The proceedings contain 63 papers. The topics discussed include: post quantum communication over the internet infrastructure;efficient and resilient edge computing: algorithms, techniques and research opportunities;se...
ISBN:
(纸本)9798400716737
The proceedings contain 63 papers. The topics discussed include: post quantum communication over the internet infrastructure;efficient and resilient edge computing: algorithms, techniques and research opportunities;self-stabilizing byzantine multivalued consensus;a further study on weak byzantine gathering of mobile agents;distributed uniform partitioning of a region using opaque ASYNC luminous mobile robots;space and move-optimal arbitrary pattern formation on a rectangular grid by robot swarms;maximal independent set via mobile agents;collision-free linear time mutual visibility for asynchronous fat robots;arbitrary pattern formation on a continuous circle by oblivious robot swarm;sublinear message bounds of authenticated implicit byzantine agreement;and LightKey: lightweight and secure key agreement protocol for effective communication in internet of vehicles.
With the continuous development of artificial intelligence, the research and application of automated connected driving is receiving more and more attention. The combination of mobile edge computing and Telematics pro...
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In order to solve the problem of high memory usage and large GPU computation in the case of a single machine. In this paper, MobileNet and Pytorch deep learning framework and Flink big data computing framework are dee...
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With high scalability and flexibility, serverless computing is becoming the most promising computing model. Existing serverless computing platforms initiate a container for each function invocation, which leads to a h...
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ISBN:
(纸本)9798350339864
With high scalability and flexibility, serverless computing is becoming the most promising computing model. Existing serverless computing platforms initiate a container for each function invocation, which leads to a huge waste of computing resources. Our examinations reveal that (i) executing invocations concurrently within a single container can provide comparable performance to that provided by multiple containers (i.e., traditional approaches);(ii) redundant resources generated within a container result in memory resource waste, which prolongs the execution time of function invocations. Motivated by these insightful observations, we propose FaaSBatch - a serverless framework that reduces invocation latency and saves scarce computing resources. In particular, FaaSBatch first classifies concurrent function requests into different function groups according to the invocation information. Next, FaaSBatch batches the invocations of each group, aiming to minimize resource utilization. Then, FaaSBatch utilizes an inline parallel policy to map each group of batched invocations into a single container. Finally, FaaSBatch expands and executes invocations of containers in parallel. To further reduce invocation latency and resource utilization, within each container, FaaSBatch reuses redundant resources created during function execution. We conduct extensive experiments based on Azure traces to evaluate the effectiveness and performance of FaaSBatch. We compare FaaSBatch with three state-of-the-art schedulers Vanilla, SFS, and Kraken. Our experimental results show that FaaSBatch effectively and remarkably slashes invocation latency and resource overhead. For instance, when executing I/O functions, FaaSBatch cuts back the invocation latency of Vanilla, SFS, and Kraken by up to 92.18%, 89.54%, and 90.65%, respectively;FaaSBatch also slashes the resource overhead of Vanilla, SFS, and Kraken by 58.89% to 94.77%, 43.72% to 90.39%, and 42.99% to 78.88%, respectively.
Beyond 5G and 6G networks are foreseen to be highly dynamic. These are expected to support and accommodate temporary activities and leverage continuously changing infrastructures from extreme edge to cloud. In additio...
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ISBN:
(纸本)9783031488023;9783031488030
Beyond 5G and 6G networks are foreseen to be highly dynamic. These are expected to support and accommodate temporary activities and leverage continuously changing infrastructures from extreme edge to cloud. In addition, the increasing demand for applications and data in these networks necessitates the use of geographically distributed Multi-access Edge computing (MEC) to provide reliable services with low latency and energy consumption. Service management plays a crucial role in meeting this need. Research indicates widespread acceptance of Reinforcement Learning (RL) in this field due to its ability to model unforeseen scenarios. However, it is difficult for RL to handle exhaustive changes in the requirements, constraints and optimization objectives likely to occur in widely distributed networks. Therefore, the main objective of this research is to design service management approaches to handle changing services and infrastructures in dynamic distributed MEC systems, utilizing advanced RL methods such as distributed Deep Reinforcement Learning (DDRL) and Meta Reinforcement Learning (MRL).
