Tag Archives: hotcloud

A Secure Cloud with Minimal Provider Trust

Amin Mosayyebzadeh and Gerardo Ravago, Boston University; Apoorve Mohan, Northeastern University; Ali Raza and Sahil Tikale, Boston University; Nabil Schear, MIT Lincoln Laboratory; Trammell Hudson, Two Sigma; Jason Hennessey, Boston University and NetApp; Naved Ansari, Boston University; Kyle Hogan, MIT; Charles Munson, MIT Lincoln Laboratory; Larry Rudolph, Two Sigma; Gene Cooperman and Peter Desnoyers, Northeastern University; Orran Krieger, Boston University

10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud ’18)
JULY 9, 2018
BOSTON, MA, USA

Bolted is a new architecture for a bare metal cloud with the goal of providing security-sensitive customers of a cloud the same level of security and control that they can obtain in their own private data centers. It allows tenants to elastically allocate secure resources within a cloud while being protected from other previous, current, and future tenants of the cloud. The provisioning of a new server to a tenant isolates a bare metal server, only allowing it to communicate with other tenant’s servers once its critical firmware and software have been attested to the tenant. Tenants, rather than the provider, control the tradeoffs between security, price, and performance. A prototype demonstrates scalable end-to-end security with small overhead compared to a less secure alternative.

Resources

Empirical Evaluation and Enhancement of Enterprise Storage System Request Scheduling

Deng Zhou, San Diego State University; Vania Fang, NetApp; Tao Xie, Wen Pan, San Diego State University; Ram Kesavan, Tony Lin, and Naresh Patel, NetApp

ACM Transactions on Storage (TOS) Volume 14 Issue 2, May 201 Article No. 14

Since little has been reported in the literature concerning enterprise storage system file-level request scheduling, we do not have enough knowledge about how various scheduling factors affect performance. Moreover, we are in lack of a good understanding on how to enhance request scheduling to adapt to the changing characteristics of workloads and hardware resources. To answer these questions, we first build a request scheduler prototype based on WAFL®, a mainstream file system running on numerous enterprise storage systems worldwide. Next, we use the prototype to quantitatively measure the impact of various scheduling configurations on performance on a NetApp®’s enterprise-class storage system. Several observations have been made. For example, we discover that in order to improve performance, the priority of write requests and non-preempted restarted requests should be boosted in some workloads. Inspired by these observations, we further propose two scheduling enhancement heuristics called SORD (size-oriented request dispatching) and QATS (queue-depth aware time slicing). Finally, we evaluate them by conducting a wide range of experiments using workloads generated by SPC-1 and SFS2014 on both HDD-based and all-flash platforms. Experimental results show that the combination of the two can noticeably reduce average request latency under some workloads.

Resources

Maximizing Efficiency By Trading Storage for Computation

usenix09_button.jpgIan F. Adams, Darrell D.E. Long, Ethan L. Miller, Shankar Pasupathy, and Mark W. Storer.

We detail the user knowledge and system knowledge needed to construct a comprehensive cost model for analyzing the trade-off between storing a result and regenerating a result.

Traditionally, computing has meant calculating results and then storing those results for later use. Unfortunately, committing large volumes of rarely used data to storage wastes space and energy, making it a very expensive strategy. Cloud computing, with its readily available and flexibly allocatable computing resources, suggests an alternative: storing the provenance data, and means to recomputing results as needed.

While computation and storage are equivalent, finding the balance between the two that maximizes efficiency is difficult. One of the fundamental challenges of this issue is rooted in the knowledge gap separating the users and the cloud administrators—neither has a completely informed view. Users have a semantic understanding of their data, while administrators have an understanding of the cloud’s underlying structure. We detail the user knowledge and system knowledge needed to construct a comprehensive cost model for analyzing the trade-off between storing a result and regenerating a result, allowing users and administrators to make an informed cost-benefit analysis.

In Proceedings of the Workshop on Hot Topics in Cloud Computing 2009 (HotCloud ’09)

Resources

  • A copy of the paper is attached to this posting.

trading-storage-hotcloud09.pdf