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Priority Heuristic in Mobile distributed Real Time Database Using Optimistic Concurrency Control

Priority Heuristic in Mobile Distributed Real Time Database ...

引用

international conference on Advanced computing and Communications (ADCOM)

作者： Prakash Kumar Singh Udai Shanker Department of Computer Science and Engineering M. M. M. University of Technology Gorakhpur India

ISBN: (纸本)9781538669129;9781538669112

Priority scheduling among running transactions is one of the most important issues in the design of mobile distributed real-time database systems (MDRTDBS). In MDRTDBS, to perform correct transaction scheduling, several priority heuristics with different concurrency control methods are used, so that, it could minimize transaction abort rate. Priority heuristic approaches deal with problem of assigning the priorities among transactions so that it could helpful with concurrency control (CC) mechanism to achieve typical time constraint. In recent few years, the performance of CC protocols of distributed real-time database systems (DRTDBS) have been examined using different priority heuristics methods. However, very few numbers of approaches have been proposed on priority heuristics for wireless environments. Hence, a new priority heuristic method based on deadline computed by considering write only operations has been proposed for wireless environments. It is improved priority heuristic approach which shows better results than earlier priority heuristics. In recent years, researchers have classified the transactions in two types called as Read only transactions (ROT) and Update transaction. This new priority heuristic for mobile environment considers ROT and update transactions separately. Further, a study has also been done to examine the impact of these priority heuristics as compared with number of locks and mixed method approaches.

关键词： Real-time systems Servers distributed databases Wireless communication Concurrency control Broadcasting

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Parallel I/O Optimizations for Scalable Deep Learning

Parallel I/O Optimizations for Scalable Deep Learning

引用

international conference on Parallel and distributed systems (ICPADS)

作者： Sarunya Pumma Min Si Wu-chun Feng Pavan Balaji Virginia Tech USA Argonne National Laboratory USA

ISBN: (纸本)9781538632086

As deep learning systems continue to grow in importance, researchers have been analyzing approaches to make such systems efficient and scalable on high-performance computing platforms. As computational parallelism increases, however, data I/O becomes the major bottleneck limiting the overall system scalability. In this paper, we continue our efforts to improve LMDB, the I/O subsystem of the Caffe deep learning framework. In a previous paper we presented LMDBIO---an optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Nevertheless, LMDBIO's optimizations, which we henceforth call LMM (localized mmap), are limited to intranode performance, and these optimizations do little to minimize the I/O inefficiencies in distributed-memory environments. In this paper, we propose LMDBIO-DM, an enhanced version of LMDBIO-LMM that optimizes the I/O access of Caffe in distributed-memory environments. We present several sophisticated data I/O techniques that allow for significant improvement in such environments. Our experimental results show that LMDBIO-DM can improve the overall execution time of Caffe by more than 30-fold compared with LMDB and by 2-fold compared with LMDBIO-LMM.

关键词： Training Machine learning Optimization Parallel processing Scalability distributed databases

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Regional Congestion Control in Datacenter Networks

Regional Congestion Control in Datacenter Networks

引用

international conference on Parallel and distributed systems (ICPADS)

作者： Fan Yang Zhan Wang Xiaoli Liu Zheng Cao Guojun Yuan Xuejun An Universiry of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781538632086

The rapid deployment of cloud computing and online services poses great challenges for data center networks, and congestion control is one of the top concerns. Although numbers of proposals in different network layers have been put forward to alleviate the negative impact of congestion, the short-lived flows, which are latency-sensitive and constitute the majority of total traffic in data centers, still suffer severe performance degradation. Since the existing congestion control methods all rely on end hosts to perceive congestion and then adjust their network sending rate, the response time is relatively long when compared with the duration of short-lived flows, which increases latency significantly. In this paper, we propose RCC, a regional congestion control mechanism, which aims to respond to congestion more quickly and eliminate the mismatch mentioned above. Different from host-based mechanisms, RCC is implemented in the switch, which detects the congestion state and schedule the traffic around the congestion point locally, without sending feedback to the distal host. Evaluation has shown that, compared with the host-based mechanism, our method achieves better performance for short-lived flows and maintains stable buffer occupancy of the switch. In addition, mixed long- and short-lived flows which contend for the same bottleneck link can share the bandwidth more fairly.

关键词： conferences

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Data Collection with Privacy Preserving in Participatory Sensing

Data Collection with Privacy Preserving in Participatory Sen...

