Hard disk drive performance characteristics

Higher performance in hard disk drives comes from devices which have better performance characteristics. These performance characteristics can be grouped into two categories: access time and data transfer time (or rate). Access time The access time or response time of a rotating drive is a measure of the time it takes before the drive can actually transfer data. The factors that control this time on a rotating drive are mostly related to the mechanical nature of the rotating disks and moving heads. It is composed of a few independently measurable elements that are added together to get a single value when evaluating the performance of a storage device. The access time can vary significantly, so it is typically provided by manufacturers or measured in benchmarks as an average. The key components that are typically added together to obtain the access t

Adding flexibility to multi-tenant networks

Georgios Ioannidis

Cloud computing has been experiencing sharp development over the last years, leading to an increased demand for application migration to the cloud. Cloud providers, in an effort to attract more customers and earn their confidence, offer to tenants the illusion of an isolated network, exposing familiar abstractions. At the same time, creating this illusion poses challenging problems for the providers, as one tenant's traffic may interfere with another's in complicated, unpredictable ways. First, new challenges have arisen in administering access-control rules (ACLs). On the one hand, installing ACLs at the server is incompatible with bare-metal support and introduces unnecessary performance overhead. On the other hand, offloading the most popular ACLs on the limited hardware memory in Top-of-Rack (ToR) switches should not be conducted naïvely, as the existence of wildcard rules presents inter-rule dependencies that must be respected. Second, tenants' demands have evolved beyond requesting hardware resources; for instance, tenants may require bandwidth provisions between their resources or optimized access to a specific cloud service, e.g., a Mail server or a Database. Cloud providers have not adequately adapted to these expanding demands, therefore elevating hardware resources to "first class citizens," as non-hardware constraints are not considered during resource allocation, instead they are applied afterwards. In this thesis we propose two architectures that facilitate cloud providers in managing their shared network resources in a flexible way. First, we demonstrate virtual flow tables, a ToR architecture that handles ACLs using a two-level memory hierarchy. The most popular ACLs are stored in the limited hardware memory, respecting any dependencies between wildcard rules, while the ToR's supervisor engine maintains access to the entire ACL rule-set. Second, we present a two-tiered architecture for scheduling cloud resources, consisting of a resource-agnostic scheduling layer and a resource-specific enforcement layer. Network resources and constraints are taken into consideration during resource scheduling, instead of afterwards, while resource provisioning, as well as general network-management policies, are delegated to the resource-specific tier.

EPFL2018

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution speedup only when disk accesses are significantly slower than in-memory query processing. On the other hand, fast storage offers loading times that approach or even outperform fully in-memory query execution response times, rendering purely frequency-based statistics incapable of capturing impact of a caching decision on query execution. For example, caching the input of a frequent query that spends most of its time processing joins is less beneficial than caching a page for a slightly less frequent but scan-heavy query. As a result, existing caching policies waste valuable memory space to cache input data that offer little-to-no acceleration for analytics. This paper proposes HPCache, a buffer management policy that enables fast analytics on high-bandwidth storage by efficiently using the available in-memory space. HPCache caches data based on their speedup potential instead of relying on frequency-based statistics. We show that, with fast storage, the benefit of in-memory caching varies significantly across queries; therefore, we quantify the efficiency of caching decisions and formulate an optimization problem. We implement HPCache in Proteus and show that i) estimating speedup potential improves memory space utilization, and ii) simple runtime statistics suffice to infer speedup expectations. We show that HPCache achieves up to 12% faster query execution over state-of-the-art caching policies, or 75% less in-memory cache footprint without deteriorating query performance. Overall, HPCache enables efficient use of the in-memory space for input caching in the presence of fast storage, without any requirement for workload predictions.

ACM2022

What You Need to Know About SDN Flow Tables

Dejan Kostic, Maciej Leszek Kuzniar, Peter Peresini

SDN deployments rely on switches that come from various vendors and differ in terms of performance and available features. Understanding these differences and performance characteristics is essential for ensuring successful deployments. In this paper we measure, report, and explain the performance characteristics of flow table updates in three hardware OpenFlow switches. Our results can help controller developers to make their programs efficient. Further, we also highlight differences between the OpenFlow specification and its implementations, that if ignored, pose a serious threat to network security and correctness.