123 research outputs found
Data management, communication systems and the edge: Challenges for the future of transportation
Edge cloud systems have emerged as a new promising alternative to address the needs of many emerging latency-critical applicationssuch as autonomous vehicles. These applications are ill-suited for traditional clouds due to the end-to-end latency and the limited bandwidth between the cloud\u27s (few) data centers and these applications.In the edge cloud model, a myriad of small-scale computing clusters are brought next to the applications at the edge of the network. Many Applicationsin transportation engineering such as autonomous vehicle collision avoidance, and fleet management, requiring more global decision making for safety and correctness of operation, can thus make use of edge cloud systems, offloading their computations to these edge clusters.The edge can then analyze data from these vehicles to optimize the traffic flow, reduce accidents, and provide transportation systems with moreautonomy. The idea of edge computing today forms a cornerstone in the design of many future systems, including, 5G networks and autonomousvehicles, among many others
Трактування та класифікація витрат в сучасній економіці
In order to meet stringent performance requirements, system administrators must effectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in different system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods
A Sensor-Actuator Model for Data Center Optimization
Cloud data centers commonly use virtualization technologies to provision compute capacity with a level of indirection between virtual machines and physical resources. In this paper we explore the use of that level of indirection as a means for autonomic data center configuration optimization and propose a sensor-actuator model to capture optimization-relevant relationships between data center events, monitored metrics (sensors data), and management actions (actuators). The model characterizes a wide spectrum of actions to help identify the suitability of different actions in specific situations, and outlines what (and how often) data needs to be monitored to capture, classify, and respond to events that affect the performance of data center operations
The Challenge of Cloud Control
Today’s cloud data center infrastructures are not even near being able to cope with the enormous and rapidly varying capacity demands that will be reality in a near future. So far, very little is understood about how to transform today’s data centers (being large, power-hungry facilities, and operated through heroic efforts by numerous administrators) into a self-managed, dynamic, and dependable infrastructure, constantly delivering expected QoS with reasonable operation costs and acceptable carbon footprint for large-scale services with sometimes dramatic variations in capacity demands. In this paper, we discuss some of the major challenges for resource-optimized cloud data center. We propose a new research area called Cloud Control, which is a control theoretic approach to a range of cloud management problems, aiming to transform today´s static and energy consuming cloud data centers into self-managed, dynamic, and dependable infrastructures, constantly delivering expected quality of service with acceptable operation costs and carbon footprint for large-scale services with varying capacity demands
Adaptive and Application-agnostic Caching in Service Meshes for Resilient Cloud Applications
Service meshes factor out code dealing with inter-micro-service communication. The overall resilience of a cloud application is improved if constituent micro-services return stale data, instead of no data at all. This paper proposes and implements application agnostic caching for micro services. While caching is widely employed for serving web service traffic, its usage in inter-micro-service communication is lacking. Micro-services responses are highly dynamic, which requires carefully choosing adaptive time-to-life caching algorithms. Our approach is application agnostic, is cloud native, and supports gRPC. We evaluate our approach and implementation using the micro-service benchmark by Google Cloud called Hipster Shop. Our approach results in caching of about 80% of requests. Results show the feasibility and efficiency of our approach, which encourages implementing caching in service meshes. Additionally, we make the code, experiments, and data publicly available
Towards Soft Circuit Breaking in Service Meshes via Application-agnostic Caching
Service meshes factor out code dealing with inter-micro-service
communication, such as circuit breaking. Circuit breaking actuation is
currently limited to an "on/off" switch, i.e., a tripped circuit breaker will
return an application-level error indicating service unavailability to the
calling micro-service. This paper proposes a soft circuit breaker actuator,
which returns cached data instead of an error. The overall resilience of a
cloud application is improved if constituent micro-services return stale data,
instead of no data at all. While caching is widely employed for serving web
service traffic, its usage in inter-micro-service communication is lacking.
Micro-services responses are highly dynamic, which requires carefully choosing
adaptive time-to-life caching algorithms. We evaluate our approach through two
experiments. First, we quantify the trade-off between traffic reduction and
data staleness using a purpose-build service, thereby identifying algorithm
configurations that keep data staleness at about 3% or less while reducing
network load by up to 30%. Second, we quantify the network load reduction with
the micro-service benchmark by Google Cloud called Hipster Shop. Our approach
results in caching of about 80% of requests. Results show the feasibility and
efficiency of our approach, which encourages implementing caching as a circuit
breaking actuator in service meshes
A Tree-based protocol for enforcing quotas in clouds
Services are increasingly being hosted on cloud
nodes to enhance their performance and increase their availability.
The virtually unlimited availability of cloud resources
enables service owners to consume resources without quantitative restrictions, paying only for what they use. To avoid cost overruns, resource consumption must be controlled and capped when necessary. We present a distributed tree-based protocol for managing quotas in clouds that minimizes communication overheads and reduces the time required to determine whether a quota has been exhausted. Experimental evaluation shows that our protocol reduces communication costs by 42% relative to a
distributed baseline solution and is up to 15 times faster
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