Benchmarking Eventual Consistency: Lessons Learned from Long-Term Experimental Studies

Bermbach, David; Tai, Stefan

doi:10.1109/ic2e.2014.37

Cited by 31 publications

(13 citation statements)

References 26 publications

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“…In addition, we observe that, to the best of our knowledge, no public cloud provider specifies some elasticity metric as SLO in its offered SLA. Besides elasticity, another example of advanced QoS property which is of interest when using storage cloud services is data consistency [57]. In the cloud scenario, where storage systems tend to adopt the eventual consistency model, which relaxes consistency guarantees in favor of availability and latency tradeoffs as required by the CAP theorem, the runtime monitoring of the consistency level (and of the degree of inconsistency) cannot be neglected.…”

Section: Monitoring Of Cloud Servicesmentioning

confidence: 99%

“…In the active mode, the service provider furnishes the broker with a testing workload and proper benchmark tools and the broker will periodically run tests to evaluate the application performance and ensure SLA compliance. This approach may be used to evaluate key and challenging properties of the cloud environment, like elasticity [56] and consistency of data storage [57], whose related metrics can be negotiated as service level objectives (SLOs) in the SLA agreed with the consumers. However, its main drawbacks are the additional traffic and load Objects represent the metrics collected using the push-based approach (e.g., service time, throughput, availability) and active pull-based approach (e.g., elasticity, consistency).…”

Section: Qos Monitoringmentioning

confidence: 99%

“…Monitoring techniques and tools used for the operating system and application levels do not allow to catch a wide range of QoS [57] Legal-rule compliance checking Scalable and run-time legal reasoning [82] Multilingual access and management of legal concepts management [83,84,85] metrics and health state metrics for containers. Docker offers the docker stat command that returns CPU and memory utilization for each running container.…”

Section: Monitoring Of Cloud Servicesmentioning

confidence: 99%

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Research challenges in legal-rule and QoS-aware cloud service brokerage

Casalicchio

Cardellini

Interino

et al. 2018

Future Generation Computer Systems

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The ICT industry and specifically critical sectors, such as healthcare, transportation, energy and government, require as mandatory the compliance of ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control that customers have on the services. Today, the cloud industry is aware of this problem (as evidenced by the compliance program of many cloud service providers), and the research community is addressing the many facets of the legal-rule compliance checking and quality assurance problem.\ud \ud Cloud service brokerage plays an important role in legislation compliance and QoS management of cloud services. In this paper we discuss our experience in designing a legal-rule and QoS-aware cloud service broker, and we explore relate research issues. Specifically we provide three main contributions to the literature: first, we describe the detailed design architecture of the legal-rule and QoS-aware broker. Second, we discuss our design choices which rely on the state of the art solutions available in literature. We cover four main research areas: cloud broker service deployment, seamless cloud service migration, cloud service monitoring, and legal rule compliance checking. Finally, from the literature review in these research areas, we identify and discuss research challenges

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Section: Monitoring Of Cloud Servicesmentioning

confidence: 99%

Section: Qos Monitoringmentioning

confidence: 99%

Section: Monitoring Of Cloud Servicesmentioning

confidence: 99%

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Research challenges in legal-rule and QoS-aware cloud service brokerage

Casalicchio

Cardellini

Interino

et al. 2018

Future Generation Computer Systems

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“…Next, we provide a qualitative analysis with respect to the nonfunctional features of the DMSs (refer to "Non-functional data management features" section). For quantitative analysis of these non-functional requirements, we refer the interested reader to the existing work focused on DBMS evaluation frameworks [44,[57][58][59][60] and evaluation results [42,61,62].…”

Section: Comparison Of Selected Dbmssmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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“…For instance, the de facto standard YCSB [29] and its extensions [30], [31] introduced database benchmarking based on CRUD interfaces, which are more compatible with modern NoSQL stores like Apache Cassandra [32]. Other approaches such as OLTPBench [33] or BenchFoundry [34], [35] aim to build comprehensive multi-quality benchmarking platforms that also include measurement approaches for qualities beyond performance, e.g., data consistency [10], [36]- [39] or elastic scalability [11], [12]. Beyond this, there are a number of approaches studying performance impacts of TLS on NoSQL datastores [40]- [42], web services [43], and web servers [44].…”

Section: Benchmarkingmentioning

confidence: 99%

Benchmarking Web API Quality

Bermbach

Wittern

2016

Lecture Notes in Computer Science

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Modern applications increasingly interact with web APIs -reusable components, deployed and operated outside the application, and accessed over the network. Their existence, arguably, spurs application innovations, making it easy to integrate data or functionalities. While previous work has analyzed the ecosystem of web APIs and their design, little is known about web API quality at runtime. This gap is critical, as qualities including availability, latency, or security can severely impact applications and user experience. In this paper, we revisit a 3-month, geo-distributed benchmark of popular web APIs, originally performed in 2015. We repeat this benchmark in 2018 and compare results from these two benchmarks regarding availability and latency. We furthermore introduce new results from assessing web API security measurements, collected both in 2015 and 2018, and results from our attempts to reach out to API providers with the results from our 2015 experiments. Our extensive experiments show that web API qualities vary 1.) based on the geo-distribution of clients, 2.) during our individual experiments, and 3.) between the two experiments. Our findings provide evidence to foster the discussion around web API quality, and can act as a basis for the creation of tools and approaches to mitigate quality issues.Index Terms-Web APIs, Benchmarking, Quality of Service ! 2. We denote an endpoint to be the combination of a resource, identified by a URL, and an HTTP method as proposed in [7].

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Benchmarking Eventual Consistency: Lessons Learned from Long-Term Experimental Studies

Cited by 31 publications

References 26 publications

Research challenges in legal-rule and QoS-aware cloud service brokerage

Research challenges in legal-rule and QoS-aware cloud service brokerage

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Benchmarking Web API Quality

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