Quantifying cloud elasticity with container-based autoscaling

2019 ◽  
Vol 98 ◽  
pp. 672-681 ◽  
Author(s):  
Fan Zhang ◽  
Xuxin Tang ◽  
Xiu Li ◽  
Samee U. Khan ◽  
Zhijiang Li
Keyword(s):  
Cloud Elasticity

An execution environment as a service for adaptive long-running workflows

2021 ◽  
Vol 17 (2) ◽  
pp. 117-139
Author(s):  
Milton Secundino de Souza-Júnior ◽  
Nelson Souto Rosa ◽  
Fernando Antônio Aires Lins
Keyword(s):  
Current Data ◽  
Delivery Mode ◽  
Insurance Company ◽  
Business Rules ◽  
Content Type ◽  
State Management ◽  
Adaptive Capabilities ◽  
Execution Environment ◽  
Cloud Elasticity ◽  
Computational Resources

Purpose This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution. Design/methodology/approach LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW. Findings LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs. Originality/value The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

scholarly journals Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine

2020 ◽  
Vol 191 ◽  
pp. 105403
Author(s):  
Vinicius Facco Rodrigues ◽  
Euclides Palma Paim ◽  
Rafael Kunst ◽  
Rodolfo Stoffel Antunes ◽  
Cristiano André da Costa ◽  
...  
Keyword(s):  
Data Compression ◽  
Cloud Elasticity

Elastipipe: On Providing Cloud Elasticity for Pipeline-structured Applications

2016 ◽  
pp. 293-304
Author(s):  
Rodrigo da Rosa Righi ◽  
Mateus Aubin ◽  
Cristiano André da Costa ◽  
Antonio Marcos Alberti ◽  
Arismar Cerqueira Sodre
Keyword(s):  
Cloud Elasticity

scholarly journals Design and evaluation of a biologically-inspired cloud elasticity framework

2020 ◽  
Vol 23 (4) ◽  
pp. 3095-3117
Author(s):  
Amjad Ullah ◽  
Jingpeng Li ◽  
Amir Hussain
Keyword(s):  
Fuzzy System ◽  
State Of The Art ◽  
The State ◽  
Effective Management ◽  
Biologically Inspired ◽  
Computational Overhead ◽  
Cloud Elasticity ◽  
Computational Resources ◽  
Auto Scaling ◽  
Selection Of

Abstract The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.

scholarly journals Brokel: Towards enabling multi-level cloud elasticity on publish/subscribe brokers

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772886
Author(s):  
Vinicius Facco Rodrigues ◽  
Ivam Guilherme Wendt ◽  
Rodrigo da Rosa Righi ◽  
Cristiano André da Costa ◽  
Jorge Luis Victória Barbosa ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Fixed Number ◽  
Smart Devices ◽  
Communication Model ◽  
Message Delivery ◽  
Application Time ◽  
System Service ◽  
Service Broker ◽  
Multi Level ◽  
Cloud Elasticity

Internet of Things networks together with the data that flow between networked smart devices are growing at unprecedented rates. Often brokers, or intermediaries nodes, combined with the publish/subscribe communication model represent one of the most used strategies to enable Internet of Things applications. At scalability viewpoint, cloud computing and its main feature named resource elasticity appear as an alternative to solve the use of over-provisioned clusters, which normally present a fixed number of resources. However, we perceive that today the elasticity and Pub/Sub duet presents several limitations, mainly related to application rewrite, single cloud elasticity limited to one level and false-positive resource reorganization actions. Aiming at bypassing the aforesaid problems, this article proposes Brokel, a multi-level elasticity model for Pub/Sub brokers. Users, things, and applications use Brokel as a centralized messaging service broker, but in the back-end the middleware provides better performance and cost (used resources × performance) on message delivery using virtual machine (VM) replication. Our scientific contribution regards the multi-level, orchestrator, and broker, and the addition of a geolocation domain name system service to define the most suitable entry point in the Pub/Sub architecture. Different execution scenarios and metrics were employed to evaluate a Brokel prototype using VMs that encapsulate the functionalities of Mosquitto and RabbitMQ brokers. The obtained results were encouraging in terms of application time, message throughput, and cost (application time × resource usage) when comparing elastic and non-elastic executions.

