Security, Performance and Energy Trade-offs of Hardware-assisted Memory Protection Mechanisms

The deployment of large-scale distributed systems, e.g., publish-subscribe platforms, that operate over sensitive data using the infrastructure of public cloud providers, is nowadays heavily hindered by the surging lack of trust toward the cloud operators. Although purely software-based solutions exist to protect the confidentiality of data and the processing itself, such as homomorphic encryption schemes, their performance is far from being practical under real-world workloads. The performance trade-offs of two novel hardware-assisted memory protection mechanisms, namely AMD SEV and Intel SGX - currently available on the market to tackle this problem, are described in this practical experience. Specifically, we implement and evaluate a publish/subscribe use-case and evaluate the impact of the memory protection mechanisms and the resulting performance. This paper reports on the experience gained while building this system, in particular when having to cope with the technical limitations imposed by SEV and SGX. Several trade-offs that provide valuable insights in terms of latency, throughput, processing time and energy requirements are exhibited by means of micro- and macro-benchmarks.Comment: European Commission Project: LEGaTO - Low Energy Toolset for Heterogeneous Computing (EC-H2020-780681

The Tradeoffs of Societal Computing

As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing where software engineering research approaches and techniques seem particularly likely to be fruitful. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole

Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of an HPC system holistically

ARTA: An economic middleware to exchange pervasive energy and computing resources

Studies reveal that an integrated system of smart grid and cloud computing ecosystems can better attain the energy efficiency objectives, considering all the aspects. To facilitate the integration, in this paper, we introduce an agent-oriented economic middleware architecture (ARTA) to exchange pervasive energy and computing resources in different layers of the service provisioning platform, from the edge layer of micro-grid and P2P-cloud to the mass production layer of the giant power plants and data centers. ARTA follows a semi-decentralized economic model by operating through partial system view in the edge-layer negotiations and considers system dynamics and uncertainties in the agents decisions.Peer ReviewedPostprint (author's final draft