Jiffy

Khandelwal, Anurag; Tang, Yupeng; Agarwal, Rachit; Stoica, Ion

doi:10.1145/3492321.3527539

Cited by 25 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If so, over-allocation of resources is done, while a cost-effective resource is allocated if the job is not latency-sensitive. Finally, Khandelwal et al [34] proposed Jiffy, which combines profiling process of the Pocket service [50] and a multiplexing method to adjust the resource provisioning method. This approach enables sharing of available capacity across concurrently running jobs, ensuring efficient resource utilization.…”

Section: Application Profilingmentioning

confidence: 99%

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Jawaddi

Ismail

2023

Preprint

View full text Add to dashboard Cite

The popularity of serverless computing has been fueled by its operational simplicity, pay-per-use pricing model, and the ability to autoscale. However, there is a lack of comprehensive reviews that focus on the autoscaling context in serverless computing. In this paper, we address this gap by proposing a taxonomy of autoscaling properties for serverless computing. To gather relevant information, we review recent contributions on autoscaling in serverless computing from 2018 to 2022. Using the proposed taxonomy, we analyze the existing autoscaling solutions. Our analysis reveals that the scaling objectives explored by researchers are limited to certain elements, and the existing serverless autoscaling approaches do not provide guarantees that the scaling policies or strategies can meet the Service Level Agreement (SLA) requirements. We conclude by recommending open challenges from three perspectives: verification of autoscaling, energy-driven autoscaling, and anomaly-aware autoscaling. These challenges highlight the need for future research to address the limitations of existing autoscaling approaches and provide more robust and reliable autoscaling mechanisms for serverless computing.

show abstract

Section: Application Profilingmentioning

confidence: 99%

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Jawaddi

Ismail

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, our solution does not need any information on resource demand. Also, as shown in [7,16,17], intermediate data sizes can consistently vary during the workload execution, resulting in the well-understood problem of potential performance degradation and/or resource underutilization [11,20]. All these works involve indirect communication, demanding two serial data copies over the network in the critical path: one from producer function to shared storage and one from shared storage to consumer function.…”

Section: Related Workmentioning

confidence: 99%

“…The slow data transfers between functions make the CloudSort benchmark to be up to 500× slower when executed on AWS Lambda with S3 instead of on a cluster of Virtual Machines (VMs). Recent studies tackle this problem by implementing optimized exchange operators [13,15], using multi-tier storage combining slow with fast storage or solely remote in-memory storage [7,8,16], exploiting per-node caches [2,19], co-locating functions on a single container [1,5,9,18], handling external storage on long-running VMs [3,22], or circumventing the network constraints [21]. However, these methods either use domain-specific optimizations, require two copies of data over the network, are not fully transparent to the user, break the advantage of fine-grained scaling, or use non-serverless components.…”

Section: Introductionmentioning

confidence: 99%

Floki

Nestorov

Berral

Misale³

et al. 2022

Proceedings of the Eighth International Workshop on Container Technologies and Container Clouds

View full text Add to dashboard Cite

Serverless computing emerges as an architecture choice to build and run containerized data-intensive pipelines. It leaves the tedious work of infrastructure management and operations to the cloud provider, allowing developers to focus on their core business logic, decomposing their jobs into small containerized functions. To increase platform scalability and flexibility, providers take advantage of hardware disaggregation and require inter-function communication to go through shared object storage. Despite data persistence and recovery advantages, object storage is expensive in terms of performance and resources when dealing with data-intensive workloads. In this paper, we present Floki, a data forwarding system for direct and interfunction data exchange proactively enabling point-to-point communication between pipeline producer-consumer pairs of containerized functions through fixed-size memory buffers, pipes, and sockets. Compared with state-of-practice object storage, Floki shows up to 74.95× of end-to-end time performance increase, reducing the largest data sharing time from 12.55 to 4.33 minutes, while requiring up to 50,738× fewer disk resources, with up to roughly 96GB space release. CCS CONCEPTS• Information systems → Data management systems.

show abstract

“…Functions also are not directly addressable, which forces them to communicate through non-local storage services (e.g. Amazon S3, Azure Blob Storage); provisioned "far-memory" key-value stores [64], [88], [152]; or queueing services [65].…”

Section: Background and Motivationmentioning

confidence: 99%

“…Some approaches to address this problem include (1) adding faster -but still remote -keyvalue stores such as in Pocket [64], Jiffy [152], REDIS [88], Wukong [153], or R2E2 [154]; (2) using a serverful orchestrator to manage long serverless invocations as short-lived containers [14],…”

Section: Introductionmentioning

confidence: 99%

Informed optimization of cloud applications

Abdi

View full text Add to dashboard Cite

Cloud applications running in private data centers or public clouds are critical to every aspect of modern society, from entertainment to healthcare to financial services. The rapid growth of cloud applications is because organizations extensively leverage the general-purpose services offered by cloud platforms. Examples are general-purpose storage services such as AWS S3 [4]. These general-purpose services promise to provide high-performance and low-cost data analysis, elastic and scalable resources, and robust and reliable computations. Yet, cloud applications confront challenges to achieving these promises for several reasons: the exponential growth of data, the network limitations, the overhead of accessing the remote storage service, and the scarcity of complete information to make optimized decisions at different layers of the applications.This dissertation focuses on proposing systems to address the challenges mentioned above.We describe two systems that expand the general-purpose service APIs to exchange information critical to optimizing decisions. We also introduce components and algorithms that interpret the exchanged information and make optimized decisions.First, we describe Kariz to address the challenge of providing high-performance and scalable analytic applications with disaggregated storage-compute deployments. Kariz targets cloud applications that leverage general-purpose services such as analytical frameworks and object storage services. Kariz introduces a distributed caching system that extends the general-purpose object storage service and caches and prefetches input data accessed by the analytical framework. It extends the analytic framework APIs to exchange job dependency graphs (DAGs) and scheduling information with the cache, makes I/O scheduling decisions according to the scheduling information, estimates the run-time improvements, and predicts bandwidth to object storage service. Kariz integrates a cross-DAG scheduler and prefers pre-fetching input data shared by multiple DAGs. To build and evaluate Kariz, we characterize the job dependency graphs(DAGs) generated by analytic frameworks in a workload from in-production clusters.Then, we describe the elements of Kariz: a straggler-resistant partial cache system, a cache controller that run alongside the analytic frameworks-i.e. Spark and Pig-, and two graph-aware caching and pre-fetching algorithm for optimizing the caching of individual queries and three policies for optimizing across queries from, potentially, multiple different clusters. With an vi algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.Second, we describe Palette, to address the growing challenge of having computation close to data in general purpose Function-as-a-Service (FaaS) serverless computing. FaaS platforms enable a simple programming model with almost unbounded elasticity, making them an exciting choice for most cloud applications. However, current FaaS platforms achieve this...

show abstract

Jiffy

Cited by 25 publications

References 21 publications

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Floki

Informed optimization of cloud applications

Contact Info

Product

Resources

About