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A Virtual Middleboxes Network Placement Algorithm in Multi-tenant Datacenter Networks 23

A Virtual Middleboxes Network Placement Algorithm in Multi-t...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Zhang, Xuewei Wang, Xaoliang Cam-Tu Nguyen Wang, Jian Qian, Zhuzhong Lu, Sanglu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538621295

Hardware middleboxes are widely used in current cloud datacenter to provide network functions such as firewalls, intrusion detection system, load balancers, etc. Unfortunately, they are expensive and unable to offer customized functions for individual tenant. To overcome this issue, there is an increasing interest in deploying software middleboxes to enable flexible security, network access functionality. This paper addresses the software middleboxes placement problem with minimum bandwidth guarantee. We first specify the model of tenants' requirement that specifies the need for virtual machines of application and middleboxes, as well as communication traffic. A virtual middlebox placement algorithm called MISSILE is then proposed to offer predictable network performance for each accepted tenant, and minimize datacenter bandwidth utilization. Extensive simulation results based on current large-scale datacenter networks verify that MISSILE is effective and provides network performance guarantee for tenants.

关键词： Middleboxes Bandwidth Cloud computing Hardware Software algorithms Network function virtualization

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GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine 23

GraphMP: An Efficient Semi-External-Memory Big Graph Process...

引用

23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Sun, Peng Wen, Yonggang Ta Nguyen Binh Duong Xiao, Xiaokui Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781538621295

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform state-of-the-art systems such as GraphChi, X-Stream and GridGraph by 31.6x, 54.5x and 23.1x respectively, when running popular graph applications on a billion-vertex graph.

关键词： Graph Processing Big Data Parallel computing

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Hierarchical Resource Distribution Network Based on Mobile Edge computing 23

Hierarchical Resource Distribution Network Based on Mobile E...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Ren, Dewang Gui, Xiaolin Dai, Huijun An, Jian Liang, Xin Lu, Wei Chen, Meihong Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Shaanxi Prov Key Lab Comp Network Xian 710049 Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 200135 Peoples R China

ISBN: (纸本)9781538621295

Mobile edge computing (MEC) is an emerging paradigm to support the proliferation of smart phones and recent outstanding mobile data traffic growth. The network edge devices with computation, communication and storage capabilities can be applied to cache popular resource in close proximity to end uses and directly distribute resource to them, which is a promising solution to provide enhanced service. Firstly, in order to respond to the users' access requests at the edge of network, the distributed resource distribution network (DrdN) is constructed to realize the quick query, positioning and distribution of resource. It mainly consists of network elements: NEF (Network Exposure Function) node for global management and MEC nodes for distribution service. Secondly, on the basis of DrdN, two kinds of MEC's service models of distributing resource are defined and classified, the total energy consumption and average service delay for distributing resource to end users are analyzed. Finally, for the heavy access load and high latency of root caching MEC node, a hierarchical caching scheme (HCS) is designed and a caching node selection algorithm based on fuzzy C-means clustering is proposed according to the location characteristics of end users. The simulation results show that the HCS can save a lot of energy and reduce the average service delay. Additionally, for the different total arrival rate of users' access requests, the change of average service delay is stable with good adaptability in the HCS.

关键词： mobile edge computing caching resource fuzzy C-means clustering service delay distribution energy consumption

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HiRy: An Advanced Theory on Design of Deadlock-free Adaptive Routing for Arbitrary Topologies 23

HiRy: An Advanced Theory on Design of Deadlock-free Adaptive...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Kawano, Ryuta Yasudo, Ryota Matsutani, Hiroki Koibuchi, Michihiro Amano, Hideharu Keio Univ Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa Japan Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo Japan

