Holistic Runtime Scheduling for the Distributed Computing Landscape

Internet services have become an indispensable part of our lives, with billions of users on a daily basis. Example use cases include services for real-time communication and collaborative editing of documents. Furthermore, there are many hidden—nonetheless omnipresent—use cases like cashier systems and sensors of industry facilities. Users expect to use Internet services at any time at low cost with the desired service quality despite potential load spikes a service might face. A straightforward strategy to provide services with high availability is to allocate dedicated resources for each service. In turn, this strategy is likely to lead to over-provisioning and increased operating expenses, which contradicts offering services at a low price. A solution to this problem is to leverage resource scheduling to share the underlying resources among many different workloads and services. Sharing the underlying resources is a key enabler to offer highly scalable services while keeping operating expenses of each service low. A wide range of resource scheduling systems for the distributed computing landscape has been proposed in the past, covering the application and infrastructure levels. Application-level scheduling focuses on problems such as given a set of resources, configuring an application to reach high throughput and good service quality. Many application-level resource scheduling systems lack support for runtime scheduling, often due to slow or unsuitable algorithms. Without runtime scheduling, resource scheduling must run in advance for many scenarios and, at best, repeats periodically to update scheduling decisions. This is likely to result in inefficient resource usage. In contrast to application-level scheduling, infrastructure-level scheduling is about orchestrating resources and serving resource requests of various applications, aiming at high resource utilization. Infrastructure-level scheduling leverages generic resource abstractions, e.g., containers and virtual machines, to fulfill these properties. These abstractions make assumptions (e.g., homogeneity, linear resource consumption) to simplify management, but ignore the fact that current distributed computing systems have been evolving in the post-Moore’s law era and many of these assumptions need to be revised. In particular, the recent trend of new programmable networking devices, ushering in a new area of in-network computing (INC), overtaxes the generic abstractions of compute containers running on servers. The ever-growing demand for Internet services in general and increasingly heterogeneous resources combined with the highly varying demand in particular, require runtime solutions for holistic resource scheduling, covering both the application and infrastructure levels. This dissertation presents four novel solutions to holistic runtime scheduling for the distributed computing landscape. Two solutions cover the application and two the infrastructure levels. We start with an analysis of the field of resource scheduling for the distributed computing landscape and classify involved systems, resources, and abstractions. Based on this, we present a classification of INC which helps to understand the design space of INC resource scheduling. Next, we discuss two scenarios at the application level and demonstrate how runtime scheduling improves resource efficiency. As a first scenario, we consider big data aggregation systems and present ROME, a middleware system to reduce the total aggregation time. ROME automatically analyzes at runtime the involved aggregation function’s data stream and optimizes each node’s responsibilities in the aggregation plan. ROME reduces total aggregation time even compared with manually fine-tuned systems. The second scenario discusses resource scheduling of distributed service function chains. We present STEAM, the first distributed runtime scheduler for this problem, that operates at packet-level granularity without requiring a priori information of traffic estimates and a global view of the systems. Compared with non-runtime solutions to this problem, STEAM achieves better service quality when using the same resources and reduces the amount of resources required to serve the same load. For the data center infrastructure level, we present two mutually exclusive solutions. Our first solution is IncSched, a system that retrofits existing data center resource schedulers for INC. Based on the proposed classification of INC, IncSched presents a new resource model, translates resource requests to be compliant with the plugged retrofitted scheduler, and holds the logic for managing INC resources. IncSched makes existing resource schedulers compatible with INC for the first time, contributing to a broad acceptance of INC. For a holistic integration of INC in data center resource scheduling, we propose HIRE, a full-fledged resource scheduling solution for INC. HIRE extends the resource model of IncSched for automatic augmentation of resource alternatives and incorporates non-linearity property of INC resource usage. HIRE is the first scheduler that combines all server and INC resources in the same scheduling problem to attribute interdependencies on data center level. These novelties make HIRE more successful in satisfying resource requests with INC, finding better placements concerning locality, and reducing tail latencies. We evaluate all solutions using extensive simulations, and for some also using system prototypes and integrated benchmarks. In summary, this dissertation proposes four novel solutions for holistic runtime resource scheduling. The contributions foster the importance of runtime resource scheduling for more efficient resource usage. Our contributions to holistic resource scheduling make shared INC available on a data center level for the first time

Letting off STEAM: Distributed Runtime Traffic Scheduling for Service Function Chaining

