P. G. Harrison, Imperial College London; N. M. Patel, NetApp Inc; J. F. Pérez, Universidad del Rosario; Z. Qiu, Imperial College London
Matrix analytic methods are developed to compute the probability distribution of response times (i.e., data access times) in distributed storage systems protected by erasure coding, which is implemented by sharding a data object into N fragments, only K<; N of which are required to reconstruct the object. This leads to a partial-fork-join model with a choice of canceling policies for the redundant N−K tasks. The accuracy of the analytical model is supported by tests against simulation in a broad range of setups. At increasing workload intensities, numerical results show the extent to which increasing the redundancy level reduces the mean response time of storage reads and significantly flattens the tail of their distribution; this is demonstrated at medium-high quantiles, up to the 99th. The quantitative reduction in response time achieved by two policies for canceling redundant tasks is also shown: for cancel-at-finish and cancel-at-start, which limits the additional load introduced whilst losing the benefit of selectivity amongst fragment service times.
Hardware-Assisted Secure Flash-Based Storage
Modern storage systems have been developed for decades with the security-critical foundation provided by operating system (OS). However, they are still vulnerable to malware attacks and software defects. Adversaries can obtain the OS kernel privilege or leverage software vulnerabilities to bypass, terminate or destroy current malware detection and defense systems. For instance, encryption ransomware accounts for more than half of all malware attacks today, but current software-based defense systems often fail to enable the victims to say no to ransom collectors. Therefore, it is natural to utilize hardware techniques which have been proven effective in defending against malware attacks.
Time Series Snippets: A New Analytics Primitive with applications to IoT Edge Computing
While most of today’s always-connected tech devices take advantage of cloud computing, many Internet of Things (IoT) developers increasingly understand the benefits of doing more analytics on the devices themselves, a philosophy known as edge computing. By performing analytic tasks directly on the sensor, edge computing can drastically reduce the bandwidth, cloud processing, and cloud storage needed.