| 1. |
- Baker, Thar, et al.
(författare)
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Enabling Technologies for Energy Cloud
- 2021
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 152, s. 108-110
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We are thrilled and delighted to present this special issue, which emphasises on the novel area of Enabling Technologies for Energy Cloud. This guest editorial provides an overview of all articles accepted for publication in this special issue.
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| 2. |
- Dhamal, Swapnil Vilas, 1988, et al.
(författare)
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Strategic Investments in Distributed Computing: A Stochastic Game Perspective
- 2022
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 1096-0848 .- 0743-7315. ; 169, s. 317-333
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Tidskriftsartikel (refereegranskat)abstract
- We study a stochastic game with a dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving a problem, while incurring a certain cost based on the invested time and computational power. We present our framework while considering a contemporary application of blockchain mining, and show that the framework is applicable to certain other distributed computing settings as well. For an in-depth analysis, we consider a particular yet natural scenario where the rate of solving the problem is proportional to the total computational power invested by the players. We show that, in Markov perfect equilibrium, players with cost parameters exceeding a certain threshold, do not invest; while those with cost parameters less than this threshold, invest maximal power. We arrive at an interesting conclusion that the players need not have information about the system state as well as each others' parameters, namely, cost parameters and arrival/departure rates. With extensive simulations and insights through mean field approximation, we study the effects of players' arrival/departure rates and the system parameters on the players' utilities.
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| 3. |
- Feliu, Josue, et al.
(författare)
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Speculative inter-thread store-to-load forwarding in SMT architectures
- 2023
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 173, s. 94-106
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Tidskriftsartikel (refereegranskat)abstract
- Applications running on out-of-order cores have benefited for decades of store-to-load forwarding which accelerates communication of store values to loads of the same thread. Despite threads running on a simultaneous multithreading (SMT) core could also access the load queues (LQ) and store queues (SQ) / store buffers (SB) of other threads to allow inter-thread store-to-load forwarding, we have skipped exploiting it because if we allow communication of different SMT threads via their LQs and SQs/SBs, write atomicity may be violated with respect to the outside world beyond the acceptable model of read -own-write-early multiple-copy atomicity (rMCA).In our prior work, we leveraged this idea to propose inter-thread store-to-load forwarding (ITSLF). ITLSF accelerates synchronization and communication of threads running in a simultaneous multi-threading processor by allowing stores in the store-queue of a thread to forward data to loads of another thread running in the same core without violating rMCA.In this work, we extend the original ITSLF mechanism to allow inter-thread forwarding from speculative stores (Spec-ITSLF). Spec-ITSLF allows forwarding store values to other threads earlier, which further accelerates synchronization. Spec-ITSLF outperforms a baseline SMT core by 15%, which is 2% better on average (and up to 5% for the TATP workload) than the original ITSLF mechanism. More importantly, Spec-ITSLF is on par with the original ITSLF mechanism regarding storage overhead but does not need to keep track of the speculative state of stores, which was an important source of overhead and complexity in the original mechanism. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 4. |
- Keramatian, Amir, 1990, et al.
(författare)
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MAD-C: Multi-stage Approximate Distributed Cluster-combining for obstacle detection and localization
- 2021
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 1096-0848 .- 0743-7315. ; 147, s. 248-267
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Tidskriftsartikel (refereegranskat)abstract
- The upcoming digitalization in the context of Cyber-physical Systems (CPS), enabled through Internet-of-Things (IoT) infrastructures, require efficient methods for distributed processing of the data, that is generated by multiple sources. We address the problem of obstacle detection and localization through data clustering, which is a common component for data processing in the fusion of multiple point clouds, each obtained by a LIDAR sensor. Such sensors generate data at high rates and can rapidly exhaust traditional methods that centrally gather and process the global data. To that end, we propose MAD-C, an approximate method for distributed data summarization through clustering, that can orthogonally build on known methods for fine-grained point-cloud clustering, and synthesize a decentralized approach, which exploits the distributed processing capacity efficiently and prevents saturation of the communication network. In MAD-C, corresponding to the point-cloud gathered by each LIDAR sensor, local clusters are first identified, each corresponding to an object in the sensed environment from the perspective of the respective sensor. Afterwards, the information about each locally detected object is transformed into a data-summary, computable in a continuous manner, with constant overhead in time and space. The summaries are then combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects in the fused data. We show that the combined summaries, in addition to localizing objects and approximating their volumetric representations, can be used to answer relevant queries regarding the relative position of the objects in environment and a geofence. We evaluate the performance of MAD-C extensively, both analytically and empirically. The empirical evaluation is performed on an IoT test-bed as well as in simulation. Our results show that MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with Randlndex > 0.95), in comparison to its baseline counterpart for obstacle detection and localization, as well as (iv) linear computational complexity in terms of the number of objects, for the geofence related queries.
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| 5. |
- Keramatian, Amir, 1990, et al.
