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Interests have been growing in integrating renewableenergy into data centers, which attracts many researchefforts in developing green-aware algorithms and systems. However, little attention was paid to the efficiency of each jouleconsumed by data center workloads. In fact, not all joulesare equal in the sense that the amount of work that can bedone by a joule can vary significantly in data centers...
While distributed graph processing engines have become popular for processing large graphs, these engines are typically configured with a static set of servers in the cluster. In other words, they lack the flexibility to scale-out or scale-in the number of servers, when requested to do so by the user. In this paper, we propose the first techniques to make distributed graph processing truly elastic...
The era of big data has led to the emergence of new real-time distributed stream processing engines like Apache Storm. We present Stela (STream processing ELAsticity), a stream processing system that supports scale-out and scale-in operations in an on-demand manner, i.e., when the user requests such a scaling operation. Stela meets two goals: 1) it optimizes post-scaling throughput, and 2) it minimizes...
Today, many organizations need to operate on datathat is distributed around the globe. This is inevitable due to thenature of data that is generated in different locations such as videofeeds from distributed cameras, log files from distributed servers, and many others. Although centralized cloud platforms havebeen widely used for data-intensive applications, such systemsare not suitable for processing...
With the rapid increase in uptake of cloud services, issues of data management are becoming increasingly prominent. There is a clear, outstanding need for the ability for specified policy to control and track data as it flows throughout cloud infrastructure, to ensure that those responsible for data are meeting their obligations. This paper introduces Information Flow Audit, an approach for tracking...
In the context of private datacenters that are operated by Web service providers such as Google, multiple applications using data parallel frameworks, such as MapReduce, coexist and share a limited supply of link bandwidth capacities. It has been shown that failures are the norm, rather than the exception, in datacenters, and will negatively affect the performance of data parallel applications as...
Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN,...
Modern science and engineering require increasingly sophisticated information technology (IT) for data analysis, simulation, and related tasks. Yet the small to medium laboratories (SMLs) in which the majority of research advances occur increasingly lack the human and financial capital needed to acquire and operate such IT. New methods are needed to provide all researchers with access to state-of-the-art...
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