Impact of Instance Seeking Strategies on Resource Allocation in Cloud Data Centers

With the prosperity of cloud computing, an increasing number of Small and Medium-sized Enterprises (SMEs) move their business to public clouds such as Amazon EC2. To help tenants deploy services in the cloud, researchers either conduct performance evaluations or design mechanisms and software on seeking virtual machines of better performance. However, few studies have investigated the impact of instance seeking strategies on resource allocation in clouds if every tenant starts to apply the same method to find the better-performing virtual machine. In this paper, we propose a cloud and a tenant model in order to simulate the process of tenants' seeking better-performing instances in the cloud. We discuss, implement and evaluate six cloud resource allocation strategies and five instance seeking strategies. We perform the evaluation via simulation based on real data traces. Our results show that instance seeking strategies can cause the exhaustion of better-performing instances and significant request growth in the cloud. Furthermore, we find that tenants could save time and budget through collaborative seeking strategies. Finally, we discuss the implications of our findings from perspectives of both tenants and providers

Techniques and tools for measuring energy efficiency of scientific software applications

Volume: 608The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important concern in scientific fields such as High Energy Physics (HEP). There has been a growing interest in utilizing alternate architectures, such as low power ARM processors, to replace traditional Intel x86 architectures. Nevertheless, even though such solutions have been successfully used in mobile applications with low I/O and memory demands, it is unclear if they are suitable and more energy-efficient in the scientific computing environment. Furthermore, there is a lack of tools and experience to derive and compare power consumption between the architectures for various workloads, and eventually to support software optimizations for energy efficiency. To that end, we have performed several physical and software-based measurements of workloads from HEP applications running on ARM and Intel architectures, and compare their power consumption and performance. We leverage several profiling tools (both in hardware and software) to extract different characteristics of the power use. We report the results of these measurements and the experience gained in developing a set of measurement techniques and profiling tools to accurately assess the power consumption for scientific workloads.Peer reviewe

Structured peer-to-peer networks:hierarchical architecture and performance evaluation

Abstract Peer-to-Peer (P2P) networking changes the way of people utilizing Internet, for example, sharing and consuming digital content, from the ground up. It continues to show its power and strength when it is combined with other emerging technologies, such as Web Services. This thesis contributes to the research and development of P2P networks from four aspects. Firstly, a P2P and Web Services converged multiple-tier system architecture is proposed. The architecture proposed enables providing Web Services in the context of heterogeneous access networks in an efficient way by utilizing P2P paradigm. A lightweight middleware architecture is introduced to fit the diversified mobile terminals. A theoretical analysis is given to provide a comparative study with the conventional centralized architecture. Secondly, a General Truncated Pyramid Peer-to-Peer (GTPP) architecture is presented to analyze the performance of hierarchical architecture compared with flat architecture. The motivation behind the GTPP architecture is to see whether an added tier can bring with it added value and functionality. A detailed mathematical analysis is provided which takes into consideration various performance metrics, including the lookup hopcount, lookup latency, maintenance traffic from a single peer point of view, and maintenance traffic from the whole system point of view. Furthermore, simulation results with respect to the lookup hopcount are also provided. Through mathematical analysis and simulation results, an optimal value regarding the number of tiers of the GTPP architecture is found, showing that 2~3 tiers are appropriate for most of situations. A specialized model is also proposed to improve the performance of hierarchical architecture. Thirdly, the performance evaluation of a communication-oriented Kademlia-based P2P system is provided in detail. NetHawk EAST-based simulation models and a prototype are both utilized to evaluate the performance. Simulation results from NetHawk EAST-based simulation models demonstrate the optimal design choices regarding the resource lookup parallelism degree and resource replication degree, and show the unnecessary existence of the messages used to detect the liveness of peers in a DHT overlay. Measurements from the prototype show the feasibility of mobile nodes acting as fully fledged overlay nodes from three different perspectives, namely CPU processing load, network traffic load, and battery consumption. The optimal size of packets which consumes battery in the most efficient way is also found through battery consumption measurements. Fourthly, the effects of different churn models on the performance of structured P2P networks are analyzed. Specifically, three typical churn models are analyzed to provide a comparative result. The simulation results show that the difference among the effects of different churn models on the performance of structured P2P networks is quantitative rather than qualitative. This provides some guidance for the selection of different churn models for the contemporary researchers

