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Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloudcomputing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become...
The underlying infrastructure of cloud computing relies on data centers monitored and maintained by the cloud service providers. Data centers usually incur enormous power consumption and are expected to have a significant impact on the local power grid due to dramatically increasing power consumption and fluctuation. In order to mitigate such fluctuation and balance the power demand and supply in...
Smart mobile devices have become ubiquitous, and people prefer to choose smartphone in daily life rather than use traditional personal computer. However, due to the hardware capability, performance of smartphones varies greatly and sometimes it cannot meet the demand of users. Furthermore, if smartphone is attacked by malicious application, the local private sensitive information will be leaked which...
Data driven programming models like MapReduce have gained the popularity in large-scale data processing. Although great efforts through the Hadoop implementation and framework decoupling (e.g. YARN, Mesos) have allowed Hadoop to scale to tens of thousands of commodity cluster processors, the centralized designs of the resource manager, task scheduler and metadata management of HDFS file system adversely...
The performance gap between processor and storage device has continuously increased during the past few decades. The gap is further exacerbated recently because applications are becoming more data-intensive in both industry and academia. Traditional storage devices, such as hard disk drives (HDD), fail to keep up with the paces of this growth. A known solution is to use solid state drives (SSD) as...
In order to cost-effectively serve a large number of users, video-on-demand (VoD) content providers often place distributed servers close to user pools. These servers have heterogeneous streaming and storage capacities, and collaboratively share contents with each other. A critical challenge is how to optimize movie storage and retrieval so as to minimize system deployment cost due to server streaming,...
Energy consumption of cloud data centers accounts for a major operational cost. This paper presents an optimization model for task scheduling to minimize task processing time and energy consumption in data centers for cloud computing. We formulate an integer programming optimization problem to minimize the expected energy consumption of homogenous tasks in a data center with a large number of servers...
In this paper, we propose a Network Virtual Platform (NetVP) to develop and verify network accelerator like an IPsec processor. The NetVP provides on-line verification mechanism and is suitable for ESL top-down design flow, supporting developments of un-timed as well as timed models. System development using this NetVP is efficient and flexible since it allows the designer to explore design spaces...
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