The Address Resolution Protocol (ARP) is used by computers to map network addresses (IP) to physical addresses (MAC). The protocol has proved to work well under regular circumstances, but it was not designed to cope with malicious hosts. By performing ARP cache poisoning or ARP spoofing attacks, an intruder can impersonate another host (man-in-the-middle attack) and gain access to sensitive information. Several schemes to mitigate, detect and prevent these attacks have been proposed, but each has its limitations. In this paper we analyze each of these schemes, identify their strengths and weaknesses, and propose guidelines for the design of an alternative and (arguably) better solution to the problem of ARP cache poisoning.
Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.
As a key part of the serverless computing paradigm, Function-asa-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-ofthe-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.
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