Effects of different churn models on the performance of structured peer-to-peer networks

Abstract—We present the effects of different churn models on the performance of structured peer-to-peer (P2P) networks in this paper. Specifically, Exponential distribution (ED), Pareto distribution (PD), and Weibull distribution (WD) are evaluated to provide a comparative analysis. Kademlia-based Peer-to-Peer Protocol (P2PP) is utilized as the underlying signaling protocol. Through simulations, we conclude that the simulated different churn models do not have a significant effect on the performance of the simulated structured P2P network. Quantitatively, ED and PD result in better performance compared to WD from the viewpoints of lookup success rate, mean network traffic load, and mean number of messages. Keywords- structured peer-to-peer network; churn model; exponential distribution; pareto distribution; weibull distribution; I

RAPL in Action : Experiences in Using RAPL for Power Measurements

To improve energy efficiency and comply with the power budgets, it is important to be able to measure the power consumption of cloud computing servers. Intel’s Running Average Power Limit (RAPL) interface is a powerful tool for this purpose. RAPL provides power limiting features and accurate energy readings for CPUs and DRAM, which are easily accessible through different interfaces on large distributed computing systems. Since its introduction, RAPL has been used extensively in power measurement and modeling. However, the advantages and disadvantages of RAPL have not beenwell investigated yet. To fill this gap,we conduct a series of experiments to disclose the underlying strengths and weaknesses of the RAPL interface by using both customized microbenchmarks and three well-known application level benchmarks: Stream, Stress-ng, and ParFullCMS. Moreover, to make the analysis as realistic as possible, we leverage two production-level power measurement datasets from the Taito, a supercomputing cluster of the Finnish Center of Scientific Computing and also replicate our experiments on Amazon EC2. Our results illustrate different aspects of RAPL and document the findings through comprehensive analysis. Our observations reveal that RAPL readings are highly correlated with plug power, promisingly accurate enough, and have negligible performance overhead. Experimental results suggest RAPL can be a very useful tool tomeasure and monitor the energy consumption of servers without deploying any complex power meters. We also show that there are still some open issues, such as driver support, non-atomicity of register updates, and unpredictable timings that might weaken the usability of RAPL in certain scenarios. For such scenarios, we pinpoint solutions and workarounds.Peer reviewe

Redundancy Removing Aggregation Network with Distance Calibration for Video Face Recognition

Attention-based techniques have been successfully used for rating image quality, and have been widely employed for set-based face recognition. Nevertheless, for video face recognition, where the base convolutional neural network (CNN) trained on large-scale data already provides discriminative features, fusing features with only predicted quality scores to generate representation are likely to cause duplicate sample dominant problem, and degrade performance correspondingly. To resolve the problem mentioned above, we propose a redundancy removing aggregation network (RRAN) for video face recognition. Compared with other quality-aware aggregation schemes, RRAN can take advantage of similarity information to tackle the noise introduced by redundant video frames. By leveraging metric learning, RRAN introduces a distance calibration scheme to align distance distributions of negative pairs of different video representations, which improves the accuracy under a uniform threshold. A series of experiments is conductedon multiple realistic data sets to evaluate the performance of RRAN, including YouTube Faces, IJB-A, and IJB-C. In comprehensive experiments, we demonstrate that our method can diminish the overall influence of poor quality components with large proportion in the video and further improve the overall recognition performance with individual difference. Specifically, RRAN achieves a 96.84% accuracy on YouTube Face, outperforming all existing aggregation schemes.Peer reviewe