Comparative Study of Cloud Computing Delivery Models

Cloud computing is a type of computing that depends on shared computing resources instead of storing local servers. There are three cloud delivery models Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS) which represent exact, prepackaged mixture of IT resources accessible through a cloud provider.

Are Timing-Based Side-Channel Attacks Feasible in Shared, Modern Computing Hardware?

This article describes how there exist various vulnerabilities in computing hardware that adversaries can exploit to mount attacks against the users of such hardware. Microarchitectural attacks, the result of these vulnerabilities, take advantage of microarchitectural performance of processor implementations, revealing hidden computing process. Leveraging microarchitectural resources, adversaries can potentially launch timing-based side-channel attacks in order to leak information via timing. In view of these security threats against computing hardware, the authors analyse current attacks that take advantage of microarchitectural elements in shared computing hardware. This analysis focuses only on timing-based side-channel attacks against the components of modern PC platforms - with references being made also to other platforms when relevant - as opposed to any other variations of side-channel attacks which have a broad application range. To this end, the authors analyse timing attacks performed against processor and cache components, again with references to other components when appropriate.

Job Management and Task Bundling

High Performance Computing is often performed on scarce and shared computing resources. To ensure computers are used to their full capacity, administrators often incentivize large workloads that are not possible on smaller systems. Measurements in Lattice QCD frequently do not scale to machine-size workloads. By bundling tasks together we can create large jobs suitable for gigantic partitions. We discuss METAQ and mpi_jm, software developed to dynamically group computational tasks together, that can intelligently backfill to consume idle time without substantial changes to users’ current workflows or executables.