Managing Large Enclaves in a Data Center

Live migration of an application or VM is a well-known technique for load balancing, performance optimization, and resource management. To minimize the total downtime during migration, two popular methods -- pre-copy or post-copy -- are used in practice. These methods scale to large VMs and applications since the downtime is independent of the memory footprint of an application. However, in a secure, trusted execution environment (TEE) like Intel's scalable SGX, the state-of-the-art still uses the decade-old stop-and-copy method, where the total downtime is proportional to the application's memory footprint. This is primarily due to the fact that TEEs like Intel SGX do not expose memory and page table accesses to the OS, quite unlike unsecure applications. However, with modern TEE solutions that efficiently support large applications, such as Intel's Scalable SGX and AMD's Epyc, it is high time that TEE migration methods also evolve to enable live migration of large TEE applications with minimal downtime (stop-and-copy cannot be used any more). We present OptMig, an end-to-end solution for live migrating large memory footprints in TEE-enabled applications. Our approach does not require a developer to modify the application; however, we need a short, separate compilation pass and specialized software library support. Our optimizations reduce the total downtime by 98% for a representative microbenchmark that uses 20GB of secure memory and by 90 -- 96% for a suite of Intel SGX applications that have multi-GB memory footprints

Rapid Tunneling and Percolation in the Landscape

Motivated by the possibility of a string landscape, we reexamine tunneling of a scalar field across single/multiple barriers. Recent investigations have suggested modifications to the usual picture of false vacuum decay that lead to efficient and rapid tunneling in the landscape when certain conditions are met. This can be due to stringy effects (e.g. tunneling via the DBI action), or by effects arising due to the presence of multiple vacua (e.g. resonance tunneling). In this paper we discuss both DBI tunneling and resonance tunneling. We provide a QFT treatment of resonance tunneling using the Schr\"odinger functional approach. We also show how DBI tunneling for supercritical barriers can naturally lead to conditions suitable for resonance tunneling. We argue using basic ideas from percolation theory that tunneling can be rapid in a landscape where a typical vacuum has multiple decay channels and discuss various cosmological implications. This rapidity vacuum decay can happen even if there are no resonance/DBI tunneling enhancements, solely due to the presence of a large number of decay channels. Finally, we consider various ways of circumventing a recent no-go theorem for resonance tunneling in quantum field theory.Comment: 47 pages, 16 figures. Acknowledgements adde

VarSim: A Fast Process Variation-aware Thermal Modeling Methodology Using Green's Functions

Despite temperature rise being a first-order design constraint, traditional thermal estimation techniques have severe limitations in modeling critical aspects affecting the temperature in modern-day chips. Existing thermal modeling techniques often ignore the effects of parameter variation, which can lead to significant errors. Such methods also ignore the dependence of conductivity on temperature and its variation. Leakage power is also incorporated inadequately by state-of-the-art techniques. Thermal modeling is a process that has to be repeated at least thousands of times in the design cycle, and hence speed is of utmost importance. To overcome these limitations, we propose VarSim, an ultrafast thermal simulator based on Green's functions. Green's functions have been shown to be faster than the traditional finite difference and finite element-based approaches but have rarely been employed in thermal modeling. Hence we propose a new Green's function-based method to capture the effects of leakage power as well as process variation analytically. We provide a closed-form solution for the Green's function considering the effects of variation on the process, temperature, and thermal conductivity. In addition, we propose a novel way of dealing with the anisotropicity introduced by process variation by splitting the Green's functions into shift-variant and shift-invariant components. Since our solutions are analytical expressions, we were able to obtain speedups that were several orders of magnitude over and above state-of-the-art proposals with a mean absolute error limited to 4% for a wide range of test cases. Furthermore, our method accurately captures the steady-state as well as the transient variation in temperature.Comment: 15 page