Recently 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.

Many large-scale distributed computing applications demand real-time responses by soft deadlines. To enable such real-time task distribution and execution on the volunteer resources, we previously proposed the design of the real-time volunteer computing platform 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. In this work, we present a full implementation RT-BOINC, adding new features including deadline timer and parameter-based admission control. We evaluate RT-BOINC at large scale using two real-time applications, namely, the games Go and Chess. The results of our case study show that RT-BOINC provides much better performance than the original BOINC in terms of average and worst-case response time, scalability and efficiency.

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 check pointing, 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 check pointing 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.

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 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.

The energy is the most critical resource in wireless sensor networks. Many energy efficient techniques have been studied especially in communication protocol. Among these protocols, PEGASIS proposed by Lindsey is the most superior interms of energy efficiency. In PEGASIS, all sensor nodes aggregate and transmit data along the single chain. However, this single chain causes some problems such as delay, unexpected long transmission and non-directional transmission to the sink. To resolve delay problem, PEGASIS cuts the chain into several chains. In this way, the delay can be decreased but there is new problem, wireless interference. In this paper, we propose a new chain construction algorithm to resolve above problems. The proposed algorithm minimizes the delay without the wireless interferences and maximizes the energy efficiency by removing the non-directional transmission when transmitting packets to the sink node on wireless sensor networks. Our simulation results show that our proposed algorithm can minimize the delay without expense of energy consumption.

In wireless sensor networks each sensor node has very limited resources, and it is very difficult to find and collect them. For this reason, updating or adding programs in sensor nodes must be performed via a communication channel at run-time. Many code update protocols have been developed for sensor networks, ranging from function-level update to full-image replacement. However, they provide only a fixed level of code update protocols. These protocols require manual selection of an appropriate protocol because they do not consider a cost analysis of the update protocols. In addition, they do not consider real-time response while updating codes. In this paper, we present an adaptive multilevel code update protocol (AMCUP) for real-time sensor operating systems. AMCUP enables energy-efficient code update via support for multilevel protocols (i.e., full-image, module-level, function-level, and instruction-level). It adaptively selects a protocol which meets deadline of applications and consumes less energy based on a cost analysis of several protocols. Our simulation and experimental results show that AMCUP can reduce energy consumption and execution time compared with existing single-level code update protocols while meeting deadline of the running applications.