Inconsistent State of Charge (SOC) of paralleldistributed Energy Storage (DES) can cause issues in microgrid stability and energy storage battery lifespan when using conventional Droop with fixed droop coefficients. ...
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ISBN:
(纸本)9798350363272;9798350363265
Inconsistent State of Charge (SOC) of paralleldistributed Energy Storage (DES) can cause issues in microgrid stability and energy storage battery lifespan when using conventional Droop with fixed droop coefficients. DES with low SOC value may discharge prematurely and exit from operation, disrupting the microgrid's stable operation. To address this, this paper proposes an adaptive Droop control strategy based on dynamic correction coefficients that consider the local SOC of each DES and coordinate their output, to maintain stable microgrid operation. Its efficacy is confirmed through simulations on the PSCAD/EMTDC platform.
作者：
Hu, YaoKeio University
Research Institute for Digital Media and Content Hiyoshi Campus Yokohama223-8523 Japan
A random walk is a process in which a random walker takes consecutive steps in space at equal intervals of time, with the length and direction of each step determined independently. Models related to random walks have...
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The National Science Data Fabric (NSDF) is our solution to the problem of addressing the data-sharing needs of the growing data science community. NSDF is designed to make sharing data across geographically distribute...
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ISBN:
(纸本)9798400701559
The National Science Data Fabric (NSDF) is our solution to the problem of addressing the data-sharing needs of the growing data science community. NSDF is designed to make sharing data across geographically distributed sites easier for users who lack technical expertise and infrastructure. By developing an easy-to-install software stack, we promote the FAIR data-sharing principles in NSDF while leveraging existing high-speed data transfer infrastructures such as Globus and XRootD. This work shows how we leverage latency and throughput information between geo-distributed NSDF sites with NSDF entry points to optimize the automatic coordination of data placement and transfer across the data fabric, which can further improve the efficiency of data sharing.
distributedcomputing platforms involve multiple processing systems connected through a network and support the parallel execution of applications. They enable huge computational power and data processing with a quick...
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ISBN:
(纸本)9783031505829;9783031505836
distributedcomputing platforms involve multiple processing systems connected through a network and support the parallel execution of applications. They enable huge computational power and data processing with a quick response time. Examples of use cases requiring distributedcomputing are stream processing, batch processing, and client-server models. Most of these use cases involve tasks executed in a sequence on different computers to arrive at the results. Numerous distributedcomputing algorithms have been suggested in the literature, focusing on efficiently utilizing compute nodes to handle tasks within a workflow on on-premises setups. Industries that previously relied on on-premises setups for big data processing are shifting to cloud environments offered by providers such as Azure, Amazon, and Google. This transition is driven by the convenience of Platform-as-a-Service offerings scuh as Batch Services, Hadoop, and Spark. These PaaS services, coupled with auto-provisioning and auto-scaling, reduce costs through a Pay-As-You-Go model. However, a significant challenge with cloud services is configuring them with only a single type of machine for performing all the tasks in the distributed workflow, although each task has diverse compute node requirements. To address this issue in this paper, we propose an Intelligent task scheduling framework that uses a classifier-based dynamic task scheduling approach to determine the best available node for each task. The proposed framework improves the overall performance of the distributedcomputing workflow by optimizing task allocation and utilization of resources. Although Azure Batch Service is used in this paper to illustrate the proposed framework, our approach can also be implemented on other PaaS distributedcomputing platforms.
One way for supporting incremental checkpointing is the exploitation of classical memory protection services - in particular the mprotect (...) system call offered by Posix compliant operating systems - for intercepti...
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