Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.Peer Reviewe
While cluster computing frameworks are contin-uously evolving to provide real-time data analysis cap...
Sheer increase in volume of data over the last decade has triggered research in cluster computing fr...
The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications o...
Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present ...
The proliferation of big-data processing platforms has already led to radically different system des...
On multicore processors, co-executing applications compete for shared resources, such as cache capac...
Recent advances in cloud-based big data analysis offers a convenient mean for providing an elastic a...
Abstract—Recent advances in cloud-based big data analysis offers a convenient mean for providing an ...
The memory system has been evolving at a fast pace recently, driven by the emergence of large-scale ...
A runtime attack can be detected on a big data system while processes are executed on various comput...
A proliferation of frameworks have emerged to handle the challenges of making distributed computatio...
ABSTRACT In this paper we study the impact of sharing memory resources on five Google datacenter app...
The processing of data-intensive applications is a challenging and time-consuming task that often re...
In this work, we investigate techniques to improve the performance of big data analytics in virtuali...
The sheer increase in the volume of data over the last decade has triggered research in cluster comp...
While cluster computing frameworks are contin-uously evolving to provide real-time data analysis cap...
Sheer increase in volume of data over the last decade has triggered research in cluster computing fr...
The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications o...
Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present ...
The proliferation of big-data processing platforms has already led to radically different system des...
On multicore processors, co-executing applications compete for shared resources, such as cache capac...
Recent advances in cloud-based big data analysis offers a convenient mean for providing an elastic a...
Abstract—Recent advances in cloud-based big data analysis offers a convenient mean for providing an ...
The memory system has been evolving at a fast pace recently, driven by the emergence of large-scale ...
A runtime attack can be detected on a big data system while processes are executed on various comput...
A proliferation of frameworks have emerged to handle the challenges of making distributed computatio...
ABSTRACT In this paper we study the impact of sharing memory resources on five Google datacenter app...
The processing of data-intensive applications is a challenging and time-consuming task that often re...
In this work, we investigate techniques to improve the performance of big data analytics in virtuali...
The sheer increase in the volume of data over the last decade has triggered research in cluster comp...
While cluster computing frameworks are contin-uously evolving to provide real-time data analysis cap...
Sheer increase in volume of data over the last decade has triggered research in cluster computing fr...
The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications o...
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