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Jinlai Xu

Bio: Jinlai Xu is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Cloud computing & Resource allocation. The author has an hindex of 4, co-authored 10 publications receiving 131 citations. Previous affiliations of Jinlai Xu include University UCINF & China University of Geosciences (Wuhan).

Papers
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Proceedings ArticleDOI
25 Jun 2017
TL;DR: Zenith is proposed, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori and employs a latency-aware scheduling and resource provisioning algorithm.
Abstract: In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model.

153 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: A new contracts-based resource sharing model for federated geo-distributed clouds is proposed that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period.
Abstract: Cloud computing and its pay-as-you-go model continue to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual cloud service providers raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing that results in significant inefficiencies in local resource allocation for individual datacenters leading to unfairness in revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements. The proposed techniques are evaluated through extensive experiments using realistic workloads and the results demonstrate the effectiveness, scalability and resource sharing efficiency of the proposed model.

13 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: PADS is proposed, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism.
Abstract: With the rapid growth of Cloud Computing technologies, enterprises are increasingly deploying their services in the Cloud. Dynamically priced cloud resources such as the Amazon EC2 Spot Instance provides an efficient mechanism for cloud service providers to trade resources with potential buyers using an auction mechanism. With the dynamically priced cloud resource markets, cloud consumers can buy resources at a significantly lower cost than statically priced cloud resources such as the on-demand instances in Amazon EC2. While dynamically priced cloud resources enable to maximize datacenter resource utilization and minimize cost for the consumers, unfortunately, such auction mechanisms achieve these benefits only at a cost significant of private information leakage. In an auction-based mechanism, the private information includes information on the demands of the consumers that can lead an attacker to understand the current computing requirements of the consumers and perhaps even allow the inference of the workload patterns of the consumers. In this paper, we propose PADS, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism. We demonstrate that PADS achieves differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue gains and allocation efficiency. We evaluate PADS through extensive simulation experiments that demonstrate that in comparison to traditional auction mechanisms, PADS achieves relatively high revenues for cloud providers while guaranteeing the privacy of the participating consumers.

12 citations

Proceedings ArticleDOI
26 Apr 2021
TL;DR: The SteemOps dataset as discussed by the authors collects over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extracts ten key types of operations performed by the users.
Abstract: Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

12 citations

Journal ArticleDOI
TL;DR: A new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period is proposed.
Abstract: In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go model continues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources towards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies in local resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model.

10 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides a tutorial on fog computing and its related computing paradigms, including their similarities and differences, and provides a taxonomy of research topics in fog computing.

783 citations

Journal ArticleDOI
TL;DR: This paper provides a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well.
Abstract: In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

228 citations

Journal ArticleDOI
TL;DR: This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC), and proposes an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization.
Abstract: With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

159 citations

Journal ArticleDOI
TL;DR: The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.
Abstract: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

158 citations

Journal ArticleDOI
TL;DR: This paper introduces FOGPLAN, a framework for QoS-aware dynamic fog service provisioning (QDFSP), and presents a possible formulation and two efficient greedy algorithms for addressing the QDFSP at one instance of time.
Abstract: Recent advances in the areas of Internet of Things (IoT), big data, and machine learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring quality of service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user. In this paper, we first introduce FOGPLAN, a framework for QoS-aware dynamic fog service provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

143 citations