Decision Model for Cloud Computing under SLA Constraints

International audienceWith the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. A critical challenge is to determine bid prices that minimize monetary costs for a user while meeting Service Level Agreement (SLA) constraints (for example, sufficient resource availability to complete a computation within a desired deadline). We propose a probabilistic model for the optimization of monetary costs, performance, and reliability, given user and application requirements and dynamic conditions. Using real instance price traces and workload models, we evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence

Decision Model for Cloud Computing under SLA Constraints

With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. A critical challenge is to determine bid prices that minimize monetary costs for a user while meeting Service Level Agreement (SLA) constraints (for example, sufficient re- source availability to complete a computation within a desired deadline). We propose a probabilistic model for the optimization of monetary costs, performance, and reliability, given user and application requirements and dynamic conditions. Using real instance price traces and workload models, we evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence

A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency

Molecular Architecture and Functional Model of the Complete Yeast ESCRT-I Heterotetramer

SummaryThe endosomal sorting complex required for transport-I (ESCRT-I) complex, which is conserved from yeast to humans, directs the lysosomal degradation of ubiquitinated transmembrane proteins and the budding of the HIV virus. Yeast ESCRT-I contains four subunits, Vps23, Vps28, Vps37, and Mvb12. The crystal structure of the heterotetrameric ESCRT-I complex reveals a highly asymmetric complex of 1:1:1:1 subunit stoichiometry. The core complex is nearly 18 nm long and consists of a headpiece attached to a 13 nm stalk. The stalk is important for cargo sorting by ESCRT-I and is proposed to serve as a spacer regulating the correct disposition of cargo and other ESCRT components. Hydrodynamic constraints and crystallographic structures were used to generate a model of intact ESCRT-I in solution. The results show how ESCRT-I uses a combination of a rigid stalk and flexible tethers to interact with lipids, cargo, and other ESCRT complexes over a span of ∼25 nm

Monetary Cost-Aware Checkpointing and Migration on Amazon Cloud Spot Instances

International audienceRecently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer low resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability tradeoffs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how mechanisms, namely, checkpointing and migration, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. We evaluate schemes that apply predictive methods for spot prices. Furthermore, we also study how work migration can improve task completion in the midst of failures while maintaining low monetary costs. Trace-based simulations show that our schemes can reduce significantly both monetary costs and task completion times of computation on spot instance

Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud

Abstract—Recently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer lower resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how one such a mechanism, namely checkpointing, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. Trace-based simulations show that our approach can reduce significantly both price and the task completion times. I

Effects of Infrared Energy on Dual Elliptical NDIR Ethanol Gas Sensors

This paper presents the effects of infrared energies on dual elliptical nondispersive infrared (NDIR) ethanol gas sensors for preventing drunken drivers and the advantages of using dual ethanol detectors for temperature compensation algorithm. In order to achieve long-term reliability according to the aging of infrared source, two ethanol detectors are placed at the two foci of ellipsoids and the averaged voltage ratios of two ethanol detectors are used to establish the compensation methods

Decision Model for Cloud Computing under SLA Constraints

With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. A critical challenge is to determine bid prices that minimize monetary costs for a user while meeting Service Level Agreement (SLA) constraints (for example, sufficient re- source availability to complete a computation within a desired deadline). We propose a probabilistic model for the optimization of monetary costs, performance, and reliability, given user and application requirements and dynamic conditions. Using real instance price traces and workload models, we evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence

Toward Real-time, Many-Task Applications on Large Distributed Systems ⋆

Abstract. In the age of Grid, Cloud, volunteer computing, massively parallel applications are deployed over tens or hundreds of thousands of resources over short periods of times to complete immense computations. In this work, we consider the problem of deploying such applications with stringent real-time requirements. One major challenge is the server-side management of these tasks, which often number in tens or hundreds of thousands on a centralized server. In this work, we design and implement a real-time task management system for many-task computing, called RT-BOINC. The system gives low O(1) worst-case execution time for task management operations, such as task scheduling, state transitioning, and validation. We implement this system on top of BOINC, a common middleware for volunteer computing. Using micro and macro-benchmarks executed in emulation experiments, we show that RT-BOINC provides significantly lower worst-case execution time, and lessens the gap between the average and the worst-case performance compared with the original BOINC implementation.