CELAR

CELAR
Name	CELAR

CELAR

CELAR is an open-source cloud elasticity management and resource allocation platform designed to automate lifecycle management for complex applications on Infrastructure-as-a-Service environments. It targets scalable deployments, enabling dynamic provisioning, performance monitoring, and cost-aware optimization across virtualized infrastructure. The project integrates model-driven orchestration with feedback-control and optimization techniques to align resource usage with application-level objectives.

History

CELAR originated as a research-driven project combining expertise from cloud computing centers, academic groups, and open-source foundations. Early work drew from contributions in autonomic computing research and was influenced by initiatives around OpenStack, Eucalyptus (software), and Apache Mesos orchestration trends. Funding and collaboration came via European Union research programs and partnerships with institutions akin to ETH Zurich, Imperial College London, and research labs that have historically produced work on elasticity and autoscaling. Over successive releases the project incorporated features inspired by commercial offerings such as Amazon EC2 Auto Scaling, techniques published in venues like ACM SIGMETRICS and IEEE International Conference on Cloud Computing, and best practices used by platforms including Google Compute Engine and Microsoft Azure.

Architecture and Components

The CELAR architecture is modular, comprising orchestrators, monitoring agents, a model repository, and optimization engines that interact with cloud APIs and virtualization layers. The orchestrator component parallels concepts from Kubernetes control planes and interfaces with resource managers similar to Apache Mesos and schedulers used by Hadoop YARN. Monitoring agents collect metrics comparable to telemetry produced by Prometheus (software), Nagios, and Zabbix, forwarding data to time-series systems and analytics modules influenced by InfluxDB usage. The model repository stores application deployment blueprints influenced by TOSCA modeling work and configuration templates reminiscent of Ansible, Puppet, and Chef (software). Optimization engines implement algorithms derived from research appearing in SAS (Special Issue), leveraging solvers and model checkers akin to Z3 (theorem prover) and optimization libraries used by IBM CPLEX or Gurobi in academic prototypes. Integration adapters enable control over IaaS providers and virtualization stacks like OpenStack, VMware vSphere, and public clouds such as Amazon Web Services and Google Cloud Platform.

Functionality and Features

CELAR provides automated elasticity policies, monitoring-informed scaling, cost-performance tradeoff analysis, and support for complex multi-tier applications. Its policy framework resembles rule-driven systems used in Drools and supports constraint-based specifications akin to languages associated with TOSCA and model-driven engineering from projects like Eclipse Modeling Framework. Monitoring features align with metric collection patterns from Prometheus (software), Graphite, and Collectd, enabling alarm and threshold detection comparable to PagerDuty alerting workflows. Optimization and decision-making adopt approaches from control theory applied in contexts such as MIMO control and algorithms discussed at IFAC conferences, while autoscaling strategies reflect studies published in IEEE Transactions on Cloud Computing and experiments run on testbeds like CloudLab and Grid'5000. The platform also offers deployment lifecycle management influenced by Jenkins automation and CI/CD pipelines used with GitLab and Travis CI.

Use Cases and Applications

CELAR targets cloud-native and legacy multi-tier applications requiring dynamic scaling, including web services, data analytics pipelines, and scientific workflows. Real-world deployments include scenarios comparable to deployments of WordPress sites on OpenStack, analytics stacks similar to Apache Spark clusters, and service-oriented architectures resembling SOA systems operated by research groups in projects akin to Horizon 2020. It supports elastic provisioning for batch processing workflows akin to those orchestrated by Apache Airflow and continuous integration workloads similar to Jenkins farms. Scientific computing use cases draw parallels to experiments run on infrastructures like European Grid Infrastructure and high-throughput systems used by institutions such as CERN for data processing bursts.

Performance and Evaluation

Performance evaluation of CELAR-style systems typically measures response time, throughput, resource utilization, cost per transaction, and adaptation latency under workload fluctuation. Benchmarks mirror methodologies used in studies employing SPEC benchmarks, microbenchmarks from YCSB, and large-scale experiments on platforms like CloudLab and Amazon EC2. Comparative analyses often reference autoscaling behavior observed in Amazon EC2 Auto Scaling, orchestration efficiency in Kubernetes, and scheduler throughput measured in Mesos studies. Results reported in associated publications demonstrate reductions in overprovisioning and improvements in SLA compliance comparable to outcomes published in IEEE Transactions on Cloud Computing and presented at conferences such as USENIX Annual Technical Conference.

Adoption and Community

The community around CELAR-style efforts includes academic researchers, open-source contributors, and operators from cloud-focused initiatives. Interaction channels replicate those of projects like OpenStack, Kubernetes, and Apache Software Foundation communities, with contributions originating from universities, research institutes, and industry partners resembling collaborations seen in Linux Foundation projects. Documentation, tutorials, and examples follow patterns set by GitHub-hosted projects and educational materials similar to those used by Coursera and edX cloud computing courses. Adoption tends to be strongest within academic testbeds and research demonstrators, with influence on commercial autoscaling features in platforms such as Amazon Web Services, Microsoft Azure, and orchestration tools like Kubernetes.

Category:Cloud computing