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Data centers are experiencing an exponential increase in the amount of network traffic that they have to sustain due to cloud computing and several emerging web applications. To face this network load, large data centers are required with thousands of servers interconnected with high bandwidth switches. Current data center, based on general purpose processor, consume excessive power while their utilization...
Community detection approaches are useful tools for revealing the structure and properties of networks. There are many approaches for identifying communities, some require inaccessible information, others need initializing parameters to perform well. Many existing algorithms require centralized decision maker to reveal communities. This paper proposes a non-centralized parameter-free method that only...
Due to the growing presence of large-scale and streaming graphs such as social networks, graph sampling and clustering play an important role in many real-world applications. One key aspect of graph clustering is the evaluation of cluster quality. However, little attention has been paid to evaluation measures for clustering quality on samples of graphs. As first steps towards appropriate evaluation...
Cloud computing becomes more and more popular for its on-demand services and pay-as-you-go model. Elasticity is the key feature of cloud computing technology, which can reduce and add resources flexibly to meet customers' need. Considering the important of elasticity of cloud computing platform, the objective of this paper is to study the evaluation elasticity of cloud computing platform. In this...
Since there is no single best performing algorithm for all problems, an algorithm portfolio would leverage the strengths of complementary algorithms to achieve the best performance. In this paper, we present and evaluate a new technique for designing algorithm portfolios for continuous black-box optimization problems, based on social choice and voting theory concepts. Our technique, which we call...
This paper proposes a method for tuning compilations to improve the size, execution time and reliability of the final application altogether. Our approach implements a genetic strategy with a multi-objective evolution that takes advantage of the NSGA-II algorithm for selecting the best compilations. Experiments show that reliability can be improved by efficiently exploring the compiler optimization...
With the increasing amount of available data, distributed data processing systems like Apache Flink, Apache Spark have emerged that allow to analyze large-scale datasets. However, such engines introduce significant computational overhead compared to non-distributed implementations. Therefore, the question arises when using a distributed processing approach is actually beneficial. This paper helps...
Fast and efficient design space exploration is a critical requirement for designing computer systems, however, the growing complexity of hardware/software systems and significantly long run-times of detailed simulators often makes it challenging. Machine learning (ML) models have been proposed as popular alternatives that enable fast exploratory studies. The accuracy of any ML model depends heavily...
Due to the increasing proliferation of computing systems in diverse application domains, the need for application-specific design of multicore/manycore processing platforms is paramount. In order to tailor processors for application-specific requirements, a multitude of processor design parameters need to be tuned accordingly. Tuning of processor design parameters involves rigorous and extensive design...
We study the joint power allocation (PA) and relay selection (RS) for a two-way relay (TWR) network supporting asymmetric traffic rates through decode-and-forward (DF) protocol. We aim at minimizing the outage probability of the system under the total power constraint. We first recast our considered problem into a convex optimization form from which the closed-form solution is obtained. Then, the...
Superpixel segmentation becomes more and more popular in the fields of computer vision and image processing. The simple linear iterative clustering (SLIC) is widely used due to its high segmentation accuracy and low computational complexity. In this paper, we propose a variance adaptive SLIC (VASLIC) algorithm. The compactness factor of the proposed algorithm is determined according to the image neighbourhood...
While results of subjective quality assessment are represented by mean opinion scores and corresponding confidence intervals, the output of an objective quality metric for a given stimulus is only a single estimated quality level. Accordingly, the performance of a metric is evaluated by measuring the accuracy of its outputs with respect to the corresponding subjective scores. However, the concept...
We present a comprehensive simulation environment for design space exploration in Asymmetric 3D-Networks-on-chip (A-3D-NoCs) covering the heterogeneity in 3D-System-on-chips (3D-SoCs). A challenging aspect of A-3D-NoC design is the consideration of interwoven parameters of the communication infrastructure and characteristics of the manufacturing technologies. Thus, simultaneous evaluation of multiple...
Software-defined networking (SDN) introduces a split between the forwarding plane and the control plane of modern network equipment. Furthermore, OpenFlow, as an open interface for SDN, fosters the development of logically centralized network operation systems (NOS). To allow multiple network operation systems accessing the same network, there is the concept of SDN hypervisors. Little is known about...
About ten years ago, we presented the results of an effort to identify the "right metric" for efficient supercomputing at this workshop, The Workshop on High-Performance, Power-Aware Computing. In this paper, we review the advances that the community has made in this area of research. The intention of this ten-year retrospective is two-fold: (1) to acknowledge the past work through a historical...
This work introduces a procedure which takes as input a global routing solution that is already improved for routability based on the traditional total overflow (TOF) metric, and then improves the distribution of congestion without increasing the TOF. Our router is able to significantly decrease the number of edges in undesirable ranges of congestion by optimizing a convex piece-wise linear penalty...
Modern multicore architectures require runtime optimization techniques to address the problem of mismatches between the dynamic resource requirements of different processes and the runtime allocation. Choosing between multiple optimizations at runtime is complex due to the non-additive effects, making the adaptiveness of the machine learning techniques useful. We present a novel method, Machine Learned...
Graphics processing units (GPUs) are becoming increasingly common in today's computing systems due to their superior performance and energy efficiency relative to their cost. To further improve these desired characteristics, researchers have proposed several software and hardware techniques. Evaluation of these proposed techniques could be tricky due to the ad-hoc nature in which applications are...
Effective use of the memory hierarchy is crucial to cloud computing. Platform memory subsystems must be carefully provisioned and configured to minimize overall cost and energy for cloud providers. For cloud subscribers, the diversity of available platforms complicates comparisons and the optimization of performance. To address these needs, we present X-Mem, a new open-source software tool that characterizes...
Besides being an in-memory-oriented computing framework, Spark runs on top of Java Virtual Machines (JVMs), so JVM parameters must be tuned to improve Spark application performance. Misconfigured parameters and settings degrade performance. For example, using Java heaps that are too large often causes a long garbage collection pause time, which accounts for over 10–20% of application execution time...
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