Ahmad Al-Shishtawy

ElastMan: elasticity manager for elastic key-value stores in the cloud

The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud Virtual Machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. We have implemented and evaluated ElastMan using the Voldemort key-value store running in an OpenStack Cloud environment. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.

A Design Methodology for Self-Management in Distributed Environments

Autonomic computing is a paradigm that aims at reducing administrative overhead by providing autonomic managers to make applications self-managing. In order to better deal with dynamic environments, for improved performance and scalability, we advocate for distribution of management functions among several cooperative managers that coordinate their activities in order to achieve management objectives. We present a methodology for designing the management part of a distributed self-managing application in a distributed manner. We define design steps, that includes partitioning of management functions and orchestration of multiple autonomic managers. We illustrate the proposed design methodology by applying it to design and development of a distributed storage service as a case study. The storage service prototype has been developed using the distributing component management system Niche. Distribution of autonomic managers allows distributing the management overhead and increased management performance due to concurrency and better locality.

ElastMan: autonomic elasticity manager for cloud-based key-value stores

The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud virtual machines and nonlinearities in Cloud services complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. We have implemented and evaluated ElastMan using the Voldemort key-value store running in a Cloud environment based on OpenStack. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.

The Role of Overlay Services In a Self-Managing Framework for Dynamic Virtual Organizations

We combine and extend recent results in autonomic computing and structured peer-to-peer to build an infrastructure for constructing and managing dynamic virtual organizations. The paper focuses on the middle layer of the proposed infrastructure, in-between the Niche overlay system on the bottom, and an architecturebased management system based on Jade on the top. The middle layer, the overlay services, are responsible for all sensing and actuation carried out by the VO management. We describe in detail the API of the resource and component overlay services both on the management node and the nodes hosting resources. We present a simple use case demonstrating resource discovery, initial deployment, self-configuration as a result of resource availability change, self-healing, self-tuning and self-protection. The advantages of the design are 1) the overlay services are in themselves self-managing, and sensor/actuation services they provide are robust, 2) management can be dealt with declaratively and at a high-level, and 3) the overlay services provide good scalability in dynamic VOs.

Enabling Self-Management Of Component Based Distributed Applications

Deploying and managing distributed applications in dynamic Grid environments requires a high degree of autonomous management. Programming autonomous management in turn requires programming environm ...

Achieving Robust Self-Management for Large-Scale Distributed Applications

Achieving self-management can be challenging, particularly in dynamic environments with resource churn (joins/leaves/failures). Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of robust management elements (RMEs), which are able to heal themselves under continuous churn. Using RMEs allows the developer to separate the issue of dealing with the effect of churn on management from the management logic. This facilitates the development of robust management by making the developer focus on managing the application while relying on the platform to provide the robustness of management. RMEs can be implemented as fault-tolerant long-living services. We present a generic approach and an associated algorithm to achieve fault-tolerant long-living services. Our approach is based on replicating a service using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. The algorithm uses P2P replica placement schemes to place replicas and uses the P2P overlay to monitor them. The replicated state machine is extended to analyze monitoring data in order to decide on when and where to migrate. We describe how to use our approach to achieve robust management elements. We present a simulation-based evaluation of our approach which shows its feasibility.

SpanEdge: Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers

In stream processing, data is streamed as a continuous flow of data items, which are generated from multiple sources and geographical locations. The common approach for stream processing is to transfer raw data streams to a central data center that entails communication over the wide-area network (WAN). However, this approach is inefficient and falls short for two main reasons: (i) the burst in the amount of data generated at the network edge by an increasing number of connected devices, (ii) the emergence of applications with predictable and low latency requirements. In this paper, we propose SpanEdge, a novel approach that unifies stream processing across a geo-distributed infrastructure, including the central and near-the-edge data centers. SpanEdge reduces or eliminates the latency incurred by WAN links by distributing stream processing applications across the central and the near-the-edge data centers. Furthermore, SpanEdge provides a programming environment, which allows programmers to specify parts of their applications that need to be close to the data source. Programmers can develop a stream processing application, regardless of the number of data sources and their geographical distributions. As a proof of concept, we implemented and evaluated a prototype of SpanEdge. Our results show that SpanEdge can optimally deploy the stream processing applications in a geo-distributed infrastructure, which significantly reduces the bandwidth consumption and the response latency.

Distributed Control Loop Patterns for Managing Distributed Applications

In this paper we discuss various control loop patterns for managing distributed applications with multiple control loops. We introduce a high-level framework, called DCMS, for developing, deploying and managing component-based distributed applications in dynamic environments. The control loops, and interactions among them, are illustrated in the context of a distributed self-managing storage service implemented using DCMS to achieve various self-* properties. Different control loops are used for different self-* behaviours, which illustrates one way to divide application management, which makes for both ease of development and for better scalability and robustness when managers are distributed. As the multiple control loops are not completely independent, we demonstrate different patterns to deal with the interaction and potential conflict between multiple managers.

Stream Processing in Community Network Clouds

Community Network Cloud is an emerging distributed cloud infrastructure that is built on top of a community network. The infrastructure consists of a number of geographically distributed compute and storage resources, contributed by community members, that are linked together through the community network. Stream processing is an important enabling technology that, if provided in a Community Network Cloud, would enable a new class of applications, such as social analysis, anomaly detection, and smart home power management. However, modern stream processing engines are designed to be used inside a data center, where servers communicate over a fast and reliable network. In this work, we evaluate the Apache Storm stream processing framework in an emulated Community Network Cloud in order to identify the challenges and bottlenecks that exist in the current implementation. The community network emulation was performed using data collected from the Guifi.net community network, Spain. Our evaluation results show that, with proper configuration of the heartbeats, it is possible to run Apache Storm in a Community Network Cloud. The performance is sensitive to the placement of the Storm components in the network. The deployment of management components on well-connected nodes improves the Storm topology scheduling time, fault tolerance, and recovery time. Our evaluation also indicates that the Storm scheduler and the stream groupings need to be aware of the network topology and location of stream sources in order to optimally place Storm spouts and bolts to improve performance.

Elasticity Controller for Cloud-Based Key-Value Stores

Clouds provide an illusion of an infinite amount of resources and enable elastic services and applications that are capable to scale up and down (grow and shrink by requesting and releasing resources) in response to changes in its environment, workload, and Quality of Service (QoS) requirements. Elasticity allows to achieve required QoS at a minimal cost in a Cloud environment with its pay-as-you-go pricing model. In this paper, we present our experience in designing a feedback elastically controller for a key-value store. The goal of our research is to investigate the feasibility of the control theoretic approach to the automation of elasticity of Cloud-based key-value stores. We describe design steps necessary to build a feedback controller for a real system, namely Voldemort, which we use as a case study in this work. The design steps include defining touchpoints (sensors and actuators), system identification, and controller design. We have designed, developed, and implemented a prototype of the feedback elasticity controller for Voldemort. Our initial evaluation results show the feasibility of using feedback control to automate elasticity of distributed key-value stores.