Latency-Aware Secure Elastic Stream Processing with Homomorphic Encryption

Rodrigo, Arosha; Dayarathna, Miyuru; Jayasena, Sanath

doi:10.1007/s41019-019-00100-5

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…HTTP Log Processor is a stream processing benchmark developed * by us based on the HTTP log data provided by the division of economic and risk analysis of Securities and Exchange Commission of USA. 18,34,35 This dataset contains the details about the web browsers and related crawlers that visited the site. Events in the HTTP log stream have sixteen fields as iij_timestamp, groupId, ip, date, time, zone, cik, accession, code, size, idx, norefer, noagent, find, crawler, and browser.…”

Section: Http Log Processor Benchmarkmentioning

confidence: 99%

Resource aware scheduler for distributed stream processing in cloud native environments

Sarathchandra

Karandana

Heenatigala

et al. 2021

Concurrency and Computation

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Recently distributed stream processors are increasingly being deployed in cloud computing infrastructures. In this article, we study performance characteristics of distributed stream processing applications in Google Compute Engine which is based on Kubernetes. We identify performance gaps in terms of throughput which appear in such environments when using a round robin (RR) scheduling algorithm. As a solution, we propose resource aware stream processing scheduler called resource aware scheduler for stream processing applications in cloud native environments (RaspaCN).We implement RaspaCN's job scheduler using two-step process. First, we use machine learning to identify the optimal number of worker nodes. Second, we use RR and multiple Knapsack algorithms to produce performance optimal stream processing job schedules. With three application benchmarks called HTTP Log Processor, Nexmark, and Email Processor representing real world stream processing scenarios we evaluate the performance benefits obtained via RaspaCN's scheduling algorithm. RaspaCN could produce percentage increase of average throughput values by at least 37%, 38%, and 10%, respectively, for HTTP Log Processor, Nexmark, and Email Processor benchmarks for fixed input data rates. Furthermore, we conduct experiments with varying input data rates as well and show 7% improved average throughput for HTTP Log Processor. These experiments show the effectiveness of our proposed stream processor job scheduler for producing improved performance.

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Section: Http Log Processor Benchmarkmentioning

confidence: 99%

Resource aware scheduler for distributed stream processing in cloud native environments

Sarathchandra

Karandana

Heenatigala

et al. 2021

Concurrency and Computation

Self Cite

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Journey from cloud of things to fog of things: Survey, new trends, and research directions

et al. 2022

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With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms. K E Y W O R D Scloud computing, internet of things, cloud of things, fog computing, latency INTRODUCTIONDifferent computing paradigms have evolved over the decades as per the needs in various situations. The concept of the Cloud computing paradigm has evolved from distributed systems and it has gained attention by offering a plethora of services to end-users anywhere and anytime on pay per use basis. It has also become the business model in the last decade due to well-known characteristics like scalability, multi-tenant environment, virtualization, and resource pooling. It is observed that a large number of industries, government, as well as private organization, have shifted their applications and data to the cloud platform. Further, the Cloud is also playing a significant role in the advancement of Internet of Things (IoT) applications. As per the transformation and movement in ubiquitous computing, we need the integration

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Privacy-Preserving Polynomial Evaluation over Spatio-Temporal Data on an Untrusted Cloud Server

Song

Tang

Yan

et al. 2021

Database Systems for Advanced Applications

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Latency-Aware Secure Elastic Stream Processing with Homomorphic Encryption

Cited by 8 publications

References 24 publications

Resource aware scheduler for distributed stream processing in cloud native environments

Resource aware scheduler for distributed stream processing in cloud native environments

Journey from cloud of things to fog of things: Survey, new trends, and research directions

Privacy-Preserving Polynomial Evaluation over Spatio-Temporal Data on an Untrusted Cloud Server

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