ISBN: (纸本)9781538621295

Recently proposed irregular networks can reduce the latency for both on-chip and off-chip systems with a large number of computing nodes and thus can improve the performance of parallel application. However, these networks usually suffer from deadlocks in routing packets when using a naive minimal path routing algorithm. To solve this problem, we focus attention on a lately proposed theory that generalizes the turn model to maintain the network performance with deadlock-freedom. The theorems remain a challenge of applying themselves to arbitrary topologies including fully irregular networks. In this paper, we advance the theorems to completely general ones. To apply the idea of the turn model to arbitrary topologies, we introduce a concept of regions that define continuous directions of channels on an n-dimensional space. Moreover, we provide a feasible implementation of a deadlock-free routing method based on our advanced theorem. To reduce the latency and the number of required Virtual Channels (VCs) with this method, a heuristic approach is introduced to reduce the number of prohibited turns between channels. Experimental results show that the routing method based on our proposed theorem can improve the network throughput by up to 138 % compared to a conventional deterministic minimal routing method. Moreover, it can reduce the latency by up to 2.9 % compared to another fully adaptive routing method.

关键词： Interconnection Networks Deadlock-free Routing Algorithm High Performance computing Irregular Networks Virtual Channels

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Fast Parallel Recovery of Many Small In-memory Objects 23

Fast Parallel Recovery of Many Small In-memory Objects

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Beineke, Kevin Nothaas, Stefan Schoettner, Michael Heinrich Heine Univ Dusseldorf Inst Informat Univ Str 1 D-40225 Dusseldorf Germany

ISBN: (纸本)9781538621295

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 (2.4x faster) and even more with small objects (> 9x).

关键词： Cloud computing Data centers Reliability Storage recovery strategies Remote replication Main memory Flash memory B-trees

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Ada-copy: An Adaptive Memory Copy Strategy for Virtual Machine Live Migration 23

Ada-copy: An Adaptive Memory Copy Strategy for Virtual Machi...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Wang, Zhong Xue, Guangtao Qian, Shiyou Liu, Gongshen Li, Minglu Cao, Jian Yu, Jiadi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China

ISBN: (纸本)9781538621295

In the cloud computing architecture, virtual machine (VM) live migration is a fundamental research topic which has drawn extensive attention from communities of industry and academy. It is critical to transfer the VM memory pages that contain essential state information to resume the VM on another host during virtual machine (VM) live migration. There are many memory copy methods, such as pre-copy and postcopy. However, these methods have two limitations: application generality and performance imbalance. In this paper, we propose Ada-copy (adaptive copy), an adaptive memory copy strategy for VM live migration. The basic idea of Ada-copy is that the memory copy method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current dirty page rate of memory, Ada-copy can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations of existing memory copy methods. To evaluate the effectiveness of our proposed strategy, we experiment with the Ada-copy on a variety of migration tasks with different dirty page rate and diverse memory usage workloads. Evaluation results show, compared with traditional methods, Ada-copy can significantly reduce the total migration time by 26%, the VM downtime by 42% and the amount of pages transferred by 35% in average.

关键词： cloud computing live migration dirty page rate virtual machine memory copy strategy

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High Resource Utilization Auto-Scaling Algorithms for Heterogeneous Container Configurations 23

High Resource Utilization Auto-Scaling Algorithms for Hetero...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Cheng, Yi-Lin Lin, Ching-Chi Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781538621295

Auto-scaling is a technique that allocates resources according to dynamic workload. This paper focuses on autoscaling with heterogeneous container configurations. The goal is to minimize the cost of container adjustments, and to reduce the resource insufficiency penalty, while maintaining high resource utilization. It is extremely difficult to achieve the minimal cost without knowing the future workloads in advance. Thus, we first propose an optimal dynamic programming algorithm that can scale optimally when given the future workload. This optimal solution is used as the baseline to evaluate other algorithms that do not have the future workload information. Then, we propose two greedy algorithms that do not need workload information in advance, and a heuristic algorithm that first predicts the workload of the next time step using Gradient Boosting Regression, then makes scaling decisions using the optimal dynamic programming algorithm. We evaluate these four algorithms with two realistic workload traces. The experiments show that when the cost to start new servers is much higher than resource insufficiency penalty, our short-term prediction approach will only increase the total cost by only 9.6%, and decrease the utilization by only 10%, when compared with the optimal dynamic programming that knows the future workload.

