The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
Meghna Pancholi - One of the best experts on this subject based on the ideXlab platform.
-
an open source benchmark suite for microservices and their hardware software implications for cloud edge systems
Architectural Support for Programming Languages and Operating Systems, 2019Co-Authors: Yu Gan, Yanqi Zhang, Dailun Cheng, Ankitha Shetty, Priyal Rathi, Nayan Katarki, Ariana Bruno, Brian Ritchken, Brendon Jackson, Meghna PancholiAbstract:Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds or thousands of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to Cluster Management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.
Yu Gan - One of the best experts on this subject based on the ideXlab platform.
-
An Open-Source Benchmark Suite for Cloud and IoT Microservices
2019Co-Authors: Yu Gan, Zhang Yanqi, Cheng Dailun, Shetty Ankitha, Rathi Priyal, Katarki Nayan, Bruno Ariana, Hu Justin, Ritchken Brian, Jackson BrendonAbstract:Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to Cluster Management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability
-
an open source benchmark suite for microservices and their hardware software implications for cloud edge systems
Architectural Support for Programming Languages and Operating Systems, 2019Co-Authors: Yu Gan, Yanqi Zhang, Dailun Cheng, Ankitha Shetty, Priyal Rathi, Nayan Katarki, Ariana Bruno, Brian Ritchken, Brendon Jackson, Meghna PancholiAbstract:Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds or thousands of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to Cluster Management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.
Alexander Zeier - One of the best experts on this subject based on the ideXlab platform.
-
predicting in memory database performance for automating Cluster Management tasks
International Conference on Data Engineering, 2011Co-Authors: Jan Schaffner, Hasso Plattner, Benjamin Eckart, Dean Jacobs, Christian Schwarz, Alexander ZeierAbstract:In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database Clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the Cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the Cluster to ensure that response time goals are met. In so doing, the number of servers in the Cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant Clustering framework for SAP's in-memory column database TREX.
Jan Schaffner - One of the best experts on this subject based on the ideXlab platform.
-
predicting in memory database performance for automating Cluster Management tasks
International Conference on Data Engineering, 2011Co-Authors: Jan Schaffner, Hasso Plattner, Benjamin Eckart, Dean Jacobs, Christian Schwarz, Alexander ZeierAbstract:In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database Clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the Cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the Cluster to ensure that response time goals are met. In so doing, the number of servers in the Cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant Clustering framework for SAP's in-memory column database TREX.
Muhammad Ali Babar - One of the best experts on this subject based on the ideXlab platform.
-
urban data Management system towards big data analytics for internet of things based smart urban environment using customized hadoop
Future Generation Computer Systems, 2019Co-Authors: Muhammad Ali Babar, Fahim Arif, Mian Ahmad Jan, Zhiyuan Tan, Fazlullah KhanAbstract:Abstract The unbroken amplification of a versatile urban setup is challenged by huge Big Data processing. Understanding the voluminous data generated in a smart urban environment for decision making is a challenging task. Big Data analytics is performed to obtain useful insights about the massive data. The existing conventional techniques are not suitable to get a useful insight due to the huge volume of data. Big Data analytics has attracted significant attention in the context of large-scale data computation and processing. This paper presents a Hadoop-based architecture to deal with Big Data loading and processing. The proposed architecture is composed of two different modules, i.e., Big Data loading and Big Data processing. The performance and efficiency of data loading is tested to propose a customized methodology for loading Big Data to a distributed and processing platform, i.e., Hadoop. To examine data ingestion into Hadoop, data loading is performed and compared repeatedly against different decisions. The experimental results are recorded for various attributes along with manual and traditional data loading to highlight the efficiency of our proposed solution. On the other hand, the processing is achieved using YARN Cluster Management framework with specific customization of dynamic scheduling. In addition, the effectiveness of our proposed solution regarding processing and computation is also highlighted and decorated in the context of throughput.