2018 27th International Conference on Computer Communication and Networks (ICCCN)最新文献

{"title":"Opportunistic Prefetching of Cellular Internet of Things (cIoT) Device Contexts","authors":"Srinivasan Iyengar, V. Gurbani, Yu Zhou, Sameerkumar Sharma","doi":"10.1109/ICCCN.2018.8487456","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487456","url":null,"abstract":"The number of IoT devices is expected to be between 32-99 Billion by 2025, many of which will use the cellular wireless data network for communications. This presents a unique challenge to the operator while allocating resources, namely how to optimally balance CPU and memory usage in virtualized and physical hosts while simultaneously handling millions of IoT devices without affecting the quality of experience of normal mobile users. Due to the sheer number of the IoT devices, it is not feasible to store their session context in memory. In this work, we present a machine learning model that predicts the network usage pattern of five broad classes of cIoT devices. The prediction model trained on a Multilayer Perceptron allows the network operator to opportunistically prefetch cIoT context from secondary storage before it is required. Further, we propose a new metric -- Value of Perfect Information -- to assess our approach. We evaluate our approach across two fronts: First, we study the efficacy of replacement algorithms such as LRU, MRU, FIFO and random replacement; we also assess the impact of varying memory slots. Finally, we evaluate our models against the default (no prefetching) model and an on-time prefetching model to demonstrate the value of our pre-fetching approach.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114895941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Scalable In-Network Operator Placement for Edge Computing","authors":"Julien Gedeon, Michael Stein, L. Wang, M. Mühlhäuser","doi":"10.1109/ICCCN.2018.8487419","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487419","url":null,"abstract":"The drawbacks encountered in today's cloud computing infrastructures have led to a paradigm shift towards in-network processing, where resources in the core and at the edge of the network are leveraged to perform computations. This can lead to decreased costs and better quality of service for users, e.g., when latency-critical applications are executed close to data sources and users. Deploying applications or parts thereof on these infrastructures requires to place operators (i.e., functional components of applications) on available resources in the network. Solving large instances of this problem in an optimal way is known to be computationally hard and, thus, practically unfeasible. While heuristic approaches exist, they mostly aim at placing functionalities on homogeneous nodes or make unrealistic assumptions for edge computing environments. To address this issue, this paper studies the placement problem in the context of a 3-tier architecture consisting of cloud, fog and edge devices. We provide a comprehensive model and propose a heuristic approach to the problem, in which we introduce constraints on the placement decision to limit the possible solution space, leading to a decrease in the solving time for the problem. These constraints exploit the characteristics of our 3-tier network architecture. To demonstrate the feasibility of the approach, we present a general framework that supports different types of heuristics. We validate the approach by implementing example heuristics for each type. We show that our approach can scale to large instances, i.e., it can significantly reduce the resolution time to find a placement solution while introducing only a small optimality gap.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"88 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Boundless Resource Orchestrator Based on Container Technology in Edge Computing","authors":"Zhenguang Yu, Jingyu Wang, Q. Qi, Haifeng Sun, Jian Zou","doi":"10.1109/ICCCN.2018.8487377","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487377","url":null,"abstract":"Edge computing reorganizes edge resources but is unstable and unreliable at present, thus, orchestration may occur occasionally. This paper provides an architecture named Boundless Resource Orchestrator (BRO) combing cloud computing and edge computing based on containers. The proposed architecture leverages container technology to accelerate and optimize the orchestration process. A master-slave paradigm is implemented in the architecture to provide region autonomy abilities rather than the centralized architecture. Considering the ever-changing circumstance of edge cloud, an orchestration strategy named Best Performance at Least Cost (BPLC) is proposed to maximize the performance of computing at minimum cost dynamically and automatically. Experiments are carried out on measuring couples of infrastructures and orchestration strategies that prove the BRO and BPLC as prior choices dealing with massive jobs in edge computing.