关键词： Auto-scaling Cloud computing Container Resource utilization

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Rethinking Multicore Application Scalability on Big Virtual Machines 23

Rethinking Multicore Application Scalability on Big Virtual ...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Shan, Jianchen Jia, Weiwei Ding, Xiaoning New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781538621295

Virtual machine (VM) sizes keep increasing in the cloud. However, little attention has been paid to analyze and understand the scalability of multicore applications on big VMs with multiple virtual CPUs (VCPUs), assuming that application scalability on VMs can be analyzed in the same ways as that on physical machines (PMs). The paper demonstrates that, since hardware CPU resource is dynamically allocated to VCPUs, the executions of multicore applications on VMs show different scalability from those on PMs. The paper systematically studies how the virtualization of CPU resource changes execution scalability, identifies key application features and system factors that affect execution scalability on VMs, and investigates possible directions to improve scalability. The paper presents a few important findings. First, the execution scalability of applications on VMs is determined by different factors than those on PMs. Second, virtualization and resource sharing can improve scalability by nature. Thus, applications may show better scalability on VMs than on PMs. Linear scalability can be achieved even when there is substantial sequential computation. Third, there is still much space to further improve execution scalability by enhancing system designs. Better scalability can be achieved by increasing allocation period length and/or matching resource allocation and workload distribution.

关键词： scalability multicore virtual machine cloud computing resource sharing

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Real-Time Dense Wired Sensor Network Based on Traffic Shaping 23

Real-Time Dense Wired Sensor Network Based on Traffic Shapin...

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23rd IEEE international conference on Embedded and Real-Time computing systems and Applications (RTCSA)

作者： Loureiro, Joao Rangarajan, Raghuraman Nikolic, Borislav Indrusiak, Leandro Tovar, Eduardo Polytech Inst Porto ISEP CISTER INESC TEC Oporto Portugal Tech Univ Carolo Wilhelmina Braunschweig Inst Comp & Network Engn Braunschweig Germany Univ York York N Yorkshire England

ISBN: (纸本)9781538618981

XDense is a novel wired 2D-mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g. thousands of sensors per square meter). It was conceived for closed-loop cyber-physical systems (CPS) that require real-time actuation, like active flow control (AFC) on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed such that they enable to extract complex features within bounded time and in a responsive manner. In this paper we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and demonstrate the effectiveness of running real-time applications supported on XDense.

关键词： Delays Ports (Computers) Real-time systems Sensors Aircraft Feature extraction Mathematical model

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vMCA: Memory Capacity Aggregation and Management in Cloud Environments 23

vMCA: Memory Capacity Aggregation and Management in Cloud En...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Garrido, Luis A. Carpenter, Paul Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781538621295

In cloud environments, the VMs within the computing nodes generate varying memory demand profiles. When memory utilization reaches its limits due to this, costly (virtual) disk accesses and/or VM migrations can occur. Since some nodes might have idle memory, some costly operations could be avoided by making the idle memory available to the nodes that need it. In view of this, new architectures have been introduced that provide hardware support for a shared global address space that, together with fast interconnects, can share resources across nodes. Thus, memory becomes a global resource. This paper presents a memory capacity aggregation mechanism for cloud environments called vMCA (Virtualized Memory Capacity Aggregation) based on Xen's Transcendent Memory (Tmem). vMCA distributes the system's total memory within a single node and globally across multiple nodes using a user-space process with high-level memory management policies. We evaluate vMCA using CloudSuite 3.0 on Linux and Xen. Our results demonstrate a peak running time improvement of 76.8% when aggregating memory, and of 37.5% when aggregating memory and implementing our policies.

关键词： Virtualization Memory Aggregation Tmem vMCA

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