引用

international conference on Parallel and distributed systems (ICPADS)

作者： Qinghua Chen Shengbao Zheng Zhengqiu Weng College of Computer Science & Technology Zhejiang University of Technology Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Information Technology Wenzhou Vocational & Technical College

ISBN: (纸本)9781538632086

Participatory sensing has increasingly become a new paradigm of data collection from a wide physical area and a large population. One of the major challenges in participatory sensing is the privacy issue. Sensing data from smartphones may contain sensitive information such as user locations. Thus, it is of great importance to preserve privacy throughout the data collection process in participatory sensing. It is however very challenging because of the distributed nature of the network, many potential malicious attackers and the convergecast model of data collection. In this paper, we present a data collection approach which preserves user privacy in participatory sensing. In this approach, a smartphone node utilizes other smartphones as intermediate nodes to transfer its sensing data. In addition, asymmetric encryption is used to prevent malicious reverse tracking along the data forwarding route, hence anonymizing the originator of the data. We analyze the security of the approach and show that it achieves a high level of security. Extensive simulations demonstrate that the proposed approach has a low overhead.

关键词： Sensors Servers Public key Data collection Smart phones Data privacy Peer-to-peer computing

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Routing in IoT Network for Dynamic Service Discovery

Routing in IoT Network for Dynamic Service Discovery

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international conference on Parallel and distributed systems (ICPADS)

作者： Hessam Moeini I-Ling Yen Farokh Bastani Department of Computer Science University of Texas at Dallas

ISBN: (纸本)9781538632086

Many existing IoT systems are built with predefined tasks and system architecture. To make better use of the large number of “things” availability in the IoT infrastructure, we consider to dynamically discover and compose IoT capabilities in the IoT network for new or dynamically arising tasks. To achieve the goal, we develop a semantic-based routing protocol for IoT service discovery. In the routing protocol to reduce the space requirement for routing, each node maintains a routing table with a “telescopic view”, i.e., more precise information about the capabilities of immediate neighbors and only a summary of capabilities for a further away neighborhood. We define an ontology of capabilities and design a capability summarization algorithm based on the ontology to realize the telescopic view concept. We also design an ontology coding scheme to further reduce the memory requirement for routing while enabling capability summarization. Our solutions significantly reduce the routing table size and, hence, is suitable for IoT devices with limited memory. Also, experimental results show that our approach can yield significantly lower network traffic and latency for IoT capability lookup when compared with existing semantic based routing algorithms.

关键词： Routing Ontologies Peer-to-peer computing Semantics Routing protocols Encoding Cameras

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Enabling Enterprise Blockchain AppDev Teams

Enabling Enterprise Blockchain AppDev Teams

引用

international conference on Advanced computing and Communications (ADCOM)

作者： Prateek Reddy Yammanuru Ayush Jain Harihara Vinayakaram Wipro Technologies

ISBN: (纸本)9781538669129;9781538669112

Blockchain is the latest buzzword in the FinTech scene and all companies big and small are vying to launch blockchain enabled products. At the basic technology level Blockchain is a distributed technology application. The challenges of operating such an application are known [1]. But the techniques of developing distributed applications by large enterprise teams, in a typical SDLC lifecycle (Develop, Test, Deploy and Upgrade) is not well known. Without proper methodologies / Formal Tools as is the case with most blockchain systems, bugs slip in easily. Studies on failures point to developers missing low handing bugs as most of the errors are simulated with 3 nodes or less [2]. The developer ecosystem is fast changing with technologies like containers and the emerging Micro Services architectures and Cloud Native computing. The decisions on setup, build, CI/CD, Automated Testing are not taken at the beginning and as pointed out by [3] affect the entire project. The good news is that there are lot of tools available in the Open source domain that addresses the needs. The bad news is that picking the right combination to work in team sizes of 5 or more is not straight forward. This paper details our journey and lessons learnt on setting up Application Development Teams for Rapid Development in Blockchain using multiple blockchain tools like Ethereum and the HyperLedger Fabric. It details both our application architecture and the modifications needed to enable a Cloud Native architecture and the build/ deploy/ testing frameworks that we used.

