MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

EIPSIM: Modeling Secure IP Address Allocation at Cloud Scale

Public clouds provide impressive capability through resource sharing. However, recent works have shown that the reuse of IP addresses can allow adversaries to exploit the latent configurations left by previous tenants. In this work, we perform a comprehensive analysis of the effect of cloud IP address allocation on exploitation of latent configuration. We first develop a statistical model of cloud tenant behavior and latent configuration based on literature and deployed systems. Through these, we analyze IP allocation policies under existing and novel threat models. Our resulting framework, EIPSim, simulates our models in representative public cloud scenarios, evaluating adversarial objectives against pool policies. In response to our stronger proposed threat model, we also propose IP scan segmentation, an IP allocation policy that protects the IP pool against adversarial scanning even when an adversary is not limited by number of cloud tenants. Our evaluation shows that IP scan segmentation reduces latent configuration exploitability by 97.1% compared to policies proposed in literature and 99.8% compared to those currently deployed by cloud providers. Finally, we evaluate our statistical assumptions by analyzing real allocation and configuration data, showing that results generalize to deployed cloud workloads. In this way, we show that principled analysis of cloud IP address allocation can lead to substantial security gains for tenants and their users

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Network performance isolation for latency-sensitive cloud applications

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Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Empirical Evaluation of Cloud IAAS Platforms using System-level Benchmarks

Cloud Computing is an emerging paradigm in the field of computing where scalable IT enabled capabilities are delivered ‘as-a-service’ using Internet technology. The Cloud industry adopted three basic types of computing service models based on software level abstraction: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Infrastructure-as-a-Service allows customers to outsource fundamental computing resources such as servers, networking, storage, as well as services where the provider owns and manages the entire infrastructure. This allows customers to only pay for the resources they consume. In a fast-growing IaaS market with multiple cloud platforms offering IaaS services, the user\u27s decision on the selection of the best IaaS platform is quite challenging. Therefore, it is very important for organizations to evaluate and compare the performance of different IaaS cloud platforms in order to minimize cost and maximize performance. Using a vendor-neutral approach, this research focused on four of the top IaaS cloud platforms- Amazon EC2, Microsoft Azure, Google Compute Engine, and Rackspace cloud services. This research compared the performance of IaaS cloud platforms using system-level parameters including server, file I/O, and network. System-level benchmarking provides an objective comparison of the IaaS cloud platforms from performance perspective. Unixbench, Dbench, and Iperf are the system-level benchmarks chosen to test the performance of the server, file I/O, and network respectively. In order to capture the performance variability, the benchmark tests were performed at different time periods on weekdays and weekends. Each IaaS platform\u27s performance was also tested using various parameters. The benchmark tests conducted on different virtual machine (VM) configurations should help cloud users select the best IaaS platform for their needs. Also, based on their applications\u27 requirements, cloud users should get a clearer picture of which VM configuration they should choose. In addition to the performance evaluation, the price-per-performance value of all the IaaS cloud platforms was also examined

System Support for Managing Risk in Cloud Computing Platforms

Cloud platforms sell computing to applications for a price. However, by precisely defining and controlling the service-level characteristics of cloud servers, they expose applications to a number of implicit risks throughout the application’s lifecycle. For example, user’s request for a server may be denied, leading to rejection risk; an allocated resource may be withdrawn, resulting in revocation risk; an acquired cloud server’s price may rise relative to others, causing price risk; a cloud server’s performance may vary due to external factors, triggering valuation risk. Though these risks are implicit, the costs they bear on the applications are not. While some risks exist in all Infrastructure-as-a-Service offerings, they are most pronounced in an emerging category called transient cloud servers. Since transient servers are carved out of instantaneous idle cloud capacity, they exhibit two distinct features: (i) revocations that are intentional, frequent and come with advanced warning, and (ii) prices that are low in average but vary across time and location. Thus, despite enabling inexpensive access to at-scale computing, transient cloud servers expose applications to risks, the scale of which were unseen in the past platforms. Unfortunately, the current generation system software are not designed to handle these risks, which in turn results in inconsistent performances, unexpected failures, missed savings, and slower adoption. In this dissertation, we elevate risk management to a first-class system design principle. Our goal is to identify the risks, quantify their costs, and explicitly manage them for applications deployed on cloud platforms. Towards that goal, we adapt and extend concepts from finance and economics to propose a new system design approach called financializing cloud computing. By treating cloud resources as investments, and by quantifying the cost of their risks, financialization enables system software to manage the risk-reward trade-offs, explicitly and autonomously. We demonstrate the utility of our approach via four contributions: (i) mitigating revocation risk with insurance policy, (ii) reducing price risk through active trading, (iii) eliminating uncertainty risk by index tracking, and (iv) minimizing server’s valuation risk via asset pricing. We conclude by observing that diversity and asymmetry in the creation and consumption of cloud compute resources is on the rise, and that financialization can be effectively employed to manage its complexity and risks