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Adding a Live Migration Model Into SimGrid One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

Adding a Live Migration Model Into SimGrid One More Step Tow...

引用

5th IEEE International conference on Cloud computing Technology and Science (IEEE CloudCom)

作者： Hirofuchi, Takahiro Lebre, Adrien Pouilloux, Laurent INRIA LINA Mines Nantes ASCOLA Res Grp F-44026 Nantes France

ISBN: (纸本)9781479915484

Although virtual machine (VM) placement problem has been an active research area over the past decade, the research community is still looking for an open simulation framework that can simulate in an accurate as well as scalable manner VM operations including live migrations. Existing frameworks leverage a naive migration model that considers neither memory update operations nor resource sharing contention, resulting in an underestimate of both the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely used simulation toolkit, SimGrid, which allows users to simulate large-scale distributed systems by using user-friendly programming API. through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. through a first use case, we also confirmed that it is possible to conduct large-scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs).

关键词： virtual machine live migration simulation cloud computing

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Efficient prefetching technique for storage of heterogeneous small files in hadoop distributed file system federation 5

Efficient prefetching technique for storage of heterogeneous...

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5th International conference on Advanced computing, ICoAC 2013

作者： Aishwarya, K. Arvind Ram, A. Sreevatson, M.C. Babu, Chitra Prabavathy, B.

ISBN: (纸本)9781479934478

Hadoop distributed File System Federation [5] is used to store and manage large files. this has been used in a university scenario to store various categories of files such as PDFs, audio, video, presentation and image files. However, HDFS Federation suffers performance penalty while storing a large number of small files. Also, scaling the namenodes in HDFS Federation does not solve the small files problem [7] but only delays the metadata accumulation. One approach to handle this problem was implemented in BlueSky [1], one of the most revalent e-learning resources in China. However, this system does not handle files from heterogeneous users and the prefetching mechanism implemented in this system takes into account only the locality of reference and does not consider file access patterns. the objective of this paper is to address the above mentioned shortcomings by developing an efficient approach to handle files from heterogeneous users and to devise an efficient prefetching algorithm based on file access patterns. the file access patterns are stored and updated in a priority heap. Heterogeneous users can upload their files and complete transparency is maintained in grouping small files into a large file. this approach of merging several small files into a large file reduces the memory footprint in Federated HDFS. In addition to the existing features, this paper also provides options to modify and delete the files stored by users in Federated HDFS. Performance of original HDFS Federation and the proposed system are benchmarked with a set of 100,000 small files. the experimental results show that the memory usage was reduced by 36% from original HDFS Federation. File read time has been brought down by 94% (with prefetching based on files access patterns) compared to the proposed system without prefetching and 92% compared to prefetching based on the locality of reference. © 2013 IEEE.

关键词： Metadata

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Cost-aware Client-side File Caching for Data-intensive Applications

Cost-aware Client-side File Caching for Data-intensive Appli...

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5th IEEE International conference on Cloud computing Technology and Science (IEEE CloudCom)

作者： Huang, Yaning Jin, Hai Shi, Xuanhua Wu, Song Chen, Yong Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

Parallel and distributed file systems are widely used to provide high throughput in high-performance computing and Cloud computing systems. To increase the parallelism, I/O requests are partitioned into multiple sub-requests (or 'flows') and distributed across different data nodes. the performance of file systems is extremely poor if data nodes have highly unbalanced response time. Client-side caching offers a promising direction for addressing this issue. However, current work has primarily used client-side memory as a read cache and employed a write-through policy which requires synchronous update for every write and significantly under-utilizes the client-side cache when the applications are write-intensive. Realizing that the cost of an I/O request depends on the struggler sub-requests, we propose a cost-aware client-side file caching (CCFC) strategy, that is designed to cache the sub-requests with high I/O cost on the client end. this caching policy enables a new trade-off across write performance, consistency guarantee and cache size dimensions. Using benchmark workloads MADbench2, we evaluate our new cache policy alongside conventional write-through. We find that the proposed CCFC strategy can achieve up to 110% throughput improvement compared to the conventional write-through policies with the same cache size on an 85-node cluster.