(författare)
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PARMA-CC: A Family of Parallel Multiphase Approximate Cluster Combining Algorithms
- 2023
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 1096-0848 .- 0743-7315. ; 177, s. 68-88
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Tidskriftsartikel (refereegranskat)abstract
- Clustering is a common task in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g., rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g., detecting critical situations such as detecting boundary crossing from a robot arm that could injure human beings) demand for efficient data clustering algorithms that can effectively utilize the increasing computational capacities of modern hardware. To that end, we leverage approximation and parallelization, where the former is to scale down the amount of data, and the latter is to scale up the computation. Regarding parallelization, we explore a design space for synchronization and workload distribution among the threads. As we study different parts of the design space, we propose representative Parallel Multiphase Approximate Cluster Combining, abbreviated as PARMA-CC, algorithms. We show that PARMA-CC algorithms yield equivalent clustering outcomes despite their different approaches. Furthermore, we show that certain PARMA-CC algorithms can achieve higher efficiency with respect to certain properties of the data to be clustered. Generally speaking, in PARMA-CC algorithms, parallel threads compute summaries associated with clusters of data (sub)sets. As the threads concurrently combine the summaries, they construct a comprehensive summary of the sets of clusters. By approximating a cluster with its respective geometrical summaries, PARMA-CC algorithms scale well with increased data volumes, and, by computing and efficiently combining the summaries in parallel, they enable latency improvements. PARMA-CC algorithms utilize special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC algorithms can complement and outperform well-established methods, with significantly better scalability, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour.
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| 6. |
- Lo Bello, L., et al.
(författare)
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Schedulability analysis of Time-Sensitive Networks with scheduled traffic and preemption support
- 2020
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press Inc.. - 0743-7315 .- 1096-0848. ; 144, s. 153-171
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Tidskriftsartikel (refereegranskat)abstract
- The Time-Sensitive Networking (TSN) set of standards introduces in IEEE 802.1 switches and end stations novel features to meet the requirements of a broad spectrum of applications that are characterized by time-sensitive and mission-critical traffic flows. In particular, the IEEE802.1Qbv-2015 amendment introduces enhancements that provide temporal isolation for scheduled traffic, i.e., a traffic class that requires transmission based on a known timescale, while the IEEE802.1Qbu-2016 introduces preemption as a mechanism to allow time-critical messages to interrupt ongoing non time-critical transmissions. Both amendments, that are now enrolled in the IEEE802.1Q-2018 standard, are very important for industrial networks, where scheduled traffic and low-latency real-time flows have to coexist, on the same network, with best-effort transmissions. In this context, this work presents a response time analysis of TSN networks that encompasses the enhancements for scheduled traffic and preemption, in various combinations. The paper presents the proposed analysis and a performance comparison between the response times calculated by the analysis and the response times obtained through OMNeT++ simulations in three different scenarios. © 2020 Elsevier Inc.
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| 7. |
- Mohammadi, Samaneh, et al.
(författare)
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Balancing Privacy and Performance in Federated Learning : a Systematic Literature Review on Methods and Metrics
- 2024
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press Inc.. - 0743-7315 .- 1096-0848. ; 192
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Tidskriftsartikel (refereegranskat)abstract
- Federated Learning (FL) has emerged as a novel paradigm in the area of Artificial Intelligence (AI), emphasizing decentralized data utilization and bringing learning to the edge or directly on-device. While this approach eliminates the need for data centralization, ensuring enhanced privacy and protection of sensitive information, it is not without challenges. Particularly during the training phase and the exchange of model update parameters between servers and clients, new privacy challenges have arisen. While several privacy-preserving FL solutions have been developed to mitigate potential breaches in FL architectures, their integration poses its own set of challenges. Incorporating these privacy-preserving mechanisms into FL at the edge computing level can increase both communication and computational overheads, which may, in turn, compromise data utility and learning performance metrics. This paper provides a systematic literature review on essential methods and metrics to support the most appropriate trade-offs between FL privacy and other performance-related application requirements such as accuracy, loss, convergence time, utility, communication, and computation overhead. We aim to provide an extensive overview of recent privacy-preserving mechanisms in FL used across various applications, placing a particular focus on quantitative privacy assessment approaches in FL and the necessity of achieving a balance between privacy and the other requirements of real-world FL applications. This review collects, classifies, and discusses relevant papers in a structured manner, emphasizing challenges, open issues, and promising research directions.
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| 8. |
- Moradi, Fereidoun, et al.
(författare)
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Tiny Twins for detecting cyber-attacks at runtime using concise Rebeca time transition system
- 2024
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press Inc.. - 0743-7315 .- 1096-0848. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a method for detecting cyber-attacks in cyber-physical systems using a monitor. The method employs an abstract model called Tiny Twin, which is built at design time and is used at runtime to detect inconsistencies. Tiny Twin is a state transition system that represents the observable behavior of the system from the monitor point of view. We model the behavior of the system in the Rebeca modeling language and use Afra model checker to generate the state space. The Tiny Twin is built automatically, by abstracting the state space while keeping the observable actions and preserving the trace equivalence. For doing that we had to solve the complexities in the state space introduced by time-shifts, nondeterministic assignments and abstraction of internal actions. We formally define the state space as Concise Rebeca Timed Transition System (CRTTS), and then map CRTTS to an LTS. The LTS is then fed to a tool to abstract away the non-observable actions.
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| 9. |
- Nejat, Mehrzad, 1989, et al.
(författare)
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Coordinated management of DVFS and cache partitioning under QoS constraints to save energy in multi-core systems
- 2020
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 1096-0848 .- 0743-7315. ; 144, s. 246-259
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Tidskriftsartikel (refereegranskat)abstract
- Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage–frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage–Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage–frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time.
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| 10. |
- Seisa, Achilleas Santi, et al.
(författare)
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An Edge Architecture for Enabling Autonomous Aerial Navigation with Embedded Collision Avoidance Through Remote Nonlinear Model Predictive Control
- 2024
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 188
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.
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