Managing Uncertainty in Autonomic Cloud Elasticity Controllers

2016 ◽  
Vol 3 (3) ◽  
pp. 50-60 ◽  
Author(s):  
Pooyan Jamshidi ◽  
Claus Pahl ◽  
Nabor C. Mendonca
Keyword(s):  
Cloud Elasticity

Towards Using Cloud Elasticity on the Internet of Things Landscape

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Rodrigo Da Rosa Righi ◽  
Márcio Miguel Gomes ◽  
Cristiano Andrá Da Costa ◽  
Helge Parzyjegla ◽  
Hans-Ulrich Heiss
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Virtual Machines ◽  
The Internet ◽  
Data Volume ◽  
Significant Performance ◽  
Cloud Elasticity ◽  
Performance Gains ◽  
The Internet Of Things

The digital universe is growing at significant rates in recent years. One of the main responsible for this sentence is the Internet of Things, or IoT, which requires a middleware that should be capable to handle this increase of data volume at real-time. Particularly, data can arrive in the middleware in parallel as in terms of input data from Radio-Frequency Identification (RFID) readers as request-reply query operations from the users side. Solutions modeled at software, hardware and/or architecture levels present limitations to handle such load, facing the problem of scalability in the IoT scope. In this context, this arti- cle presents a model denoted Eliot - Elasticity-driven Internet of Things - which combines both cloud and high performance computing to address the IoT scal- ability problem in a novel EPCglobal-compliant architecture. Particularly, we keep the same API but offer an elastic EPCIS component in the cloud, which is designed as a collection of virtual machines (VMs) that are allocated and deallocated on-the-fly in accordance with the system load. Based on the Eliot model, we developed a prototype that could run over any black-box EPCglobal- compliant middleware. We selected the Fosstrak for this role, which is currently one of the most used IoT middlewares. Thus, the prototype acts as an upper layer over the Fosstrak to offer a better throughput and latency performances in an effortless way. The results are encouraging, presenting significant performance gains in terms of response time and request throughput when comparing both elastic (Eliot) and non-elastic (standard Fosstrak) executions.  

Enhancing performance of IoT applications with load prediction and cloud elasticity

2020 ◽  
Vol 109 ◽  
pp. 689-701 ◽  
Author(s):  
Rodrigo da Rosa Righi ◽  
Everton Correa ◽  
Márcio Miguel Gomes ◽  
Cristiano André da Costa
Keyword(s):  
Load Prediction ◽  
Iot Applications ◽  
Cloud Elasticity

An Infrastructure-as-a-Service Cloud

2013 ◽  
pp. 302-324 ◽  
Author(s):  
Weijia Song ◽  
Zhen Xiao
Keyword(s):  
Cloud Model ◽  
Scale Up ◽  
Resource Provisioning ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Accounting Model ◽  
Cloud Elasticity ◽  
Business Customers ◽  
Cloud Users ◽  
Service Cloud

Cloud computing allows business customers to elastically scale up and down their resource usage based on needs. This feature eliminates the dilemma of planning IT infrastructures for Cloud users, where under-provisioning compromises service quality while over-provisioning wastes investment as well as electricity. It offers virtually infinite resource. It also made the desirable “pay as you go” accounting model possible. The above touted gains in the Cloud model come from on-demand resource provisioning technology. In this chapter, the authors elaborate on such technologies incorporated in a real IaaS system to exemplify how Cloud elasticity is implemented. It involves the resource provisioning technologies in hypervisor, Virtual Machine (VM) migration scheduler and VM replication. The authors also investigate the load prediction algorithm for its significant impacts on resource allocation.

Sign in / Sign up

Export Citation Format

Share Document