1,913 research outputs found
Energy Saving Techniques for Phase Change Memory (PCM)
In recent years, the energy consumption of computing systems has increased
and a large fraction of this energy is consumed in main memory. Towards this,
researchers have proposed use of non-volatile memory, such as phase change
memory (PCM), which has low read latency and power; and nearly zero leakage
power. However, the write latency and power of PCM are very high and this,
along with limited write endurance of PCM present significant challenges in
enabling wide-spread adoption of PCM. To address this, several
architecture-level techniques have been proposed. In this report, we review
several techniques to manage power consumption of PCM. We also classify these
techniques based on their characteristics to provide insights into them. The
aim of this work is encourage researchers to propose even better techniques for
improving energy efficiency of PCM based main memory.Comment: Survey, phase change RAM (PCRAM
A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems
Recent technological advances have greatly improved the performance and
features of embedded systems. With the number of just mobile devices now
reaching nearly equal to the population of earth, embedded systems have truly
become ubiquitous. These trends, however, have also made the task of managing
their power consumption extremely challenging. In recent years, several
techniques have been proposed to address this issue. In this paper, we survey
the techniques for managing power consumption of embedded systems. We discuss
the need of power management and provide a classification of the techniques on
several important parameters to highlight their similarities and differences.
This paper is intended to help the researchers and application-developers in
gaining insights into the working of power management techniques and designing
even more efficient high-performance embedded systems of tomorrow
ISP-friendly Peer-assisted On-demand Streaming of Long Duration Content in BBC iPlayer
In search of scalable solutions, CDNs are exploring P2P support. However, the
benefits of peer assistance can be limited by various obstacle factors such as
ISP friendliness - requiring peers to be within the same ISP, bitrate
stratification - the need to match peers with others needing similar bitrate,
and partial participation - some peers choosing not to redistribute content.
This work relates potential gains from peer assistance to the average number
of users in a swarm, its capacity, and empirically studies the effects of these
obstacle factors at scale, using a month-long trace of over 2 million users in
London accessing BBC shows online. Results indicate that even when P2P swarms
are localised within ISPs, up to 88% of traffic can be saved. Surprisingly,
bitrate stratification results in 2 large sub-swarms and does not significantly
affect savings. However, partial participation, and the need for a minimum
swarm size do affect gains. We investigate improvements to gain from increasing
content availability through two well-studied techniques: content bundling -
combining multiple items to increase availability, and historical caching of
previously watched items. Bundling proves ineffective as increased server
traffic from larger bundles outweighs benefits of availability, but simple
caching can considerably boost traffic gains from peer assistance.Comment: In Proceedings of IEEE INFOCOM 201
Load Value Approximation: Approaching the Ideal Memory Access Latency
Approximate computing recognizes that many applications can tolerate inexactness. These applications, which range from multimedia processing to machine learning, operate on inherently noisy and imprecise data. As a result, we can tradeoff some loss in output value integrity for improved processor performance and energy-efficiency. In this paper, we introduce load value approximation. In modern processors, upon a load miss in the private cache, the data must be retrieved from main memory or from the higher-level caches. These data accesses are costly both in terms of latency and energy. We implement load value approximators, which are hardware structures that learn value patterns and generate approximations of the data. The processor can then use these approximate data values to continue executing without incurring the high cost of accessing memory. We show that load value approximators can achieve high coverage while maintaining very low error in the application’s output. By exploiting the approximate nature of applications, we can draw closer to the ideal memory access latency. 1
Multimedia Correlation Analysis in Unstructured Peer-to-Peer Network
Recent years saw the rapid development of peer-topeer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature
Blazes: Coordination Analysis for Distributed Programs
Distributed consistency is perhaps the most discussed topic in distributed
systems today. Coordination protocols can ensure consistency, but in practice
they cause undesirable performance unless used judiciously. Scalable
distributed architectures avoid coordination whenever possible, but
under-coordinated systems can exhibit behavioral anomalies under fault, which
are often extremely difficult to debug. This raises significant challenges for
distributed system architects and developers. In this paper we present Blazes,
a cross-platform program analysis framework that (a) identifies program
locations that require coordination to ensure consistent executions, and (b)
automatically synthesizes application-specific coordination code that can
significantly outperform general-purpose techniques. We present two case
studies, one using annotated programs in the Twitter Storm system, and another
using the Bloom declarative language.Comment: Updated to include additional materials from the original technical
report: derivation rules, output stream label
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