9,044 research outputs found
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
ShenZhen transportation system (SZTS): a novel big data benchmark suite
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets
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