Statistic Rate Monotonic Scheduling

In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods.National Science Foundation (CCR-970668

The Runtime Behavior of Composite SOAP Web Services under Transient Loads

Services are computational elements that expose functionality in a platform independent manner. They are the basic building blocks of the service-oriented (SO) design/integration paradigm. Composite Web Services (CWS) aggregate multiple Web Services (WSs), which is typically achieved by use of a workflow language. A workflow coordinates services in a manner that is consistent with the desired overall functionality (e.g. business process). When the atomic and composite services are exposed to various users, the performance and runtime behavior of WSs becomes important. To ensure wide deployment of CWS, the performance issues must be studied. This research focuses on the performance of atomic and composite SOAP (Simple Object Access Protocol) WSs under transient overloads. This research includes conducting experiments with WSs, studying the runtime behavior, and building simulation models of WSs workflow patterns. Simulation models of different WSs workflow patterns are built to study different situations. Timeout and network latency are added to the model to better simulate real systems. The simulation models are used to predict the runtime behavior of WSs and CWS, as well as to improve the performance with existing, limited resources

Cost-Efficient NFV-Enabled Mobile Edge-Cloud for Low Latency Mobile Applications

Mobile edge-cloud (MEC) aims to support low la- tency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location micro- clouds, leading to resource wastage during stable/low work- load periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging Network Function Virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFV- enabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a near- optimal MEC operational cost over time. We show through ex- tensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications’ low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions

Handling Overload Conditions in Real-Time Systems

This chapter deals with the problem of handling overload conditions, that is, those critical situations in which the computational demand requested by the application exceeds the processor capacity. If not properly handled, an overload can cause an abrupt performance degradation, or even a system crash. Therefore, a real-time system should be designed to anticipate and tolerate unexpected overload situations through specific kernel mechanisms

Go-With-The-Winner: Client-Side Server Selection for Content Delivery

Content delivery networks deliver much of the web and video content in the world by deploying a large distributed network of servers. We model and analyze a simple paradigm for client-side server selection that is commonly used in practice where each user independently measures the performance of a set of candidate servers and selects the one that performs the best. For web (resp., video) delivery, we propose and analyze a simple algorithm where each user randomly chooses two or more candidate servers and selects the server that provided the best hit rate (resp., bit rate). We prove that the algorithm converges quickly to an optimal state where all users receive the best hit rate (resp., bit rate), with high probability. We also show that if each user chose just one random server instead of two, some users receive a hit rate (resp., bit rate) that tends to zero. We simulate our algorithm and evaluate its performance with varying choices of parameters, system load, and content popularity.Comment: 15 pages, 9 figures, published in IFIP Networking 201

Design and Implementation of Distributed Resource Management for Time Sensitive Applications

In this paper, we address distributed convergence to fair allocations of CPU resources for time-sensitive applications. We propose a novel resource management framework where a centralized objective for fair allocations is decomposed into a pair of performance-driven recursive processes for updating: (a) the allocation of computing bandwidth to the applications (resource adaptation), executed by the resource manager, and (b) the service level of each application (service-level adaptation), executed by each application independently. We provide conditions under which the distributed recursive scheme exhibits convergence to solutions of the centralized objective (i.e., fair allocations). Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. We finally validate our framework with simulations using the TrueTime toolbox in MATLAB/Simulink

Nice Resource Reservations in Linux

Computing systems are becoming more and more complex and powerful every year. It is nowadays not uncommon to run several server applications on the same physical platform. This gives rise to a need for resource reservation techniques, so that administrators may prioritize some applications, or customers, over others. This article gives a brief introduction to the Linux kernel 2.6 task scheduler. The article also presents an implementation of a scheduling mechanism, that in a non-intrusive way introduces CPU bandwidth reservations for a task, or a group of tasks, in the GNU/Linux operating system. The scheduling mechanism is first tested using ded icated load tasks, and then on a setup consisting of two Apache servers

Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award