关键词： Blockchain Tools Testing Computer architecture distributed ledger Fabrics Chaos

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Fast Parallel Recovery of Many Small In-Memory Objects

Fast Parallel Recovery of Many Small In-Memory Objects

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international conference on Parallel and distributed systems (ICPADS)

作者： Kevin Beineke Stefan Nothaas Michael Schottner Heinrich-Heine-Universität Dusseldorf Dusseldorf Germany Heinrich-Heine-Universitat Dusseldorf Dusseldorf Nordrhein-Westfalen DE

ISBN: (纸本)9781538632086

Social media networks as well as online graph analytics operate on large-scale graphs with millions of vertices, even billions in some cases. Low-latency access is essential, but caching suffers from the mostly irregular access patterns of the aforementioned application domains. Hence, distributed in-memory systems are proposed keeping all data always in memory. These in-memory systems are typically not optimized for the sheer amounts of small data objects, which demands new concepts regarding the local and global data management as well as for the fault-tolerance mechanisms to mask server failures and power outages. In this paper, we propose a novel backup distribution and parallel recovery approach aiming at fast recovery of servers storing hundreds of millions of small objects. All proposed concepts have been implemented within the open source distributed system DXRAM and have been evaluated in the Microsoft Azure cloud with up to 72 high performance virtual machines in two scale-sets. For evaluation, we used two benchmarks: the Yahoo! Cloud Serving Benchmark and a recovery benchmark. The experiments show that the proposed recovery strategy is able to recover servers with 500,000,000 small data objects in less than 2 seconds and, also, to efficiently mask server failures under heavy load. Furthermore, DXRAM outperforms the state-of-the-art system RAMCloud in additional recovery experiments with large objects (2.4× faster) and even more with small objects (> 9×).

关键词： Servers Random access memory distributed databases Peer-to-peer computing Fault tolerance Fault tolerant systems Power system reliability

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Online Auctions with Dynamic Costs for Ridesharing

Online Auctions with Dynamic Costs for Ridesharing

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international conference on Parallel and distributed systems (ICPADS)

作者： Chaoli Zhang Fan Wu Xiaofeng Gao Guihai Chen Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781538632086

Uber, Lyft and some other companies achieve great business success in the area of ridesharing. However, there are still many issues to be addressed, for instance, the lack of a well-designed rush hour (i.e., when supply is much less than demand) pricing strategy, the seat resources waste and so on. The online ridesharing model fits better to reality but brings significant design challenges. In this paper, we propose a complementary online auction design: No Preemption auction. The auction in ridesharing needs to deal with dynamic costs, which is different from most of the existing auctions and brings significant challenges. We design corresponding online auction mechanism for this model. Our mechanism is truthful with Risk Aversion Hypothesis, budget balanced (i.e., the payment is not less than the cost), computational efficient, individually rational, and make the best use of the seat resources. We give theoretical proof of the truthfulness, budget balance, computational efficiency and individual rationality. We also give a discussion about the general online ridesharing mechanism performance. Finally, we evaluate the performance of our mechanism based on real taxi-trace data in Shanghai. The results show that our mechanism can achieve rather good revenue and social welfare.

关键词： Automobiles Resource management Pricing Surges Companies Public transportation

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Extending Blockchain Functionality with Statechain

Extending Blockchain Functionality with Statechain

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international conference on Parallel and distributed systems (ICPADS)

作者： Xiaokang Chen Kunlong Zhang School of Computer Science and Technology Tianjin University Tianjin China

ISBN: (纸本)9781538632086

Blockchain has attracted great attention as the basis of cryptocurrencies such as Bitcoin, but its capabilities extend far beyond that. Blockchain has the potential to revolutionize applications because it provides a transparent, immutable, and append-only ledger that can be used to build new decentralized applications. However, it is hard to implement changes to Bitcoin without forking blockchain. As a result, Bitcoin has difficulty in adapting to new demands and extending new functionality. The traditional way to introduce new functionality is to modify Bitcoin's code base, but which will lead to some problems such as (a) the security of blockchains, (b) the allocation of currency, and (c) the waste of resources. We present a novel design, statechain, which uses Bitcoin blockchain to propagate application log. It can add new functionality without requiring blockchain fork changes from Bitcoin. Statechain enables application nodes to efficiently query the log as well as transfer log between blockchains. We detail how the application nodes achieve application-level consensus at each block. As far as we know, this technology has not been exploited. However, it has a great practical significance that enables the introduction of new functionality safer and makes the development of blockchain-based applications easier. We have used statechain to explore the field of transferring bitcoins and other cryptocurrencies directly between multiple blockchains.

关键词： Bitcoin Peer-to-peer computing Protocols Relays

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Maximizing the Profit of Cloud Broker with Priority Aware Pricing

Maximizing the Profit of Cloud Broker with Priority Aware Pr...

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international conference on Parallel and distributed systems (ICPADS)

作者： Xinhou Wang Song Wu Kezhi Wang Sheng Di Hai Jin Kun Yang Shumao Ou Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Northumbria University UK Argonne National Laboratory USA University of Essex UK Oxford Brookes University UK

ISBN: (纸本)9781538632086

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

关键词： Cloud computing Pricing Google Heuristic algorithms Monitoring Virtual machining

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