether existing literature differentiates the three properties, exemplifies metrics, and considers typical cloud characteristics and cloud roles. Results: Our SLR yields 418 initial results from which we select 20 for in-depth evaluation based on our quality selection criteria. In our evaluation, we recommend concepts, definitions, and metrics for each property. Conclusions: Software architects can use our recommendations to analyze the quality of cloud computing applications. Cloud providers and cloud consumers can specify service level objectives based on our metric suggestions.

Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics

In cloud computing, software applications are potentially able to use only the computing resources that are minimally needed for performant operation. Because cloud providers provision such resources on a pay-per-use basis, software architects are interested in analyzing the operational costs that accrue for such applications, allowing architects to optimize for cost-efficiency. Current analysis approaches like Palladio focus on traditional performance metrics but lack support for cost-efficiency metrics. Therefore, software architects have to inaccurately estimate operational costs of their applications, potentially leading to economically unusable applications. To tackle this problem, we integrated cost metrics into SimuLizar, a Palladio extension for analyzing cloud computing applications. Our integration allows architects to attach prices to computing resources used by software applications. In a proof-of-concept evaluation with a simple book shop, we show that our integration allows architects to analyze costs with high accuracy.

Analyzing Cost-Efficiency of Cloud Computing Applications with SimuLizar?

[Context and motivation] Cyber-physical systems, like modern cars and industrial automation systems, are highly connected and complex. [Question/problem] Their various interconnections open interfaces for attackers, and their complexity increases the risk of undetected security vulnerabilities. Hence, an important part of requirements engineering is threat modeling. It is a means to elicit security assets, goals, and assumptions, and to derive required security controls. Effective threat modeling needs a systematic workshop setup. [Principal ideas/results] In this paper, we report our experiences and lessons learned from threat modeling workshops that we conducted with industry partners from the domains of industrial automation, health care, smart home, and automotive. [Contribution] In conclusion, we derive a set of open challenges. 

Hendrik Eikerling

Papers

Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics

Analyzing Cost-Efficiency of Cloud Computing Applications with SimuLizar?

Eliciting Security Requirements - An Experience Report