关键词： Parallel file system client-side file caching data-intensive computing high-performance computing Cloud computing

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Indexed Bloom Filters for Web Caches Summaries

Indexed Bloom Filters for Web Caches Summaries

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5th International conference on Computational Collective Intelligence (ICCCI)

作者： Lefebvre, Sylvain LISITE ISEP F-75006 Paris France

ISBN: (纸本)9783642404955;9783642404948

the popularity of web applications has shown rapid growth on Web Services and Cloud computing. One of the major challenge in Web applications is that it needs to store lots of assets in cache memory to garantee responsive feeling for the user. User satisfaction is an important constraint imposing Quality of Service requirements. therefore the use of distributed caches becomes necessary for numerous applications, fostering the need of efficient load distribution strategy. However some services (for example, on demand document searching) also require computations in addition to caching. this article presents a new load balancing policy called Block Based Workload And Cache Aware load balancing (BB-WACA) policy aimed at reducing the information retrieval time of the processor using Bloom Filter Based cache summaries. the BB-WACA is a centralized load balancing algorithm which routes the incoming requests to the machine where the underlying data will most probably be in its cache without overloading any nodes in the cluster.

关键词： Cache memory

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Locally Exploitable Heterogeneous Multihop Communications Applied to Cooperative-robots

Locally Exploitable Heterogeneous Multihop Communications Ap...

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5th International conference on Ubiquitous and Future Networks (ICUFN)

作者： Tanaka, Akira Tokyo Natl Coll Technol Dept Comp Sci Tokyo Japan

ISBN: (纸本)9781467359900

We have developed locally exploitable multihop communication systems that consist of easily-obtainable devices and enable user-oriented communications. By narrowing their purposes, difficult challenges for multihops are resolved. Taking advantage of pervasiveness, we study multihops to be locally embedded in WANs, and newly develop heterogeneously multihop-linked-cooperative-robots by integrating our multihop technologies: (a) smartphone multimedia-chatting, (b) infrared-communications with error-correction, switching, multipath-prevention and route-diversity, (c) energy-conserving-routing with mobility-prediction and procedure-integration in UHF systems. Uni-/multi-/broadcast, barrier-free and language-independent interface, visible-light-communication and heterogeneity are incorporated. Consequently, the cooperative-robots hold promise as search-and-rescue robots in disaster sites such as earthquakes as well as in daily life.

关键词： mobile multihop communications ubiquitous computing routing multicast heterogeneous communications positioning visible light communications automated robot route diversity distributed shared memory

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LOG ANALYSIS IN CLOUD computing ENVIRONMENT WIth HADOOP AND SPARK

LOG ANALYSIS IN CLOUD COMPUTING ENVIRONMENT WITH HADOOP AND ...

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2013 5th IEEE International conference on Broadband Network & Multimedia Technology

作者： Xiuqin LIN Peng WANG Bin WU Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479900961

Log is the main source of the system operation status, user behavior analysis etc. Log analysis system needs not only the massive and stable data processing ability but also the adaptation to a variety of scenarios under the requirement of efficiency, which can’t be achieved from standalone analysis tools or even single cloud computing framework. We present a unified cloud platform for batch log data analysis with the combination of Hadoop and Spark. Hadoop provides a distributed file system and off-line batch computing framework, while the computing pattern in Spark is based on distributed memory. the joint of Hadoop, Spark and the data warehouse and analysis tools of Hive and Shark makes it possible to provide a unified cloud platform with batch analysis and in-memory computing capacity in order to process log in a high available, stable and efficient way.

关键词： Log analysis Hadoop Spark Hive Shark

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VOLUME: Enable Large-Scale In-memory Computation on Commodity Clusters

VOLUME: Enable Large-Scale In-Memory Computation on Commodit...

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IEEE International conference on Cloud computing Technology and Science (CloudCom)

作者： Zhiqiang Ma Ke Hong Lin Gu Hong Kong University of Science and Technology

Traditional cloud computing technologies, such as MapReduce, use file systems as the system-wide substrate for data storage and sharing. A distributed file system provides a global name space and stores data persistently, but it also introduces significant overhead. Several recent systems use DRAM to store data and tremendously improve the performance of cloud computing systems. However, both our own experience and related work indicate that a simple substitution of distributed DRAM for the file system does not provide a solid and viable foundation for data storage and processing in the data center environment, and the capacity of such systems is limited by the amount of physical memory in the cluster. To overcome the challenge, we construct VOLUME (Virtual On-Line Unified memory Environment), a distributed virtual memory to unify the physical memory and disk resources on many compute nodes, to form a system-wide data substrate. the new substrate provides a general memory based abstraction, takes advantage of DRAM in the system to accelerate computation, and, transparent to programmers, scales the system to handle large datasets by swapping data to disks and remote servers. the evaluation results show that VOLUME is much faster than Hadoop/HDFS, and delivers 6-11x speedups on the adjacency list workload. VOLUME is faster than both Hadoop/HDFS and Spark/RDD for in-memory sorting. For kmeans clustering, VOLUME scales linearly to 160 compute nodes on the th-1/GZ supercomputer.

