Related papers: Toward Reliable and Rapid Elasticity for Streaming…
This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous processing of IoT data to provide real-time services and…
Serverless clouds promise efficient scaling, reduced toil and monetary costs. Yet, serverless-ing a complex, legacy application might require major refactoring and thus is risky. As a case study, we use Airflow, an industry-standard…
Distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation…
A growing number of critical workflow applications leverage a streamlined edge-hub-cloud architecture, which diverges from the conventional edge computing paradigm. An edge device, in collaboration with a hub device and a cloud server,…
The increase in real time ultra-high definition video services presents a challenging issue to current network infrastructures, because of its high bandwidth usage, which saturate network links. The required bandwidth is related to strict…
Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance,…
There is an increasing trend for businesses to migrate their systems towards the cloud. Security concerns that arise when outsourcing data and computation to the cloud include data confidentiality and privacy. Given that a tremendous amount…
We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing…
Stream processing is a computing paradigm that supports real-time data processing for a wide variety of applications. At Meta, it's used across the company for various tasks such as deriving product insights, providing and improving user…
VM startup time is an essential factor in designing elastic cloud applications. For example, a cloud application with autoscaling can reduce under- and over-provisioning of VM instances with a precise estimation of VM startup time, and in…
Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire…
Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data…
Virtualization technology reduces cloud operational cost by increasing cloud resource utilization level. The incorporation of virtualization within cloud data centers can severely degrade cloud performance if not properly managed. Virtual…
Elasticity is fundamental to cloud computing, as it enables quickly allocating resources to match the demand of each workload as it arrives, rather than pre-provisioning resources to meet performance objectives. However, even serverless…
Controlling and analyzing cyberphysical and robotics systems is increasingly becoming a Big Data challenge. Pushing this data to, and processing in the cloud is more efficient than on-board processing. However, current cloud-based solutions…
We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Megaphone has the following differentiating characteristics:…
Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot…
The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency…
Fault tolerance is critical for distributed stream processing systems, yet achieving error-free fault tolerance often incurs substantial performance overhead. We present AF-Stream, a distributed stream processing system that addresses the…
In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update…