Ashkan Paya

Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem

In this paper, we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.

Energy-Aware Load Balancing Policies for the Cloud Ecosystem

The energy consumption of computer and communication systems does not scale linearly with the workload. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: distribute the workload evenly to the smallest set of servers operating at an optimal energy level, while observing QoS constraints, such as the response time. Our model applies to clustered systems, the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling.

A Cloud Reservation System for Big Data Applications

Emerging Big Data applications increasingly require resources beyond those available from a single server and may be expressed as a complex workflow of many components and dependency relationships—each component potentially requiring its own specific, and perhaps specialized, resources for its execution. Efficiently supporting this type of Big Data application is a challenging resource management problem for existing cloud environments. In response, we propose a two-stage protocol for solving this resource management problem. We exploit spatial locality in the first stage by dynamically forming rack-level coalitions of servers to execute a workflow component. These coalitions only exist for the duration of the execution of their assigned component and are subsequently disbanded, allowing their resources to take part in future coalitions. The second stage creates a package of these coalitions, designed to support all the components in the complete workflow. To minimize the communication and housekeeping overhead needed to form this package of coalitions, the technique of combinatorial auctions is adapted from market-based resource allocation. This technique has a considerably lower overhead for resource aggregation than the traditional hierarchically organized models. We analyze two strategies for coalition formation: the first, history-based uses information from past auctions to pre-form coalitions in anticipation of predicted demand; the second one is a just-in-time that builds coalitions only when support for specific workflow components is requested.

Is Cloud Self-organization Feasible?

In this paper we discuss why cloud self-organization is not only desirable, but also critical for the future of cloud computing. We analyze major challenges and discuss practical principles for cloud self-organization. After a brief presentation of a hierarchical cloud architecture model we outline the advantages of a self-organization model based on coalition formation and combinatorial auctions.

A Cloud Service for Adaptive Digital Music Streaming

In this paper we present an adaptive digital music streaming cloud service based on the Amazon Web Services; the players are applications running on mobile devices connected to the Internet via cellular or via wireless networks. Adaptive streaming means that the data rate is determined dynamically function of the available network bandwidth, as well as power reserves of the mobile device. The service applies lossy compression to high quality audio files stored on the cloud to lower the data rate at a level determined by the resources available. We analyze the results of experiments with real-time data conversion for adaptation to the bandwidth available to mobile devices such as smart phones and tablets.

An approach for scaling cloud resource management

Given its current development trajectory, the complexity of cloud computing ecosystems are evolving to where traditional resource management strategies will struggle to remain fit for purpose. These strategies have to cope with ever-increasing numbers of heterogeneous resources, a proliferation of new services, and a growing user-base with diverse and specialized requirements. This growth not only significantly increases the number of parameters needed to make good decisions, it increases the time needed to take these decisions. Consequently, traditional resource management systems are increasingly prone to poor decisions making. Devolving resources management decisions to the local environment of that resource can dramatically increase the speed of decisions making; moreover, the cost of gathering global information can thus be eliminated; saving communication costs. Experimental data, provided in this paper, illustrate that extant cloud deployments can be used as effective vehicles for devolved decision making. This finding strengthens the case for the proposed paradigm shift, since it does not require a change to the architecture of existing cloud systems. This shift would result in systems in which resources decide for themselves how best they can be used. This paper takes this idea to its logical conclusion and proposes a system for supporting self-managing resources in cloud environments. It introduces the concept of coalitions, consisting of collaborating resources, formed for the purpose of service delivery. It suggests the utility of restricting the interactions between the end-user and the cloud service provider to a well-defined services interface. It shows how clouds can be considered functionally, as engines for delivering an appropriate set of resources in response to service requests. And finally, since modern applications are increasingly constructed from sophisticated workflows of complex components, it shows how combinatorial auctions can be used to effectively deliver packages of resources to support those workflows.

Bid-Centric Cloud Service Provisioning

Bid-centric service descriptions have the potential to offer a new cloud service provisioning model that promotes portability, diversity of choice and differentiation between providers. A bid matching model based on requirements and capabilities is presented that provides the basis for such an approach. In order to facilitate the bidding process, tenders should be specified as abstractly as possible so that the solution space is not needlessly restricted. To this end, we describe how partial TOSCA service descriptions allow for a range of diverse solutions to be proposed by multiple providers in response to tenders. Rather than adopting a lowest common denominator approach, true portability should allow for the relative strengths and differentiating features of cloud service providers to be applied to bids. With this in mind, we describe how service descriptions can be augmented with additional information that allows the bid matching algorithm to make use of heterogeneous processing resources, such as GPUs and MICs.

Cloud-Based Simulation of a Smart Power Grid

Is it feasible to automatically generate a cloud environment for applications based on a dynamic computational model when the actual work flow changes in time? We discuss the answer to this question in the context of a complex application, the simulation of a smart grid. We argue that the IaaS cloud delivery model offers enough flexibility and that the Amazon Web Services have evolved to the point when automatic generation of a computing environment is not only feasible, but also leads to an efficient computing infrastructure. In this paper we develop a a model of a smart power grid and then investigate the means to reduce the time needed for the automatic generation of the simulation environment and to reduce the overall cost of the simulation.