关键词： Random access memory Semantics Sparks Substrates Cloud computing Servers Processor scheduling

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distributed Clique Percolation based community detection on social networks using MapReduce

Distributed Clique Percolation based community detection on ...

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conference on Information and Knowledge Technology

作者： Ali Varamesh Mohammad Kazem Akbari Mehdi Fereiduni Saeed Sharifian Alireza Bagheri Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran

In study of complex networks, valuable insights can be obtained by mining structural and functional sub-units of networks, usually called communities, modules, or clusters. One of the approaches to community detection is Clique Percolation Method (CPM) which is the most popular overlapping community detection method and in recent years has been used in analysis of different kinds of networks. However, application of CPM even on small size social networks is very challenging due to its extensive memory, processing, and IO requirements. Hence it is necessary to use distributed and parallel computing models to tackle CPM's computational challenges. In this paper a new distributed algorithm for computation of CPM will be introduced. the new algorithm is based on the MapReduce distributed computing model which extensively has been used to solve large scale data processing problems. Experimental results will be provided to show that the new MapReduce based algorithm for computation of CPM outperforms the best available algorithms with one order of magnitude when benchmarking them against real-world social network datasets.

关键词： Communities Social network services Computational modeling Benchmark testing Clustering algorithms distributed computing Complex networks

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Adding a Live Migration Model into SimGrid: One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

Adding a Live Migration Model into SimGrid: One More Step To...

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IEEE International conference on Cloud computing Technology and Science (CloudCom)

作者： Takahiro Hirofuchi Adrien Lèbre Laurent Pouilloux ASCOLA Research Group Mines de Nantes - INRIA - LINA Nantes France

Although virtual machine (VM) placement problem has been an active research area over the past decade, the research community is still looking for an open simulation framework that can simulate in an accurate as well as scalable manner VM operations including live migrations. Existing frameworks, however, leverage a naive migration model that considers neither memory update operations nor resource sharing contention, resulting in an underestimate of both the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely used simulation toolkit, SimGrid, which allows users to simulate large-scale distributed systems by using user-friendly programming API. through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. through a first use case, we also confirmed that it is possible to conduct large-scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs).

关键词： Computational modeling Databases Random access memory Benchmark testing Servers Bandwidth Data models

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Cost-Aware Client-Side File Caching for Data-Intensive Applications

Cost-Aware Client-Side File Caching for Data-Intensive Appli...

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IEEE International conference on Cloud computing Technology and Science (CloudCom)

作者： Yaning Huang Hai Jin Xuanhua Shi Song Wu Yong Chen Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China Department of Computer Science Texas Tech University Lubbock Texas USA

Parallel and distributed file systems are widely used to provide high throughput in high-performance computing and Cloud computing systems. To increase the parallelism, I/O requests are partitioned into multiple sub-requests (or `flows') and distributed across different data nodes. the performance of file systems is extremely poor if data nodes have highly unbalanced response time. Client-side caching offers a promising direction for addressing this issue. However, current work has primarily used client-side memory as a read cache and employed a write-through policy which requires synchronous update for every write and significantly under-utilizes the client-side cache when the applications are write-intensive. Realizing that the cost of an I/O request depends on the struggler sub-requests, we propose a cost-aware client-side file caching (CCFC) strategy, that is designed to cache the sub-requests with high I/O cost on the client end. this caching policy enables a new trade-off across write performance, consistency guarantee and cache size dimensions. Using benchmark workloads MADbench2, we evaluate our new cache policy alongside conventional write-through. We find that the proposed CCFC strategy can achieve up to 110% throughput improvement compared to the conventional write-through policies with the same cache size on an 85-node cluster.

关键词： Servers throughput distributed databases Benchmark testing Analytical models Cloud computing Time factors

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