741 research outputs found
numpywren: serverless linear algebra
Linear algebra operations are widely used in scientific computing and machine
learning applications. However, it is challenging for scientists and data
analysts to run linear algebra at scales beyond a single machine. Traditional
approaches either require access to supercomputing clusters, or impose
configuration and cluster management challenges. In this paper we show how the
disaggregation of storage and compute resources in so-called "serverless"
environments, combined with compute-intensive workload characteristics, can be
exploited to achieve elastic scalability and ease of management.
We present numpywren, a system for linear algebra built on a serverless
architecture. We also introduce LAmbdaPACK, a domain-specific language designed
to implement highly parallel linear algebra algorithms in a serverless setting.
We show that, for certain linear algebra algorithms such as matrix multiply,
singular value decomposition, and Cholesky decomposition, numpywren's
performance (completion time) is within 33% of ScaLAPACK, and its compute
efficiency (total CPU-hours) is up to 240% better due to elasticity, while
providing an easier to use interface and better fault tolerance. At the same
time, we show that the inability of serverless runtimes to exploit locality
across the cores in a machine fundamentally limits their network efficiency,
which limits performance on other algorithms such as QR factorization. This
highlights how cloud providers could better support these types of computations
through small changes in their infrastructure
Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks
Resource-disaggregated data centre architectures promise a means of pooling
resources remotely within data centres, allowing for both more flexibility and
resource efficiency underlying the increasingly important
infrastructure-as-a-service business. This can be accomplished by means of
using an optically circuit switched backbone in the data centre network (DCN);
providing the required bandwidth and latency guarantees to ensure reliable
performance when applications are run across non-local resource pools. However,
resource allocation in this scenario requires both server-level \emph{and}
network-level resource to be co-allocated to requests. The online nature and
underlying combinatorial complexity of this problem, alongside the typical
scale of DCN topologies, makes exact solutions impossible and heuristic based
solutions sub-optimal or non-intuitive to design. We demonstrate that
\emph{deep reinforcement learning}, where the policy is modelled by a
\emph{graph neural network} can be used to learn effective \emph{network-aware}
and \emph{topologically-scalable} allocation policies end-to-end. Compared to
state-of-the-art heuristics for network-aware resource allocation, the method
achieves up to higher acceptance ratio; can achieve the same acceptance
ratio as the best performing heuristic with less networking resources
available and can maintain all-around performance when directly applied (with
no further training) to DCN topologies with more servers than the
topologies seen during training.Comment: 10 pages + 1 appendix page, 8 figure
From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey
Context data is in demand more than ever with the rapid increase in the
development of many context-aware Internet of Things applications. Research in
context and context-awareness is being conducted to broaden its applicability
in light of many practical and technical challenges. One of the challenges is
improving performance when responding to large number of context queries.
Context Management Platforms that infer and deliver context to applications
measure this problem using Quality of Service (QoS) parameters. Although
caching is a proven way to improve QoS, transiency of context and features such
as variability, heterogeneity of context queries pose an additional real-time
cost management problem. This paper presents a critical survey of
state-of-the-art in adaptive data caching with the objective of developing a
body of knowledge in cost- and performance-efficient adaptive caching
strategies. We comprehensively survey a large number of research publications
and evaluate, compare, and contrast different techniques, policies, approaches,
and schemes in adaptive caching. Our critical analysis is motivated by the
focus on adaptively caching context as a core research problem. A formal
definition for adaptive context caching is then proposed, followed by
identified features and requirements of a well-designed, objective optimal
adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys
Journal at this time of publishing in arxiv.or
Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures
The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel
IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT servicesPostprint (author's final draft
An LSH-based offloading method for IoMT services in integrated cloud-edge environment
© 2021 ACM. Benefiting from the massive available data provided by Internet of multimedia things (IoMT), enormous intelligent services requiring information of various types to make decisions are emerging. Generally, the IoMT devices are equipped with limited computing power, interfering with the process of computation-intensive services. Currently, to satisfy a wide range of service requirements, the novel computing paradigms, i.e., cloud computing and edge computing, can potentially be integrated for service accommodation. Nevertheless, the private information (i.e., location, service type, etc.) in the services is prone to spilling out during service offloading in the cloud-edge computing. To avoid privacy leakage while improving service utility, including the service response time and energy consumption for service executions, a Locality-sensitive-hash (LSH)-based offloading method, named LOM, is devised. Specifically, LSH is leveraged to encrypt the feature information for the services offloaded to the edge servers with the intention of privacy preservation. Eventually, comparative experiments are conducted to verify the effectiveness of LOM with respect to promoting service utility
Hierarchical and Decentralised Federated Learning
Federated learning has shown enormous promise as a way of training ML models
in distributed environments while reducing communication costs and protecting
data privacy. However, the rise of complex cyber-physical systems, such as the
Internet-of-Things, presents new challenges that are not met with traditional
FL methods. Hierarchical Federated Learning extends the traditional FL process
to enable more efficient model aggregation based on application needs or
characteristics of the deployment environment (e.g., resource capabilities
and/or network connectivity). It illustrates the benefits of balancing
processing across the cloud-edge continuum. Hierarchical Federated Learning is
likely to be a key enabler for a wide range of applications, such as smart
farming and smart energy management, as it can improve performance and reduce
costs, whilst also enabling FL workflows to be deployed in environments that
are not well-suited to traditional FL. Model aggregation algorithms, software
frameworks, and infrastructures will need to be designed and implemented to
make such solutions accessible to researchers and engineers across a growing
set of domains.
H-FL also introduces a number of new challenges. For instance, there are
implicit infrastructural challenges. There is also a trade-off between having
generalised models and personalised models. If there exist geographical
patterns for data (e.g., soil conditions in a smart farm likely are related to
the geography of the region itself), then it is crucial that models used
locally can consider their own locality in addition to a globally-learned
model. H-FL will be crucial to future FL solutions as it can aggregate and
distribute models at multiple levels to optimally serve the trade-off between
locality dependence and global anomaly robustness.Comment: 11 pages, 6 figures, 25 reference
Leveraging Resources on Anonymous Mobile Edge Nodes
Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network.
We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge?
Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications.
In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network.
In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible
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Modeling industry 4.0 based fog computing environments for application analysis and deployment
The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical run-time parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with tailored optimization methods than can handle the heterogeneity and scale of the fog can improve the overall system run-time parameters and improve constraint satisfaction. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 90% accurate on load and delay predictions for application deployments in both cloud and edge
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