77,419 research outputs found
Advanced Strategies for Precise and Transparent Debugging of Performance Issues in In-Memory Data Store-Based Microservices
The rise of microservice architectures has revolutionized application design,
fostering adaptability and resilience. These architectures facilitate scaling
and encourage collaborative efforts among specialized teams, streamlining
deployment and maintenance. Critical to this ecosystem is the demand for low
latency, prompting the adoption of cloud-based structures and in-memory data
storage. This shift optimizes data access times, supplanting direct disk access
and driving the adoption of non-relational databases.
Despite their benefits, microservice architectures present challenges in
system performance and debugging, particularly as complexity grows. Performance
issues can readily cascade through components, jeopardizing user satisfaction
and service quality. Existing monitoring approaches often require code
instrumentation, demanding extensive developer involvement. Recent strategies
like proxies and service meshes aim to enhance tracing transparency, but
introduce added configuration complexities.
Our innovative solution introduces a new framework that transparently
integrates heterogeneous microservices, enabling the creation of tailored tools
for fine-grained performance debugging, especially for in-memory data
store-based microservices. This approach leverages transparent user-level
tracing, employing a two-level abstraction analysis model to pinpoint key
performance influencers. It harnesses system tracing and advanced analysis to
provide visualization tools for identifying intricate performance issues. In a
performance-centric landscape, this approach offers a promising solution to
ensure peak efficiency and reliability for in-memory data store-based cloud
applications
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Software-Defined Cloud Computing: Architectural Elements and Open Challenges
The variety of existing cloud services creates a challenge for service
providers to enforce reasonable Software Level Agreements (SLA) stating the
Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid
such penalties at the same time that the infrastructure operates with minimum
energy and resource wastage, constant monitoring and adaptation of the
infrastructure is needed. We refer to Software-Defined Cloud Computing, or
simply Software-Defined Clouds (SDC), as an approach for automating the process
of optimal cloud configuration by extending virtualization concept to all
resources in a data center. An SDC enables easy reconfiguration and adaptation
of physical resources in a cloud infrastructure, to better accommodate the
demand on QoS through a software that can describe and manage various aspects
comprising the cloud environment. In this paper, we present an architecture for
SDCs on data centers with emphasis on mobile cloud applications. We present an
evaluation, showcasing the potential of SDC in two use cases-QoS-aware
bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and
discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing,
Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi,
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