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126554334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Channel Access Priority Classes in LTE-LAA Spectrum Sharing System","authors":"Yao Ma","doi":"10.1109/ICCCN.2018.8487443","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487443","url":null,"abstract":"To provide differentiated quality of service in long-term evolution (LTE) license assisted access (LAA) procedure, the 3GPP has defined several channel access priority classes (CAPCs). They use distinct arbitration inter-frame space (AIFS), contention window (CW) size, and payload duration. While evaluating the effects of CW size and payload duration is relatively straightforward, accurately modelling and analyzing the effect of AIFS has not been satisfactorily addressed. Available methods on analyzing different AIFSs are accurate for only limited parameter setups, or involve systematic approximations. Different from existing results, we develop a non-homogeneous per-slot Markov chain model to represent the state of each priority class during and after the AIFS, and analyze the channel access probability (CAP), successful transmission probability (STP), and average throughput of each class. Some novel features of our method include: 1) we model and solve the per-slot class-dependent link statistics (such as CAP and STP), which vary based on the slot location; and 2) we provide an in-depth analysis on the average throughput, and design a multi-class combinatorial procedure to evaluate average time spent per successful transmission on each delay cell. We program the LAA CAPC algorithms and implement extensive Monte Carlo simulations, which validate the accuracy of our analytical results even in very low throughput region for lower priority classes, and demonstrate the effects of AIFS and other parameters in an LAA system. These results provide solid progress for evaluating priority classes in the LTE-LAA system and other spectrum sharing systems, and can be extended to support system and parameter optimization.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122571140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Big Data Collecting Method Based on Compressive Sensing in WSN","authors":"De-gan Zhang, Xiao-hua Liu, Yu-ya Cui, Hong-tao Peng","doi":"10.1109/ICCCN.2018.8487389","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487389","url":null,"abstract":"Considered the wireless sensor network clustering structure, a new big data collecting method based on compressive sensing is proposed. The collection process is as follows: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network, and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector, and collect data through compressive sensing technology; Among clusters, clusters forward measurement values to sink node along multi-hop routing tree which we built before. Performance analyzing and comparison of results show that this method is superior to other methods regardless of in a cluster or inter-cluster.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survivability Model for Security and Dependability Analysis of a Vulnerable Critical System","authors":"Xiaolin Chang, Shaohua Lv, R. Rodríguez, Kishor S. Trivedi","doi":"10.1109/ICCCN.2018.8487446","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487446","url":null,"abstract":"This paper aims to analyze transient security and dependability of a vulnerable critical system, under vulnerability-related attack and two reactive defense strategies, from a severe vulnerability announcement until the vulnerability is fully removed from the system. By severe, we mean that the vulnerability-based malware could cause significant damage to the infected system in terms of security and dependability while infecting more and more new vulnerable computer systems. We propose a Markov chain-based survivability model for capturing the vulnerable critical system behaviors during the vulnerability elimination process. A high-level formalism based on Stochastic Reward Nets is applied to automatically generate and solve the survivability model. Survivability metrics are defined to quantify system attributes. The proposed model and metrics not only enable us to quantitatively assess the system survivability in terms of security risk and dependability, but also provide insights on the system investment decision. Numerical experiments are constructed to study the impact of key parameters on system security, dependability and profit.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126475570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Scalable Computing Architectures for Interactions Analysis","authors":"Taruna Seth, Chao Feng, M. Ramanathan, V. Chaudhary","doi":"10.1109/ICCCN.2018.8487405","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487405","url":null,"abstract":"Characterization of pharmacological signal transductions leading to drug-induced expressions of genes and proteins requires the capability to identify interactions among different potential predictor components, e.g. genomic data, clinical data, and environmental data. The detection of these gene-gene and gene-environment interactions remains challenging due to the exponential computational complexity and high dimensionality of the interaction problem. The problem is further exacerbated due to the involvement of very large-scale epidemiological datasets. Efficient high-order interaction analysis of such large-scale data is not feasible with the traditional frameworks. Parallel implementations of such applications in traditional cluster environments are often inefficient due to the storage bandwidth and network I/O limitations. Scalable distributed platforms can offer better scalability to such problems compared to the cluster architectures. Moreover, such data- and compute- intensive problems can benefit even further from data-intensive supercomputing (DISC) architectures that have been shown to yield superior performance compared to the regularly used cluster platforms. In this paper, we evaluate the applicability of different architectures such as traditional server based distributed architectures supported on commodity hardware and shared nothing architectures with massively parallel processing capabilities, towards the Interaction Analysis problem. Our experiments show that the massively parallel processing, shared-nothing architecture outweigh the benefits often realized through traditional server based and even distributed computing architectures. We conclude that the rapidly growing class of shared nothing architectures offers a potentially efficient and viable alternative to facilitate high-order interaction analysis involving extremely large scale biological datasets and is well suited to this category of data- and compute- intensive problems that cannot be addressed effectively using traditional frameworks.