Timely-Throughput Optimal Coded Computing over Cloud Networks

In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. With timely computation requests submitted to the system with computation deadlines, our goal is to design the optimal computation-load allocation scheme and the optimal data encoding scheme that maximize the timely computation throughput (i.e, the average number of computation tasks that are accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Lagrange Estimate-and Allocate (LEA) strategy, which achieves the optimal timely computation throughput. It is shown that compared to the static allocation strategy, LEA increases the timely computation throughput by 1.4X - 17.5X in various scenarios via simulations and by 1.27X - 6.5X in experiments over Amazon EC2 clustersComment: to appear in MobiHoc 201

Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra

Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries

Value-based scheduling in real-time systems

A real-time system must execute functionally correct computations in a timely manner. Most of the current real-time systems are static in nature. However in recent years, the growing need for building complex real-time applications coupled with advancements in information technology drives the need for dynamic real-time systems. Dynamic real-time systems need to be designed not only to deal with expected load scenarios, but also to handle overloads by allowing graceful degradation in system performance. Value-based scheduling is a means by which graceful degradation can be achieved by executing critical tasks that offer high values/benefits/rewards to the functioning of the system. This thesis identifies the following two issues in dynamic real-time scheduling: (i) maintaining high system reliability without affecting its schedulability and (ii) providing graceful degradation to the system during overload and maintaining high schedulability during underloads or near full loads. Further, we use value-based scheduling techniques to address these issues. The first contribution of this thesis is a reliability-aware value-based scheduler capable of maintaining high system reliability and schedulability. We use a performance index (PI) based value function for scheduling, which can capture the tradeoff between schedulability and reliability. The proposed scheduler selects a suitable redundancy level for each task so as to increase the performance index of the system. We show through our simulation studies that proposed scheduler maintains a high system value (PI). The second contribution of this thesis is an adaptive value-based scheduler that can change its scheduling behavior from deadline-based scheduling to value-based scheduling based on the system workload, so that it can maintain a high system value with fewer deadline misses. Further, the scheduler is extended to heterogeneous computing (HC) systems, wherein the computing capabilities of processors/machines are different, and propose two adaptive schedulers (Basic and Integrated) for HC systems. The performance of the proposed scheduling algorithms is studied through extensive simulation studies for both homogeneous and heterogeneous computing systems. We have concluded that the proposed adaptive scheduling scheme maintains a high system value with fewer deadlines misses for all range workloads. Amongst the schedulers for HC systems, we conclude that the Basic scheduler, which has a lesser run-time complexity, performs better for most of the workloads. The last contribution of this thesis is the design and implementation of the proposed adaptive value-based scheduler for homogeneous computing systems in a real-time Linux operating system, RT-Linux. We compare the performance of the implementation with EDF and Highest Value-Density First (HVDF) schedulers for various ranges of workloads and show that the proposed scheduler performs better in maintaining a high system value with fewer deadline misses

Cross-layer scheduling and resource allocation for heterogeneous traffic in 3G LTE

3G long term evolution (LTE) introduces stringent needs in order to provide different kinds of traffic with Quality of Service (QoS) characteristics. The major problem with this nature of LTE is that it does not have any paradigm scheduling algorithm that will ideally control the assignment of resources which in turn will improve the user satisfaction. This has become an open subject and different scheduling algorithms have been proposed which are quite challenging and complex. To address this issue, in this paper, we investigate how our proposed algorithm improves the user satisfaction for heterogeneous traffic, that is, best-effort traffic such as file transfer protocol (FTP) and real-time traffic such as voice over internet protocol (VoIP). Our proposed algorithm is formulated using the cross-layer technique. The goal of our proposed algorithm is to maximize the expected total user satisfaction (total-utility) under different constraints. We compared our proposed algorithm with proportional fair (PF), exponential proportional fair (EXP-PF), and U-delay. Using simulations, our proposed algorithm improved the performance of real-time traffic based on throughput, VoIP delay, and VoIP packet loss ratio metrics while PF improved the performance of best-effort traffic based on FTP traffic received, FTP packet loss ratio, and FTP throughput metrics