Network function virtualization has introduced a high degree of flexibility for orchestrating service functions. The provisioning of chains of service functions requires making decisions on both (1) placement of service functions and (2) scheduling of traffic through them. The placement problem (1) can be tackled during the planning phase, by exploiting coarse-grained traffic information, and has been studied extensively. However, runtime traffic scheduling (2) for optimizing system utilization and service quality, as required for future edge cloud and mobile carrier scenarios, has not been addressed so far.We fill this gap by presenting a queuing-based system model to characterize the runtime traffic scheduling problem for service function chaining. We propose a throughput-optimal scheduling policy, called integer allocation maximum pressure policy (IA-MPP). To ensure practicality in large distributed settings, we propose multi-site cooperative IA-MPP (STEAM), fulfilling runtime requirements while achieving near-optimal performance. We examine our policies in various settings representing real-world scenarios. STEAM closely matches IA-MPP in terms of throughput, and significantly outperforms (possible adaptations of) existing static or coarse-grained dynamic solutions, requiring 30%-60% less server capacity for similar service quality. Our STEAM prototype shows feasibility running on a standard server

Switches for HIRE: Resource Scheduling for Data Center In-Network Computing

The recent trend towards more programmable switching hardware in data centers opens up new possibilities for distributed applications to leverage in-network computing (INC). Literature so far has largely focused on individual application scenarios of INC, leaving aside the problem of coordinating usage of potentially scarce and heterogeneous switch resources among multiple INC scenarios, applications, and users. The traditional model of resource pools of isolated compute containers does not fit an INC-enabled data center. This paper describes HIRE, a Holistic INC-aware Resource managEr which allows for server-local and INC resources to be coordinated in a unified manner. HIRE introduces a novel flexible resource (meta-)model to address heterogeneity, resource interchangeability, and non-linear resource requirements, and integrates dependencies between resources and locations in a unified cost model, cast as a min-cost max-flow problem. In absence of prior work, we compare HIRE against variants of state-of-the-art schedulers retrofitted to handle INC requests. Experiments with a workload trace of a 4000 machine cluster show that HIRE makes better use of INC resources by serving 8-30% more INC requests, while at the same time reducing network detours by 20%, and reducing tail placement latency by 50%

Holistic Resource Scheduling for Data Center In-Network Computing

The recent trend towards more programmable switching hardware in data centers opens up new possibilities for distributed applications to leverage in-network computing (INC). Literature so far has largely focused on individual application scenarios of INC, leaving aside the problem of coordinating usage of potentially scarce and heterogeneous switch resources among multiple INC scenarios, applications, and users. Alas, the traditional model of resource pools of isolated compute containers does not fit an INC-enabled data center. This paper describes HIRE, a holistic INC-aware resource manager which allows for server-local and INC resources to be coordinated in unison. HIRE introduces a novel flexible resource (meta-)model to address heterogeneity and resource interchangeability, and includes two approaches for INC scheduling: (a) retrofitting existing schedulers; (b) designing a new one. For (a), HIRE presents a retrofitting API and demonstrates it with four state-of-the-art schedulers. For (b), HIRE proposes a flow-based scheduler, cast as a min-cost max-flow problem, where a unified cost model is used to integrate the different costs. Experiments with a workload trace of a 4000 machine cluster show that HIRE makes better use of INC resources by serving 8-30% more INC requests, while simultaneously reducing network detours by 20% and reducing tail placement latency by 50%

N-Benzylbenzamides: A Novel Merged Scaffold for Orally Available Dual Soluble Epoxide Hydrolase/Peroxisome Proliferator-Activated Receptor γ Modulators

Metabolic syndrome (MetS) is a multifactorial disease cluster that consists of dyslipidemia, cardiovascular disease, type 2 diabetes mellitus, and obesity. MetS patients are strongly exposed to polypharmacy; however, the number of pharmacological compounds required for MetS treatment can be reduced by the application of multitarget compounds. This study describes the design of dual-target ligands that target soluble epoxide hydrolase (sEH) and the peroxisome proliferator-activated receptor type γ (PPARγ). Simultaneous modulation of sEH and PPARγ can improve diabetic conditions and hypertension at once. N-Benzylbenzamide derivatives were determined to fit a merged sEH/PPARγ pharmacophore, and structure-activity relationship studies were performed on both targets, resulting in a submicromolar (sEH IC50 = 0.3 μM/PPARγ EC50 = 0.3 μM) modulator 14c. In vitro and in vivo evaluations revealed good ADME properties qualifying 14c as a pharmacological tool compound for long-term animal models of MetS