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125263795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximating the k-Minimum Distance Rumor Source Detection in Online Social Networks","authors":"Soklong Lim, Jun Hao, Zaixin Lu, Xuechen Zhang, Zhao Zhang","doi":"10.1109/ICCCN.2018.8487400","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487400","url":null,"abstract":"Online Social networks (OSNs) are now one of the main resources for people to keep abreast of current news and to exchange opinions about new products and social trends, etc. However, unethical use of OSNs also provides a convenient conduit to the diffusion of malicious rumors and misinformation, thus it is of significant importance to discover rumor diffusion and detect the rumor source. This is a very challenging task, as shown in many existing works, e.g., even in the regular tree graphs, the accuracy of detecting the information source from a diffusion snapshot cannot exceed 31%. To overcome this issue, in this work, we propose a novel system framework for information source detection in OSNs and investigate a new rumor source detection problem, called $k$-Minimum Distance Rumor Source Detection (k-MDRSD). Specifically, given a rumor spreading snapshot, our target is to find a small set of rumor candidates which can be used as initial seeds for further iterative query or investigation. To this end, we introduce a notion, called distance error, for rumor candidate sets and formulate the k-MDRSD problem. Resorting to methods from Combinatorics, we develop a near optimal algorithm for k-MDRSD. By experimental simulation, we show that the proposed k-MDRSD significantly improves the likelihood of detecting rumor sources or trend-setters in OSNs.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"18 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Layer Self-Similar Coflow Scheduling for Machine Learning Clusters","authors":"Guang Yang, Yong Jiang, Qing Li, Xuya Jia, Mingwei Xu","doi":"10.1109/ICCCN.2018.8487329","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487329","url":null,"abstract":"In recent years, many companies have developed various distributed computation frameworks for processing machine learning (ML) jobs in clusters. Networking is a well-known bottleneck for ML systems and the cluster demands efficient scheduling for huge traffic (up to 1GB per flow) generated by ML jobs. Coflow has been proven an effective abstraction to schedule flows of such data-parallel applications. However, the implementation of coflow scheduling policy is constrained when coflow characteristics are unknown a prior, and when TCP congestion control misinterprets the congestion signal leading to low throughput. Fortunately, traffic patterns experienced by some ML jobs support to speculate the complete coflow characteristic with limited information. Hence this paper summarizes coflow from these ML jobs as self-similar coflow and proposes a decentralized self-similar coflow scheduler Cicada. Cicada assigns each coflow a probe flow to speculate its characteristics during the transportation and employs the Shortest Job First (SJF) to separate coflow into strict priority queues based on the speculation result. To achieve full bandwidth for throughput- sensitive ML jobs, and to guarantee the scheduling policy implementation, Cicada promotes the elastic transport-layer rate control that outperforms prior works. Large-scale simulations show that Cicada completes coflow 2.08x faster than the state-of-the-art schemes in the information-agnostic scenario.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127054826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards 3D Deployment of UAV Base Stations in Uneven Terrain","authors":"Xiaofei He, Wei Yu, Hansong Xu, Jie Lin, Xinyu Yang, Chao Lu, Xinwen Fu","doi":"10.1109/ICCCN.2018.8487319","DOIUrl":"https://doi.org/10.1109/ICCCN.2018.8487319","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs), also known as drones, have become a new paradigm to provide emergency wireless communication infrastructure when conventional base stations are damaged or unavailable. In this paper, we propose new schemes to enable the 3D deployment of drones, which can provide network coverage and connectivity services for users located in uneven terrain. We formalize two models, including optimal coverage model and optimal connectivity model, which belong to NP-hard. To be specific, we first consider both the quality of service (QoS) requirements of users and the capacity of drones. We then formalize the problem and design a heuristic scheme, called Particle Swarm Optimization (PSO) algorithm to achieve a cost-effective solution. We also address the optimal connectivity problem in a scenario, in which a number of isolated local networks have been established by users through ad hoc communication and/or device-to-device (D2D) communication. We further develop the cost-effective heuristic algorithm to effectively minimize the total number of required drones. Via extensive performance evaluation, our experimental results demonstrate that the proposed schemes can achieve the effective deployment of drones for users in uneven terrain with respect to the number of required drones.","PeriodicalId":399145,"journal":{"name":"2018 27th International Conference on Computer Communication and Networks (ICCCN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}