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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. In addition, we survey the current and emerging technologies and provide a few implementation tips. Towards Unified Big Data Processing.

Big Data 154
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What is ITOps? Why IT operations is more crucial than ever in a multicloud world

Dynatrace

As more organizations adopt cloud-native technologies, traditional approaches to IT operations have been evolving. Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. So, what is ITOps?

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Kubernetes in the wild report 2023

Dynatrace

While Kubernetes is still a relatively young technology, a large majority of global enterprises use it to run business-critical applications in production. Findings provide insights into Kubernetes practitioners’ infrastructure preferences and how they use advanced Kubernetes platform technologies. Java, Go, and Node.js

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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

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Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. However, building and utilizing HCM presents challenges, including interconnecting various memory technologies (e.g.,

Latency 52
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Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware.

Hardware 103
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Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware.